2014crc postersessionproceedings

Proceedings of

PhD Student Poster

Session 2014 Construction Research Congress Proceedings edited by Mani Golparvar-Fard, Ph.D., and Youngjib Ham

This proceedings is invaluable to all practitioners and researchers in the field of

construction engineering and management.

2014 CRC PhD Student Poster Session

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2014 Construction Research Congress PhD Student Poster Sessions Proceedings edited by Mani Golparvar-Fard, Ph.D., and Youngjib Ham

Chair: Mani Golparvar-Fard, Ph.D., University of Illinois at Urbana-Champaign

PhD Student Poster Session Organizing Committee Member:

Youngjib Ham, University of Illinois at Urbana-Champaign

CRC Executive Committee: SangHyun Lee, Ph.D., University of Michigan – Ann Arbor Amr Kandil, Purdue University Susan Bogus, University of New Mexico

CRC2014 Conference Chair: Daniel Castro-Lacouture, Ph.D., Georgia Tech CRC2014 Technical Committee Co-Chairs: Baabak Ashuri, Ph.D., Georgia Tech Javier Irizarry, Ph.D., Georgia Tech

The Technical Committee of CRC2014 PhD Student Poster Session: Alex Albert, Ph.D., North Carolina State University Amir Behzadan, Ph.D., Central Florida University Caroline Clevenger, Ph.D., Colorado State University Changbum Ahn, Ph.D., University of Nebraska- Lincoln Ken-Yu Liu, Ph.D., University of Washington Mani Golparvar-Fard, Ph.D., University of Illinois at Urbana-Champaign

Ming Liu, Ph.D., University of Alberta Mounir El-Asmar, Ph.D., Arizona State University Pardis Pishdad-Bozorgi, Ph.D., Georgia Tech SangUk Han, Ph.D., University of Alberta Tanyel Bulbul, Ph.D., Virginia Tech Thais Alves, Ph.D., San Diego State University Xinyi Song, Ph.D., Georgia Tech The Members of the Jury– CRC2014 PhD Student Poster Session: Ali Touran, Ph.D., Northeastern University Carrie Sturts Dossick, Ph.D., University of Washington Charles Jahern, Ph.D., Iowa State University Daniel Castro-Lacouture, Ph.D., Georgia Tech Eddy Rojas, Ph.D., University of Nebraska- Lincoln Iris Tommelein, Ph.D., University of California – Berkeley Jesus M. de la Garza, Ph.D., Virginia Tech Miroslaw Skibniewski, University of Maryland – College Park Mohamed Al-Hussein, Ph.D., University of Alberta Simaan Abourizk, Ph.D., University of Alberta

List of Posters

1. RFID AND BIM-ENABLED WORKER LOCATION TRACKING TO SUPPORT REAL-TIME BUILDING PROTOCOL CONTROL AND DATA VISUALIZATION ON A LARGE HOSPITAL PROJECT ............................. 9 Aaron M. Costin ([email protected]), Advisor: Dr. Jochen Teizer Georgia Institute of Technology

2. DEVELOPING CONTEXT SPECIFIC AND GENERALIZED CONSTRUCTION LABOUR PRODUCTIVITY MODELS ............................................................................................................................................................. 10 Abraham Assefa Tsehayae (tsehayae@ualberta .ca), Advisor: Dr. Aminah Robinson Fayek University of Alberta

3. AUTOMATED ASSESSMENT OF TORNADO-INDUCED BUILDING DAMAGE BASED ON LASER SCANNING ......................................................................................................................................................... 11 Alireza G. Kashani ([email protected]), Advisor: Dr. Andrew J. Graettinger University of Alabama

4. COST EVALUATION MODEL FOR HOUSING RETROFIT DECISION-MAKING: A CASE STUDY ............. 12 Amirhosein Jafari ([email protected]), Advisor: Dr. Vanessa Valentin University of New Mexico

5. SEGMENTATION AND NURBS FITTING OF UNORDERED BUILDING POINT CLOUDS .......................... 13 Andrey Dimitrov ([email protected]), Advisors: Dr. Feniosky Pena Mora, Dr. Mani Golparvar-Fard Columbia University

6. SAVES II: A MULTIPLE SIGNALS ENHANCED AUGMENTED VIRTUALITY TRAINING SYSTEM FOR CONSTRUCTION HAZARD RECOGNITION ..................................................................................................... 14 Ao Chen ([email protected]), Advisors: Dr. Brian Kleiner, Dr. Mani Golparvar-Fard Virginia Tech

7. QUANTIFYING ENERGY-USE BEHAVIOR IN COMMERCIAL BUILDINGS ................................................. 15 Ardalan Khosrowpour ([email protected]), Rimas Gulbinas ([email protected]), Advisor: Dr. John E. Taylor Virginia Tech

8. ADOPTION READINESS OF PREVENTION THROUGH DESIGN (PTD) CONTROLS IN CONCRETE, MASONRY, AND ASPHALT ROOFING ............................................................................................................. 16 Ari Goldberg ([email protected]), Advisor: Dr. Deborah Young-Corbett Virginia Tech

9. A BIO-INSPIRED VIRTUAL PEDAGOGICAL ENVIRONMENT TO STIMULATE BIO-INSPIRED THINKING ............................................................................................................................................................................ 17 Aruna Muthumanickam ([email protected]), Advisor: Dr. John E. Taylor Virginia Tech

10. OPTIMIZING THE SUSTAINABILITY OF SINGLE-FAMILY HOUSING UNITS ........................................... 18 Aslihan Karatas ([email protected]), Advisor: Dr. Khaled El-Rayes University of Illinois at Urbana-Champaign

11. THREE-TIERED DATA & INFORMATION INTEGRATION FRAMEWORK FOR HIGHWAY PROJECT DECISION- MAKINGS ........................................................................................................................................ 19 Asregedew Woldesenbet ([email protected]), Advisor: Dr. “David” Hyung Seok Jeong Iowa State University

12. MINIMIZING EFFECTS OF OVERFITTING AND COLLINEARITY IN CONSTRUCTION COST ESTIMATION: A NEW HYBRID APPROACH ..................................................................................................... 20 Bo Xiong ([email protected]), Advisor: Dr. Martin Skitmore, Dr. Bo Xia Queensland University of Technology


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13. AUTOMATED REAL-TIME TRACKING AND 3D VISUALIZATION OF CONSTRUCTION EQUIPMENT OPERATION USING HYBRID LIDAR SYSTEM ................................................................................................. 21 Chao Wang ([email protected]), Advisor: Dr. Yong K. Cho Georgia Institute of Technology

14. INTERDEPENDENT INFRASTRUCTURE NETWORK SYSTEM VULNERABILITY IDENTIFICATION ..... 22 Christopher Van Arsdale ([email protected]), Advisor: Dr. Amlan Mukherjee Michigan Technological University

15. VOLATILE ORGANIC COMPOUNDS EMISSIONS GENERATED IN HOT-MIX ASPHALT PAVEMENT CONSTRUCTION AND THEIR HEALTH EFFECTS ON PAVEMENT WORKERS ........................................... 23 Dan Chong ([email protected]), Advisor: Dr. Yuhong Wang The Hong Kong Polytechnic University

16. QUANTITATIVE PERFORMANCE ASSESSMENT OF SINGLE-STEP AND TWO-STEP DESIGN-BUILD PROCUREMENT ................................................................................................................................................ 24 David Ramsey ([email protected]), Advisor: Dr. Mounir El Asmar, Dr. G. Edward Gibson Arizona State University

17. RISK ALLOCATION IN PUBLIC-PRIVATE PARTNERSHIPS: ANALYSIS OF CONTRACTUAL PROVISIONS IN 18 U.S. HIGHWAY PROJECTS .............................................................................................. 25 Duc A. Nguyen ([email protected]) and Edwin Gonzalez ([email protected]), Advisor: Dr. Michael J. Garvin Virginia Tech

18. EXTENDING BUILDING INFORMATION MODELING (BIM) INTEROPERABILITY TO GEO-SPATIAL DOMAIN USING SEMANTIC WEB TECHNOLOGY .......................................................................................... 26 Ebrahim P. Karan ([email protected]), Advisor: Javier Irizarry Georgia Institute of Technology

19. QUANTIFYING THE RISKS OF WILDFIRE TO BUILDINGS IN WILDLAND URBAN INTERFACE: A FORWARD VIEW ............................................................................................................................................... 27 Elmira Kalhor ([email protected]), Advisor: Dr. Vanessa Valentin University of New Mexico

20. COLLABORATION THROUGH INNOVATION: A MULTI-LAYERED FRAMEWORK FOR THE AECM INDUSTRY .......................................................................................................................................................... 28 Erik A. Poirier ([email protected]), Advisor: Dr. Daniel Forgues, Dr. Sheryl Staub-French École de Technologie Supérieure

21. THE VIRTUAL CONSTRUCTION SIMULATOR: AN EDUCATIONAL GAME IN CONSTRUCTION ENGINEERING ................................................................................................................................................... 29 Fadi Castronovo ([email protected]), Advisor: Dr. John I. Messner The Pennsylvania State University

22. PREDICTIVE EMISSIONS MODELS FOR EXCAVATORS ......................................................................... 30 Heni Fitriani ([email protected]), Advisor: Dr. Phil Lewis Oklahoma State University

23. ESTIMATING EXTREME EVENT RECOVERY WITH CONSTRUCTION ACTIVITY CHANGE POINTS ... 31 Henry D. Lester ([email protected]), Advisor: Dr. Gary P. Moynihan University of Alabama

24. AN INTEGRATED SIMULATION AND OPTIMIZATION BASED RESIDENTIAL CONSTRUCTION CARBON FOOTPRINT AND EMISSION ASSESSMENT .................................................................................. 32 Hong Xian Li ([email protected]), Advisor: Dr. Mohamed Al-Hussein, Dr. Mustafa Gül University of Alberta


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25. 3D RECONSTRUCTION OF INDUSTRIAL EQUIPMENT USING COMBINED GEOMETRIC AND TOPOLOGICAL INFORMATION FROM LASER-SCANNED DATA .................................................................. 33 Hyojoo Son ([email protected]), Advisor: Dr. Changwan Kim Chung-Ang University

26. INFORMATION EXTRACTION AND AUTOMATED REASONING FOR AUTOMATED REGULATORY COMPLIANCE CHECKING IN THE CONSTRUCTION DOMAIN ...................................................................... 34 Jiansong Zhang ([email protected]), Advisor: Dr. Nora El-Gohary University of Illinois at Urbana-Champaign

27. EX-ANTE ASSESSMENT OF PERFORMANCE IN CONSTRUCTION PROJECTS: A SYSTEM-OF-SYSTEMS APPROACH ...................................................................................................................................... 35 Jin Zhu ([email protected]), Advisor: Dr. Ali Mostafavi Florida International University

28. A FRAMEWORK FOR PUBLIC PRIVATE PARTNERSHIP RISK MITIGATION IN RURAL POST CONFLICT ENVIRONMENTS– A SYSTEMS APPROACH ............................................................................... 36 John T. Mitchell ([email protected]), Advisor: Dr. Yvan Beliveau Virginia Tech

29. FRAMEWORK FOR ON-SITE BIOMECHANICAL ANALYSIS DURING CONSTRUCTION TASKS ........... 37 JoonOh Seo ([email protected]), Advisor: Dr. SangHyun Lee University of Michigan

30. DEVELOP A PRICE ESCALATION METHOD FOR SINGLE AWARD INDEFINITE DELIVERY/INDEFINITE QUANTITY CONTRACTS: AXE BIDDING ......................................................................................................... 38 Jorge A. Rueda ([email protected]), Advisor: Dr. Douglas D. Gransberg Iowa State University

31. CONSTRUCTION OPERATIONS AUTOMATION USING MODIFIED DISCRETE EVENT SIMULATION MODELS ............................................................................................................................................................. 39 Joseph Louis ([email protected]), Advisor: Dr. Phillip S. Dunston Purdue University

32. AUTONOMOUS NEAR-MISS FALL ACCIDENT DETECTION TECHNIQUE USING INERTIAL MEASUREMENT UNITS ON CONSTRUCTION IRON-WORKERS .................................................................. 40 Kanghyeok Yang ([email protected]) and Sepideh S. Aria ([email protected]), Advisor: Dr. Changbum Ahn University of Nebraska at Lincoln

33. MANAGING WATER AND WASTEWATER INFRASTRUCTURE IN SHRINKING CITIES ......................... 41 Kasey Faust ([email protected]), Advisor: Dr. Dulcy Abraham Purdue University

34. MONITORING CONSTRUCTION PROGRESS AT THE OPERATION-LEVEL USING 4D BIM AND SITE PHOTOLOGS ..................................................................................................................................................... 42 Kevin K. Han ([email protected]), Advisor: Dr. Mani Golparvar-Fard University of Illinois at Urbana-Champaign

35. ESTIMATING OPTIMAL LABOR PRODUCTIVITY: A TWO-PRONG STRATEGY ...................................... 43 Krishna Kisi ([email protected]) and Nirajan Mani ([email protected]), Advisor: Dr. Eddy Rojas University of Nebraska-Lincoln

36. AN INVESTIGATION OF OCCUPANT ENERGY USE BEHAVIOR AND INTERVENTIONS IN A RESIDENTIAL CONTEXT .................................................................................................................................. 44 Kyle Anderson ([email protected]), Advisor: Dr. SangHyun Lee


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University of Michigan

37. MEASURING THE COMPLEXITY OF MEGA CONSTRUCTION PROJECTS IN CHINA—A FUZZY ANALYTIC NETWORK PROCESS ..................................................................................................................... 45 Lan Luo, Advisor: Dr. Qinghua He Tongji University

38. DECISION SUPPORT SYSTEM FOR SUSTAINABLE LABOR MANAGEMENT IN MASONRY CONSTRUCTION ............................................................................................................................................... 46 Laura Florez ([email protected]), Advisor: Dr. Daniel Castro-Lacouture Georgia Institute of Technology

39. BIM-BASED INTEGRATED APPROACH FOR OPTIMIZED CONSTRUCTION SCHEDULING UNDER RESOURCE CONSTRAINTS ............................................................................................................................. 47 Hexu Liu ([email protected]), Advisor: Dr. Mohamed Al-Hussein, Dr. Ming Lu University of Alberta

40. INCREASING MINDFULNESS OF COORDINATION PRACTICES IN INNER CITY UTILITY PROJECTS: THE ROLE OF NEW (BIM) TECHNOLOGIES ................................................................................................... 48 Léon L. olde Scholtenhuis ([email protected]), Advisor: Dr. T. Hartmann University of Twente

41. A SYSTEMATIC RISK ANALYSIS APPROACH AGAINST TUNNEL-INDUCED BUILDING DAMAGES .... 49 Limao Zhang ([email protected]), Advisor: Dr. Xianguo Wu Huazhong University of Science and Technology

42. MODELING AND VISUALIZING THE FLOW OF TRADE CREWS IN CONSTRUCTION USING AGENTS AND BUILDING INFORMATION MODELS (BIM) .............................................................................................. 50 Lola Ben-Alon ([email protected]), Advisor: Dr. Rafael Sacks Technion IIT

43. MEASURING INTERDEPENDENT INFRASTRUCTURE RESILIENCE UNDER NORMAL AND EXTREME CONDITIONS ...................................................................................................................................................... 51 María E. Nieves-Meléndez ([email protected]), Advisor: Dr. Jesús M. de la Garza Virginia Tech

44. THERMALLY ACTIVATED CLAY BASED BIOMASS POZZOLANA INVESTIGATIONS FOR SUSTAINABLE CONSTRUCTION IN GHANA ................................................................................................... 52 Mark Bediako ([email protected]), Advisor: SKY Gawu and AA Adjaottor Kwame Nkrumah University of Science and Technology, Ghana

45. ASSESSMENT OF ACTIVITIES’ CRITICALITY TO CASH-FLOW PARAMETERS ..................................... 53 Marwa Hussein Ahmed ([email protected]), Advisor: Dr. Tarek Zayed, Dr. Ashraf Elazouni Concordia University

46. UNDERSTANDING CURRENT HORIZONTAL DIRECTIONAL DRILLING PRACTICES IN MAINLAND CHINA BEING USED FOR ENERGY PIPELINE CONSTRUCTION .................................................................. 54 Maureen Cassin ([email protected]), Advisor: Dr. Samuel Ariaratnam Arizona State University

47. OPTIMIZING THE SELECTION OF SUSTAINABILITY MEASURES FOR EXISTING BUILDINGS ............ 55 Moatassem Abdallah ([email protected]), Advisor: Dr. Khaled El-Rayes University of Illinois at Urbana-Champaign

48. COMPETENCIES AND PERFORMANCE IN CONSTRUCTION PROJECTS ............................................. 56 Moataz Nabil Omar ([email protected]), Advisor: Dr. Aminah Robinson Fayek University of Alberta


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49. IMPROVING CONSTRUCTION COST ESCALATION ESTIMATION USING MACROECONOMIC, ENERGY AND CONSTRUCTION MARKET VARIABLES ................................................................................. 57 Mohsen Shahandashti ([email protected]), Advisor: Dr. Baabak Ashuri Georgia Institute of Technology

50. EX-ANTE SIMULATION AND VISUALIZATION OF SUSTAINABILITY POLICIES IN INFRASTRUCTURE SYSTEMS: A HYBRID METHODOLOGY FOR MODELING AGENCY-USER-ASSET INTERACTIONS .......... 58 Mostafa Batouli ([email protected]), Advisor: Dr. Ali Mostafavi Florida International University

51. DYNAMIC FATIGUE MODEL FOR ASSESSING MUSCLE FATIGUE DURING CONSTRUCTION TASKS ............................................................................................................................................................................ 59 MyungGi Moon ([email protected]) Advisor: Dr. SangHyun Lee University of Michigan

52. TOWARD SUSTAINABLE CAPITAL TRANSPORTATION INFRASTRUCTURE: MAXIMIZING PERFORMANCE OF PREPLANNING PHASE .................................................................................................. 60 Nahid Vesali ([email protected]), Advisor: Dr. Mehmet Emre Bayraktar Florida International University

53. A QUANTITATIVE INVESTIGATION OF BUILDING MICRO-LEVEL POWER MANAGEMENT THROUGH ENERGY HARVESTING FROM OCCUPANT MOBILITY .................................................................................. 61 Neda Mohammadi ([email protected]), Advisor: Dr. Tanyel Bulbul, Dr. John E. Taylor Virginia Tech

54. ESTIMATING LABOR PRODUCTIVITY FRONTIER: A PILOT STUDY ....................................................... 62 Nirajan Mani ([email protected]) and Krishna P. Kisi ([email protected]), Advisor: Dr. Eddy M. Rojas University of Nebraska-Lincoln

55. A DECISION SUPPORT SYSTEM FOR SUSTAINABLE MULTI OBJECTIVE ROADWAY ASSET MANAGEMENT .................................................................................................................................................. 63 Omidreza Shoghli ([email protected]), Advisor: Dr. Jesus M. de la Garza Virginia Tech

56. SIMULEICON: A SIMULATION-BASED MULTI-OBJECTIVE DECISION-SUPPORT TOOL FOR SUSTAINABLE BUILDING DESIGN ................................................................................................................... 64 Peeraya Inyim ([email protected]), Advisor: Dr. Yimin Zhu, Dr. Wallied Orabi Florida International University

57. CONSTRUCTION OPERATIONS PROCESS DATA MODELING AND KNOWLEDGE DISCOVERY USING MACHINE LEARNING CLASSIFIERS ................................................................................................................ 65 Reza Akhavian ([email protected]), Advisor: Dr. Amir H. Behzadan University of Central Florida

58. THE DEVELOPMENT OF AN AUTOMATED PROGRESS MONITORING AND CONTROL SYSTEM FOR CONSTRUCTION PROJECTS ........................................................................................................................... 66 Reza Maalek ([email protected]), Advisor: Dr. Janaka Ruwanpura, Dr. Derek Lichti University of Calgary

59. OPTIMUM RESOURCE UTILIZATION PLANNING IN CONSTRUCTION PORTFOLIOS THROUGH MODELING OF EVERYDAY UNCERTAINTIES AT CERTAIN CONFIDENCE LEVEL ..................................... 67 Reza Sheykhi ([email protected]), Advisor: Dr. Wallied Orabi Florida International University

60. USING STEP APPROACH TO ACHIEVE SUCCESSFUL OUTCOMES ON COMPLEX PROJECTS ........ 68 Ron Patel ([email protected]), Advisor: Dr. Edward J. Jaselskis


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North Carolina State University

61. QUANTIFYING HUMAN MOBILITY PERTURBATION UNDER THE INFLUENCE OF TROPICAL CYCLONES ........................................................................................................................................................ 69 Qi Wang ([email protected]), Advisor: Dr. John E. Taylor Virginia Tech

62. CONSTRUCTION WORKERS’ BEHAVIOR INFLUENCED BY SOCIAL NORMS: A STUDY OF WORKERS’ BEHAVIOR USING AGENT-BASED SIMULATION INTEGRATED WITH EMPIRICAL METHODS ............................................................................................................................................................................ 70 Seungjun Ahn ([email protected]), Advisor: Dr. SangHyun Lee University of Michigan

63. CONSTRUCTION SITE LAYOUT PLANNING USING SIMULATION .......................................................... 71 SeyedReza RazaviAlavi ([email protected]), Advisor: Dr. Simaan AbouRizk University of Alberta

64. 4-DIMENSIONAL PROCESS-AWARE SITE-SPECIFIC CONSTRUCTION SAFETY PLANNING .............. 72 Sooyoung Choe ([email protected]), Advisor: Dr. Fernanda Leite The University of Texas at Austin

65. THE IMPACT OF BUSINESS-PROJECT INTERFACE ON CAPITAL PROJECT PERFORMANCE ........... 73 Sungmin Yun ([email protected]), Advisor: Dr. Stephen P. Mulva, Dr. William J. O’Brien University of Texas at Austin

66. EXPLORING A PREFERENTIAL FRAMEWORK FOR FUTURE PROJECT OPPORTUNITIES ................ 74 Timothy W. Gardiner ([email protected]), Advisor: Dr. Yvan J. Beliveau Virginia Tech

67. ENVISIONING MORE SUSTAINABLE INFRASTRUCTURE THROUGH CHOICE ARCHITECTURE ........ 75 Tripp Shealy ([email protected]), Advisor: Dr. Leidy Klotz Clemson University

68. SEGMENTATION AND RECOGNITION OF ROADWAY ASSETS FROM CAR-MOUNTED CAMERA VIDEO STREAMS USING A SCALABLE NON-PARAMETRIC IMAGE PARSING METHOD ........................... 76 Vahid Balali ([email protected]), Advisor: Dr. Mani Golparvar-Fard University of Illinois at Urbana-Champaign

69. INTEGRATED COMPUTATIONAL MODEL IN SUPPORT OF VALUE ENGINEERING ............................. 77 Yalda Ranjbaran ([email protected]), Advisor: Dr. Osama Moselhi Concordia University

70. IMPROVING CAMPUS BUILDING ENERGY EFFICIENCY AND OCCUPANTS SATISFACTION THROUGH APPLICATION OF ARTIFICIAL INTELLIGENCE INTO CAMPUS FACILITY MANAGEMENT ...... 78 Yang Cao ([email protected]), Advisor: Dr. Xinyi Song Georgia Institute of Technology

71. A BIO-INSPIRED SOLUTION TO MITIGATE URBAN HEAT ISLAND EFFECTS ....................................... 79 Yilong Han ([email protected]), Advisor: Dr. John E. Taylor Virginia Tech

72. FORECASTING LONG-TERM STAFFING REQUIREMENTS FOR STATE TRANSPORTATION AGENCIES .......................................................................................................................................................... 80 Ying Li ([email protected]), Advisor: Dr. Timothy R. B. Taylor University of Kentucky


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73. VISION-BASED BUILDING ENERGY DIAGNOSTICS AND RETROFIT ANALYSIS USING 3D THERMOGRAPHY AND BIM ............................................................................................................................. 81 Youngjib Ham ([email protected]), Advisor: Dr. Mani Golparvar-Fard University of Illinois at Urbana-Champaign

74. MULTI-TIERED SELECTION OF PROJECT DELIVERY SYSTEMS FOR CAPITAL PROJECTS .............. 82 Zorana Popić ([email protected]), Advisor: Dr. Osama Moselhi Concordia University


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List of Participants

Student Name School Advisor Name

1 Aaron Costin Georgia Institute of Technology Jochen Teizer

2 Abraham Tsehayae University of Alberta Aminah Robinson Fayek

3 Alireza Geranmayeh Kashani University of Alabama Andrew J. Graettinger

4 Amirhosein Jafari University of New Mexico Vanessa Valentin

5 Andrey Dimitrov Columbia University Feniosky Pena Mora, Mani Golparvar-Fard

6 Ao Chen Virginia Tech Brian Kleiner, Mani Golparvar-Fard

7 Ardalan Khosrowpour Virginia Tech John E. Taylor

8 Ari Goldberg Virginia Tech Deborah Young-Corbett

9 Aruna Muthumanickam Virginia Tech John E. Taylor

10 Aslihan Karatas University of Illinois at Urbana-Champaign

Khaled El-Rayes

11 Asregedew Woldesenbet Iowa State University “David” Hyung Seok Jeong

12 Bo Peter Xiong Queensland University of Technology

Martin Skitmore, Bo Xia

13 Chao Wang Georgia Institute of Technology Yong K. Cho

14 Christopher Van Arsdale Michigan Technological University

Amlan Mukherjee

15 Dan Chong The Hong Kong Polytechnic University

Yuhong Wang

16 David Ramsey Arizona State University Mounir El Asmar, G. Edward Gibson

17 Duc Nguyen & Edwin Gonzales Virginia Tech Michael J. Garvin

18 Ebrahim Karan Georgia Institute of Technology Javier Irizarry

19 Elmira Kalhor University of New Mexico Vanessa Valentin

20 Erik Poirier École de Technologie Supérieure Daniel Forgues, Sheryl Staub-French

21 Fadi Castronovo The Pennsylvania State University

John I. Messner

22 Heni Fitriani Oklahoma State University Phil Lewis

23 Henry D. Lester University of Alabama Gary P. Moynihan

24 Hong Li University of Alberta Mohamed Al-Hussein, Mustafa Gül

25 Hyojoo Son Chung-Ang University Changwan Kim

26 Jiansong Zhang University of Illinois at Urbana-Champaign

Nora El-Gohary

27 Jin Zhu Florida International University Ali Mostafavi

28 John Mitchell Virginia Tech Yvan Beliveau

29 JoonOh Seo University of Michigan SangHyun Lee

30 Jorge A. Rueda Iowa State University Douglas D. Gransberg

31 Joseph Louis Purdue University Phillip S. Dunston

32 Kanghyeok Yang & Sepidek S. Aria

University of Nebraska at Lincoln Changbum Ahn

33 Kasey Faust Purdue University Dulcy Abraham

34 Kevin Han University of Illinois at Urbana-Champaign

Mani Golparvar-Fard

35 Krishna Kisi University of Nebraska-Lincoln Eddy Rojas


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36 Kyle Anderson University of Michigan SangHyun Lee

37 Lan Luo Tongji University Qinghua He

38 Laura Florez Georgia Institute of Technology Daniel Castro-Lacouture

39 Lio Liu University of Alberta Mohamed Al-Hussein, Ming Lu

40 Léon L. olde Scholtenhuis University of Twente T. Hartmann

41 Limao Zhang Huazhong University of Science and Technology

Xianguo Wu

42 Lola Ben-Alon Technion IIT Rafael Sacks

43 Maria Nieves Virginia Tech Jesús M. de la Garza

44 Mark Bediako Kwame Nkrumah University of Science and Technology

SKY Gawu and AA Adjaottor

45 Marwa Hussien Concordia University Tarek Zayed, Ashraf Elazouni

46 Maureen Cassin Arizona State University Samuel Ariaratnam

47 Moatassem Abdallah University of Illinois at Urbana-Champaign

Khaled El-Rayes

48 Moataz Omar University of Alberta Aminah Robinson Fayek

49 Mohsen Shahandashti Georgia Institute of Technology Baabak Ashuri

50 Mostafa Batouli Florida International University Ali Mostafavi

51 MyungGi Moon University of Michigan SangHyun Lee

52 Nahid Vesali Mahmoud Florida International University Mehmet Emre Bayraktar

53 Neda Mohammadi Virginia Tech Tanyel Bulbul, John E. Taylor

54 Nirajan Mani University of Nebraska-Lincoln Eddy M. Rojas

55 Omidreza Shoghli Virginia Tech Jesus M. de la Garza

56 Peeraya Inyim Florida International University Yimin Zhu, Wallied Orabi

57 Reza Akhavian University of Central Florida Amir H. Behzadan

58 Reza Maalek University of Calgary Janaka Ruwanpura, Derek Lichti

59 Reza Sheykhi Florida International University Wallied Orabi

60 Ron Patel North Carolina State University Edward J. Jaselskis

61 Ryan Qi Wang Virginia Tech John E. Taylor

62 Seungjun Ahn University of Michigan SangHyun Lee

63 SeyedReza RazaviAlavi University of Alberta Simaan AbouRizk

64 Sooyoung Choe University of Texas at Austin Fernanda Leite

65 Sungmin Yun University of Texas at Austin Stephen P. Mulva, William J. O’Brien

66 Timothy W. Gardiner Virginia Tech Yvan J. Beliveau

67 Tripp Shealy Clemson University Leidy Klotz

68 Vahid Balali University of Illinois at Urbana-Champaign

Mani Golparvar-Fard

69 Yalda Ranjbaran Concordia University Osama Moselhi

70 Yang Cao Georgia Institute of Technology Xinyi Song

71 Yilong Han Virginia Tech John E. Taylor

72 Ying Li University of Kentucky Timothy R. B. Taylor

73 Youngjib Ham University of Illinois at Urbana-Champaign

Mani Golparvar-Fard

74 Zorana Popić Concordia University Osama Moselhi


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1. RFID and BIM-Enabled Worker Location Tracking to Support Real-time Building Protocol

Control and Data Visualization on a Large Hospital Project

Aaron M. Costin ([email protected]), Advisor: Dr. Jochen Teizer

Georgia Institute of Technology

As construction job sites get larger and more complex, the need to increase building protocol control

and security is becoming more necessary. Having a real-time tracking system for materials, equipment

and personnel installed on a job site will help project managers to enhance the security, safety, quality

control, and worker logistics of a construction project. This research presents the method of integrating

passive Radio Frequency Identification (RFID) and Building Information Modeling (BIM) for real-time

tracking of personnel, material, and equipment. The main purpose is to generate real-time data to

monitor for safety, security, and worker logistics, as well as to produce leading indicators for safety

and building protocol control. The concept of reference tags will be utilized along with a cloud server,

mobile field devices, and software to assist the project managers with staying connected with the job

site, from supply chain management to installation. Hardware components include passive RFID tags,

portal RFID readers, fixed turn-style readers, and mobile handheld devices. The system was deployed

on a 900,000 square feet hospital project that consisted of three major buildings, 125 contractors, and

1,200 workers. Preliminary results show that the integration of these technologies enhances

productivity, reduces scheduling issues, assists in subcontractor management, and provides real-time

information on deployed crews and building activities. High-level metrics have been developed at the

project and large contractor level. Additionally, the system also provided real-time information on local

worker participation as part of the project goal. Significantly, based on experimental analysis, it is

demonstrated that the RFID and BIM system is a practical and resourceful tool to provide real-time

information and location tracking to increase safety, security, and building protocol control.


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2. Developing Context Specific and Generalized Construction Labour Productivity Models

Abraham Assefa Tsehayae ([email protected]), Advisor: Dr. Aminah Robinson Fayek

University of Alberta

Construction labour productivity (CLP), which measures the efficiency of construction labourers in

converting a given set of inputs, such as materials and equipment, into tangible outputs, is one

significant issue shaping the viability of undertaking construction projects in Canada due to its

substantial and direct impact on project costs. Unfortunately, Albertan construction labour productivity

at economic level shows a declining trend. This decline together with shortage of labour supply in the

nation and particularly in the province of Alberta has threatened the future of investment in construction

and optimizing CLP through appropriate analysis and modeling is therefore past critical. However,

modeling CLP is a challenge as the input variables (factors and practices) influencing it are numerous,

complex, dynamic, and inconsistent from project to project. Thus, an integrated and flexible approach

to develop and adapt CLP models to suit different project contexts is not yet achieved. This PhD poster

presents the established research framework for developing CLP models based on a system based

granular computing approach so as to addresses the stated limitations. The focus of the research in

terms of the objective and proposed system model based on input (key factors), process (work

sampling proportions), and output (CLP) variables is discussed. Development of context specific CLP

models, adapting developed CLP models to suit varying contexts, and abstracting context specific

models to a generalized CLP model is also presented. Initial findings on the identification of key input

variable categories and preliminary data analysis of the relationship between process variables (work

sampling proportions) and labour productivity are reported. As its final outcomes, the study will

establish multilevel critical factors and practices for improved construction planning and execution and

provide industry with an advanced prediction tool for use in construction planning and project control.


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3. Automated Assessment of Tornado-Induced Building Damage Based on Laser Scanning

Alireza G. Kashani ([email protected]), Advisor: Dr. Andrew J. Graettinger

University of Alabama

The assessment of damage sustained by built infrastructure after natural disasters is of importance to

analyze loads and mechanisms of structural failures in order to improve design and construction

methods. It is also required to prepare loss estimates in order to manage disaster assistance programs

and reconstruction efforts. Perishable damage data should be appropriately recorded, and

investigations should be completed in a timely manner in order to avoid interfering with clean up and

recovery efforts that quickly change damage sites.

3D laser scanning is an effective technology that enables acquiring geometric information of

damaged buildings with high precision. The dense 3D point cloud data produced by laser scanning

virtually reconstructs the damage site and enables engineers to take measurements and retrieve

geometric information needed for damage assessment. Such information is vital to analyze structural

damage and estimate loss. However, manual taking measurements and processing data are

challenging and time consuming due to the large size of data collected and repetitive manual

operations and calculations.

This PhD research aims at developing a data processing framework to automatically analyze

tornado-induced building damage based on laser scanning data. A GIS-enabled data processing

framework was developed to automatically detect damaged roof and wall surfaces in scans of

buildings and generate GIS damage and wind speed maps. This framework also enables to retrieve

geographic information including the percentage of roof/wall damage, the roof pitch, the distance and

orientation of roof/wall surface in respect to tornado path, etc.

Performance of data processing framework was tested with simulated data, laboratory scans,

and actual data collected after tornadoes. Roof models with controlled extent of damage were made

and scanned with controlled settings. Also, simulated point clouds of damaged buildings with

controlled settings and complexities were generated. These point cloud datasets were used to

objectively evaluate accuracy of algorithms and examine impacts of data- and algorithm-related

factors such as point cloud density, extent of damage, color of roof shingles, etc. Therefore, optimum

laser scanning and algorithm settings were identified. These analyses determined that for typical point

cloud density (>25 points/m2), proposed algorithms resulted in less than 10% error in calculating

percentages of roof and wall damage. The proposed framework was also tested with actual damage

data collected after the Tuscaloosa, AL and Moore, OK tornadoes. The developed framework

calculated roof/wall loss and estimated wind speeds at finer scales than the typical large-scale

assessments done by reconnaissance engineers.

The damage assessment framework developed in this research can be adopted for practical

applications in construction industry including:

Automated generation of damage maps for disaster response and recovery

Automated loss database updating for management of insurance and disaster assistance

programs

Automated preliminary loss estimation for cost analysis of repair and reconstruction projects


Page 12 of 84

4. Cost Evaluation Model for Housing Retrofit Decision-Making: A Case Study

Amirhosein Jafari ([email protected]), Advisor: Dr. Vanessa Valentin

University of New Mexico

Since over 60% of the housing inventory in the United States is more than 30 years old, the

refurbishment and retrofitting of old homes is becoming an important issue. The retrofitting of existing

homes can have a positive impact on the environment if methods to conserve energy and natural

resources are considered. Even though there are numerous resources for providing advice on how to

retrofit a facility, it is a complex decision to select optimum combination of retrofitting activities for a

specific building. Two of the main concerns of housing retrofits are how much money the owner needs

to invest and how best to use this investment to ensure an optimum return. A typical solution to these

problems is to evaluate different possible retrofitting alternatives conducting a life cycle cost analysis

(LCCA). The decision-maker can then select a plan with minimum life cycle cost (LCC) as the best

housing retrofit strategy. However, it would be a hard and time consuming process to perform a LCCA

for all possible alternatives for each building. The objective of this study is to introduce a simple

approach for evaluating housing retrofit alternatives, using a real retrofitting case study as a

benchmark. First, a detailed LCCA is performed for implementing a combination of 15 different

retrofitting activities - varying from low to high cost efforts - to the case of a house built in 1950’s in

Albuquerque, New Mexico. After that, assuming that investment of retrofitting costs will reduce energy

consumption cost of the building, an approach is developed to illustrate the trend of retrofitting cost

and energy consumption saving of different housing retrofit plans for the studied case. The initial

results illustrate that the retrofitting plan with minimum LCC for the studied case needs $36,575

investment which may cause decrease in annual utility costs to 15% for life cycle of 50 years. The

results also show that by increasing the life cycle of the project, the optimum LCC retrofitting plan

approaches to Net Zero Energy (NZE) strategy. Using the developed approach and according to the

housing retrofit case study as a benchmark, this study develops a model to evaluate the effectiveness

of retrofitting efforts according to the cost and environmental issues. The developed model can aid

project owners in evaluating their existing retrofitting projects according to the project cost and energy

savings. Further, the results provide guidance for decision-makers to how much money it is preferred

to invest for a new retrofitting project.


Page 13 of 84

5. Segmentation and NURBS Fitting of Unordered Building Point Clouds

Andrey Dimitrov ([email protected]), Advisors: Dr. Feniosky Pena Mora, Dr. Mani

Golparvar-Fard

Columbia University

3D modeling of the built environment is used in a variety of civil engineering analysis scenarios.

Significant applications include generating 3D models for the real-estate industry, building renovation

projects, and inspection of fabrication and on-site assemblies during construction. This data can

originate from structured light methods, mainly laser scanners, or 3D reconstruction methods using

images or videos. The process of generating 3D models from point cloud data involves manually

identifying collections of points that belong to each surface, and then fitting geometry (e.g., meshes,

primitives, NURBS, subdivision) to them. Despite several existing systems that assist with semi-

automated segmentation and placement of architectural elements and building systems such as pipes

and ducts, the process is still primarily manual, painstaking, and requires expertise. The complexity of

the task in hand produces significant technical challenges attributed to: 1) Density: Point clouds exhibit

locally variable densities as well as missing data due to occlusions; 2) Surface Roughness: The

physical texture of common surfaces can range from smooth (steel, marble) to very irregular (grass,

crushed stone) within a single scene, thus no priors about noise levels can be reliably used; 3)

Curvature: Surfaces can be flat, single curved, double curved, or have undulations at multiple scales,

making boundaries hard to define; 4) Clutter: A scene can be made up of multiple objects in close

proximity, making feature detection difficult; and 5) Abstraction: 3D modeling as an abstraction process,

requires some decisions to be made by the user. In consequence, automation needs to balance

flexibility with the ease of use. The over-arching objective of this work is to create and validate a new

method for creating semantically rich CAD models from point cloud data. We address the technical

research challenges by proposing a segmentation and NURBS fitting method for automated modeling.

In our method, the user sets a single parameter that accounts for the desired level of abstraction. We

treat this parameter as a locally adaptive threshold, allowing segmentation to account for local context.

Segmentation starts with a multi-scale feature detection step, describing surface roughness and

curvature around each 3D point, followed by seed finding and region growing steps. We then

successively fit uniform B-spline curves in 2D as planar cross sectional cuts on the surface. An

intermediate B-Spline surface is then computed by globally optimizing the cross sections and lofting

over the cross sections. The final NURBS surface for each segment is computed iteratively as a

refinement of the intermediate surface. We also present a new benchmark of incomplete and noisy

point clouds assembled from a variety of architectural/construction scenes, together with their human-

generated segmentations and NURBS surfaces, which we treat as ground truth. We then compute

metrics that measure how well the computer-generated segmentations and NURBS surfaces match

the human-generated ones, and compare the performance of the state-of-the-art method. The ground

truth in our experiments was manually generated from point cloud scenes. Segments were defined

based on experts’ judgment of surface continuity and not object completeness. We introduce seven

metrics on accuracy and completeness for comparison of segmentation and NURBS fitting algorithms.

By comparing results with the state-of-the-art methods using these metrics, our method shows reliable

performance for both segmentation and NURBS fitting in challenging point clouds. Our contributions

are three-fold: First, we present a region-growing algorithm based on multi-scale geometrical features

that treats the desired level of modeling abstraction as a locally adaptive parameter. Second, we

introduce a NURBS fitting method that accounts for all the topological variations and reliably maps

physical space to parameter space given unordered, incomplete, and noisy point clouds. Third, we

present metrics and perform quantitative comparisons of our method with the human-generated

segmentations, and provide a publicly available dataset for future analysis and comparison of point

cloud segmentation.


Page 14 of 84

6. SAVES II: A Multiple Signals Enhanced Augmented Virtuality Training System for

Construction Hazard Recognition

Ao Chen ([email protected]), Advisors: Dr. Brian Kleiner, Dr. Mani Golparvar-Fard

Virginia Tech

Safety training arises its importance in construction in recent two decades but it doesn't approach the

full expectations in practice as it supposed to. High number of fatalities and injuries occur every year

that push those companies which treat safety as their core value more eager to improve the effort of

safety training. Accompany with the rapid growth of IT innovation in current years, Augmented

Virtuality (AV) presents its potential in construction safety training. Comparing with Augmented Reality

and Virtual Reality, AV brings high quality of realistic telepresence and enhanced power of synthetic

imagery experience without exposing the workers to the fully hazardous training site. SAVES, an AV

based strategy for training construction hazard recognition is developed and applied to improve the

workers’ safety awareness and hazard recognition skills in order to approach the desired expectations

of best safety program. SAVES has showed its effectiveness and benefits in the field test but there is

a need to better understand 1) whether different safety programs and multiple signals can be

synthesized for hazards perceiving and 2) such plentiful information stream can help to maximize

hazard recognition skills in workers. Thus, SAVES II is developed to answer such questions. Besides

the integrated BIM, photographs of typical energy sources, built-in augmented training scenarios and

interactive avatar that developed in SAVES, the traditional training methods such as lectures, videos

and power points are enhanced in SAVES II in order to response to the questions. Furthermore,

multiple enriched signals such as visual signals, audio signals and difficult levels are also developed

with the purpose of testing how worker’s working memory perceives the information with such multi-

signals and helps to improve hazard recognition skills in long term memory. The modeling process,

analysis and the current lessons learned are discussed later.


Page 15 of 84

7. Quantifying Energy-Use Behavior in Commercial Buildings

Ardalan Khosrowpour ([email protected]), Rimas Gulbinas ([email protected]), Advisor: Dr.

John E. Taylor

Virginia Tech

Buildings account for more than 40% of CO2 emissions in the United States and recent research has

indicated that CO2 emissions of commercial buildings are expected to increase faster than all other

types of buildings at an annual rate of 1.8%. Recent advances in building technologies and materials,

construction methods, building monitoring and control systems, and other similar areas has enabled

the development and management of increasingly energy efficient environments. However,

technology independent elements such as the behavior of building occupants remain significant

factors responsible for energy consumption and associated CO2 emissions of these buildings. In this

research, we illustrate a novel approach to quantify and target inefficient occupants’ energy

consumption. The primary objective is to classify commercial building occupants based on their energy

consumption pattern. The secondary objective is to design a new metric for building occupant energy-

use entropy based on energy consumption pattern consistency. We developed an algorithm that

implements clustering and machine learning methods along with various entropy measures to

categorize the occupants, days, and hours based on energy consumption rates and patterns. Using

real-time high resolution wireless power meters, we conducted an energy monitoring experiment in a

commercial building involving approximately 100 participants located in Denver, CO. The data from

this experiment were used to develop the models and to assign occupants with similarly patterned

energy consumption into categories. This novel methodology executes as a powerful proxy to ease

the energy prediction procedure for commercial building occupants. More importantly, it also enables

us to categorize employees based on their energy-use patterns. Results demonstrate 6 distinct

categories for occupants and entropy measures fully support the robustness of our model. Moreover,

clustering along business days show a fairly uniform distribution of consumption patterns among all

days of a week while each individual benefits from a specific subset of existing patterns. Obtained

outcomes hold promise of efficient and inexpensive energy reduction by targeting and quantifying high

and inconsistent energy consumers in the commercial sector. Holding targeted interventions and

offering personalized feedback to building occupants will provide us with an enormous capability of

energy reduction.


Page 16 of 84

8. Adoption Readiness of Prevention through Design (PtD) Controls in Concrete, Masonry,

and Asphalt Roofing

Ari Goldberg ([email protected]), Advisor: Dr. Deborah Young-Corbett

Virginia Tech

Concrete, masonry, and asphalt roofing operations are associated with some of the most pressing

occupational health hazard risks in construction. The Prevention through Design (PtD) approach to

controlling these risks involves the design of tools, equipment, systems, work processes, and facilities

in order to reduce, or eliminate, hazards associated with work. Though PtD controls exist, the extent

of their use is yet to be documented. To determine current usage trends and adoption-readiness of

decision-makers regarding PtD controls in the concrete, masonry, and asphalt roofing trades. A survey

instrument to capture information about current PtD control usage trends and decision-maker opinions

about PtD controls was developed and validated. Controls investigated for concrete/masonry are

dust collection equipment, wet-method systems, isolation systems, and sweeping compound.

Controls investigated for asphalt roofing are asphalt tanker delivery systems, hot luggers/mechanical

spreaders/felt-laying machines, insulated kettles/insulated hot luggers, low-fuming asphalt, and kettle

fume guards. A telephone survey was completed of 365 decision makers in member firms of the

Mason Contractors Association of America (MCAA)(n=700), the Concrete Sawing and Drilling

Association (CSDA)(n=541), the American Concrete Pavement Association (ACPA)(n=4000), and the

National Roofing Contractors Association (NRCA)(n=4000). Data analysis is currently underway. This

poster will present the validated survey instrument and preliminary findings about PtD control solutions

in these three construction trades. The results will enable and understanding of the barriers to adoption

of healthier controls. The identification of barriers to adoption will allow for greater diffusion within the

construction industry. The development of intervention strategies based on key constructs such as

“perceived benefits”, “perceived risks”, “relative advantage”, and “trust in technology” is a novel

contribution of the proposed work and offers the potential for translation of this work’s findings into a

broader industrial population.


Page 17 of 84

9. A Bio-inspired Virtual Pedagogical Environment to Stimulate Bio-inspired Thinking

Aruna Muthumanickam ([email protected]), Advisor: Dr. John E. Taylor

Virginia Tech

The BioBuild program: Building professionals are challenged with delivering complex built

environments that meet the needs of shifting societal and environmental needs. Bio-inspired thinking

is a comprehensive approach that can be used to address many of the problems of urbanization by

applying answers from living systems, i.e., the biological world. The BioBuild program at Virginia Tech

is a newly launched interdisciplinary effort that rigorously prepares professionals who can contribute

effectively to the development of a bio-inspired built environment.

CyberGRID - Virtual environment for pedagogy: CyberGRID (Cyber-enabled Global Research

Infrastructure for Design) is a virtual environment created by researchers at Columbia University and

Virginia Tech that provides an opportunity for geographically distributed teams to collaboratively work

on design and construction projects. The use of the environment furthers civil engineering pedagogy

by leveraging the benefits of simulating the professional project collaboration experience virtually.

Bio-inspiration: In this poster, we describe the design of a bio-inspired version of the

CyberGRID that draws inspiration from the vascular system of trees. The mechanism by which

essential nutrients and water are transmitted to various plant parts inspires the translation of

knowledge and dissemination into architectural elements.

Bio-inspired CyberGRID: The bio-inspired version of the CyberGRID is a bio-mimicked virtual

teaching-learning experience that focuses on preparing future professionals for the design and

construction of bio-inspired built environments. This is done by providing an opportunity to experience

bio-inspired structures (e.g., the Turning Torso in Sweden and the Gherkin in the UK) in a bio-inspired

environment. Furthermore, the bio-inspired CyberGRID provides the infrastructure for student-led bio-

inspired projects to be developed, presented and stored for viewing by future course participants.


Page 18 of 84

10. Optimizing the Sustainability of Single-Family Housing Units

Aslihan Karatas ([email protected]), Advisor: Dr. Khaled El-Rayes

University of Illinois at Urbana-Champaign

The sustainability of housing units can be improved by optimizing their social, environmental, and

economic performances. The integration of green building equipment and systems such as

geothermal heat pumps and water-efficient faucets often improves the social and environmental

performances of housing units; however they can increase their initial cost and life cycle cost.

Therefore, decision-makers need to carefully analyze and optimize the potential tradeoffs between the

social, environmental, and economic performances of housing units.

The main goal of this study is to develop novel multi-objective models for optimizing the

sustainability of single-family housing units that represent 66% of the residential housing inventory in

the US. To accomplish this goal, the research objectives of this study are to develop (1) an innovative

housing social impact model that is capable of generating and analyzing optimal tradeoffs between

the social quality of life for housing residents and the life cycle cost of housing; (2) a novel housing

environmental performance model for maximizing the environmental performance of housing units

while minimizing their initial cost; (3) a multi-objective optimization model that provides the capability

of generating optimal tradeoffs among the three housing sustainability objectives of social quality-of-

life, environmental performance, and life cycle cost; and (4) a scalable and expandable parallel

computing framework that provides the capability of reducing the computational time of optimizing

housing sustainability decisions and transforming this optimization problem from an intractable

problem to a feasible and practical one.

The performances of these developed models and framework were analyzed and refined using

case studies of single-family housing units. The results of these performance evaluations illustrated

that the developed optimization models were capable of generating a wide range of optimal solutions,

where each identifies an optimal configuration of design and construction decisions that provides an

optimal tradeoff among the three housing sustainability objectives. These novel research models and

framework are expected to enhance the current practice of housing design and construction and

contribute to maximizing the sustainability of single-family housing units.


Page 19 of 84

11. Three-Tiered Data & Information Integration Framework for Highway Project Decision-

Makings

Asregedew Woldesenbet ([email protected]), Advisor: Dr. “David” Hyung Seok Jeong

Iowa State University

State highway agencies invest a large amount of resources in collecting, storing and managing various

types of data ranging from roadway inventory to pavement condition data during the life cycle of a

highway infrastructure project. Despite this huge investment, the current level of data use for decision

making is highly limited in many circumstances and is raising serious concerns whether the growing

amount of data adds value to the final users and offers any meaningful return on the data collection

efforts. To date, there has not been any standard procedure or a tool in the highway industry to

integrate and assess the level of data utilization and/or help evaluate and justify these data collection

efforts. This study presents a holistic approach that can systematically integrate and bridge data with

information and decisions through incorporation of a unique and proactive performance assessment

technique to improve the utilization of growing amount of data in transportation agencies for effective

decision-making processes. With a focus on enhancing active utilization of data and measuring the

level of data use in supporting highway management decisions, this approach delivers i) three-tiered

hierarchical framework and ii) a new data and information performance assessment tool, Highway

Infrastructure Data Integration (HIDI) index through the application of a social network theory. The

HIDI index is developed to evaluate the status of data utilization that may serve as Highway

Infrastructure Data Report Card and help justify the return on investment on the continuous and

growing data collection efforts. It will allow agencies to interlink data, information and decisions and

develop active utilization plan of currently existing databases to placing the right information in the

hands of decision-makers. It will also allow agencies in the development of new data collection and

knowledge generation plan to support key decisions which historically were not well-supported with

information and data. This new framework may be used as a benchmarking example to State Highway

Agencies in the area of data and information integration to make effective and reliable decisions

through data-driven insights.


Page 20 of 84

12. Minimizing effects of overfitting and collinearity in construction cost estimation: A new

hybrid approach

Bo Xiong ([email protected]), Advisor: Dr. Martin Skitmore, Dr. Bo Xia

Queensland University of Technology

Overfitting and collinearity problems are commonly existed in current construction cost estimation

applications and many other topics in construction management discipline. A hybrid approach of

Akaike information criterion (AIC) stepwise regression and principal component regression (PCR) is

proposed to solve overfitting and collinearity problems, and validated by comparing with other linear

regression models. Mean square error by applying leave one out cross validation (MSELOOCV) is

used in model selection during deciding predictive variables and principle components. The

MSELOOCV of AIC regression model is 2.0% less than the default stepwise regression with

minimizing the sum of squared error (SSE) and 19.9% less than the stepwise regression with

maximizing adjusted R square and 48.2% less than the regression model with all predictive variables.

The PCR is introduced after selection of predictive variables. In principal components selection, the

SSE model and AIC model are selected for their MSELOOCV values much less than those of the

adjusted R square model and the regression model keeping all principal components. The AIC-PCR

approach not only solves overfitting and collinearity problems, but also improve the predictability with

7.8% less MSE than the default stepwise regression model. The validity of this approach is validated

in this research and an AIC-PCR standard procedure is for researchers and practitioners to apply this

approach in overcoming the widely existed overfitting and collinearity problems when dealing with

other similar forecasting situations.


Page 21 of 84

13. Automated Real-Time Tracking and 3D Visualization of Construction Equipment

Operation Using Hybrid LiDAR System

Chao Wang ([email protected]), Advisor: Dr. Yong K. Cho


The interactions between workers, equipment, and materials can easily create visibility-related

accidents. Visibility problems can lead to serious collisions without pro-active warnings. There have

been a number of advances in vision-aid techniques because lacking full visibility is a major

contributing factor in accidents at construction sites. However, unstructured work areas like

construction sites are difficult to graphically visualize because they involve highly unpredictable

activities and change rapidly. Construction site operations require real-time or near real-time

information about the surrounding work environment, which further complicates graphical modeling

and updating. One commonly used method to obtain the 3D position of an object is based on 3D laser

scanning technology; this method, however, has some limitations, such as low data collection speed

and low object recognition rates. It has always been a challenge to recognize specific objects from a

3D point cloud in unstructured construction environments because it is difficult to rapidly extract the

target area from background scattered noises in a large and complex 3D point cloud. While rapid

workspace modeling is essential to effectively control construction equipment, few approaches have

been accepted by the construction industry due to the difficulty of addressing all the challenges of

current construction material handling tasks with the current sensor technologies. The main objective

of this research is to design, develop, and validate a 3D visualization framework to collect and process

dynamic spatial information rapidly at a cluttered construction job site for safe and effective

construction equipment operations. A custom-designed hybrid LiDAR system was developed in this

study to rapidly recognize the selected target objects in a 3D space by dynamically separating target

object’s point cloud data from a background scene for a quick computing process. A smart scanning

method was also developed to only update the target object’s point cloud data while keeping the

previously scanned static work environments. Then the target’s point cloud data were rapidly

converted into a 3D surface model using the concave hull surface modeling algorithm after a process

of data filtering and downsizing to increase the model accuracy and data processing speed. The

generated surface model and the point cloud of static surroundings were wirelessly presented to the

equipment operator. Validation of the proposed methodology was implemented at a real world

construction jobsite. The 3D surface model of the tested equipment can be generated in less than a

second, and then wirelessly transmitted to the operator in the cabin through a configured local network.

The test results indicate that the proposed rapid workspace modeling approach can improve the heavy

equipment operations by distinguishing surface-modeled dynamic target objects from the point cloud

of existing static environment in 3D views in near real time. This research proposes fundamental

research on 3D workspace modeling to foster a breakthrough innovation in robotic manufacturing and

automated construction site operations which will greatly benefit the future U.S. construction industry

and society in general. The knowledge obtained will enrich the literature in the important area of

construction engineering and management and provide solutions to industry-driven problems. This

research also proposes vigorous goals for integrating research and education.


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14. Interdependent Infrastructure Network System Vulnerability Identification

Christopher Van Arsdale ([email protected]), Advisor: Dr. Amlan Mukherjee

Michigan Technological University

For municipal infrastructure managers, identifying the vulnerabilities of infrastructure networks is a

difficult task. As infrastructure networks become more interdependent, the ability to analyze and

identify points that could disrupt network or system performance become more complicated while

simultaneously becoming more important. By identifying the vulnerabilities in infrastructure systems,

managers will be able to proactively plan contingencies and optimally utilize resources to avoid failures

in vulnerable networks. In addition, Planners and designers will minimize the vulnerabilities in new or

upgraded systems by having a specific measure to apply for certification from sustainability ratings

systems such as EnvisionTM, e.g., criteria CR2.5 in EnvisionTM, requires “Documentation outlining

potential traps and vulnerabilities and associated costs of risk”.

The literature indicates that individual infrastructure network performance is based on the

topology and flow in a network. However, these studies have not tried integrating multiple types of

networks to assess their vulnerability from network co-dependence. When networks of infrastructure,

traffic, or natural systems are interconnected, either physically or due to proximity, one network

becoming unviable has the potential force other networks’ to become unviable. This research will help

close this gap and provide decision makers metrics of their integrated infrastructure system

vulnerabilities. Therefore, the objectives of this research are: 1) Develop metrics for robustness and

resilience of an integrated infrastructure network. 2) Develop a method to identify critical points in an

integrated infrastructure network consisting of natural and transportation networks and rank them in

order of vulnerability. The fundamental hypothesis driving this research is that vulnerability of a system

is a function of system resilience and robustness. Within the context of this research, robustness is

defined as the amount of disruption a system can withstand before becoming unstable and unviable.

Resilience is the time it a system takes to recover to a stable and viable state. Viability is defined as a

state where the network or system meets the minimum level of service for its users. Methods based

in Graph and network theory will be used. Viability theory will be employed to determine limits within

which a system can viably deliver an acceptable level of service. A simulation system is being

developed to test how alterations in the network topology and flow are likely to effect system

performance. Specifically the study will test the sensitivity of networks to probabilistic link-disabling

events. For example the loss of a road or bridge, or a rapid influx of traffic onto a highway effectively

disables that link in the network and will affect the network performance.

Current research is developing a framework to plot simulated measurements of system viability

to identify states and conditions under which a network is stable and viable. The representation of the

networks in question (transportation and natural networks for this research) will be constructed using

data from existing GIS datasets.


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15. Volatile Organic Compounds Emissions Generated in Hot-mix Asphalt Pavement

Construction and Their Health Effects on Pavement Workers

Dan Chong ([email protected]), Advisor: Dr. Yuhong Wang

The Hong Kong Polytechnic University

Hot-mix asphalt (HMA) pavement is widely used in the building roads, airport runway, and parking lots.

The unique characteristic of HMA pavement construction is that its placement and compaction have

to be conducted at elevated temperature, which typically range from 135 °C to 150 °C. During this

high temperature, massive amount of volatile organic compounds (VOCs) are generated and released

to the air, which pose a potential health risk to pavement construction workers. The connection

between the pavement workers’ exposure to VOCs and the pavement construction process has not

been adequately addressed in existing studies. The aim of this study is to assess the VOCs generated

in HMA pavement construction and investigate their potential effects on pavement workers’ health.

Forty VOCs samples were collected from various locations (emission source points ESPs and worker

breathing zone WBZs) and time points of six HMA pavement projects, and were subsequently

analyzed in the laboratory for characterization of their chemical compositions and concentrations by

using gas chromatography/mass selective detector (GC/MSD). The findings include the following: 1)

the chemical species in VOCs at different time points and locations during HMA construction are

closely correlated; 2) the VOCs concentrations during the pavement stage are higher than those during

the compaction stages, and they generally decline with time; 3) porous asphalt mixture seemingly

generates more VOCs emissions than the no-porous asphalt mixtures; 4) VOCs concentration is

higher at the workplace in the absence of wind; 5) the majority of the identified chemicals are listed as

hazardous materials by various occupational regulatory agencies; however, their concentrations at the

ESPs and WBZs are below the mandated or recommended exposure limits, except for a few identified

chemicals whose toxicological profiles have not been developed. Possible mitigation opportunities

were also examined including emission source control, intervention in the VOCs propagation path,

and receptor protection. This paper contributes to the knowledge of the types and concentrations of

VOCs generated in asphalt pavement construction, their potential health risks to workers, and possible

mitigation measures.

mailto:[email protected]


Page 24 of 84

16. Quantitative Performance Assessment of Single-Step and Two-Step Design-Build

Procurement

David Ramsey ([email protected]), Advisor: Dr. Mounir El Asmar, Dr. G. Edward

Gibson

Arizona State University

The use of design-build (DB) in the construction industry has become increasingly common, and

several studies have shown increased project performance for DB as compared to other project

delivery systems (e.g., Songer and Molenaar 1997, Konchar and Sanvido 1998, Wardani et. al. 2006).

There are two primary methods used to procure DB services: single-step & two-step. Single-step DB

involves design-builders responding to an owner’s solicitation that requires qualifications, technical,

managerial and/or cost components in one submittal. Two-step DB involves first submitting a

statement of qualifications and the owner shortlisting a limited number of firms, who are then invited

to prepare full proposals. Major design and construction stakeholders feel that single-step DB places

a lot of burden on the industry, mainly due to the risk associated with the extensive resources

expended during project procurement. Quantitative performance assessments have not been

completed to compare the effectiveness of single-step DB versus two-step DB procurement. The

primary goal of this research is to examine the resource expenditure and efficiency impacts (e.g., the

procurement performance) associated with single-step DB as compared to two-step DB. Three

objectives were completed in order to achieve this research goal: (1) the resource expenditure from

the industry perspective (e.g., the DB team) was calculated through studying procurement costs for

each method. (2) Procurement schedule performance was quantified. (3) Quality and potential for

innovation were also documented by collecting data about innovative solutions and various quality

metrics for the procurement and project phases. In order to characterize the potential differences

between single-step DB and two-step DB, the authors developed a research plan with three distinct

phases. Phase I consisted of a thorough review of the DB procurement body of knowledge, as well as

development of the data-collection survey with input from expert researchers and industry

collaborators. Phase II consisted of data collection from project managers and project executives

through surveys and follow-up phone interviews. Phase III consisted of univariate data analysis and

interpretation of the results. Results show two-step DB is a more effective procurement method as

compared to single-step DB. In fact, offerors of single-step DB projects are expending, on average,

5.43% of total project cost on the procurement phase. Conversely, offerors of two-step DB projects

are expending on average 1% of total project cost on procurement. Additionally, there was no

conclusive evidence showing the relative procurement schedule performance of single-step projects

was reduced as compared to two-step projects. Moreover, performance-based technical requirements

seem to be more prevalent in two-step projects over single-step projects, potentially leaving room for

more innovation with two-step DB. Another interesting result is the two-step projects achieving a higher

LEED rating than their single-step counterparts. Prior to this research, the literature was lacking a

quantification of DB procurement costs based on actual project data. The contribution of this research

is presenting clear performance differences by evaluating and quantifying key procurement metrics for

single-step and two-step DB. The results are of extreme practical significance to the DB industry, in

fact this study was funded by the Design-Build Institute of America (DBIA). The quantification of DB

procurement cost and schedule can inform DB industry stakeholders about the potential implications

associated with selecting single-step versus two-step DB procurement.



Page 25 of 84

17. Risk Allocation in Public-Private Partnerships: Analysis of Contractual Provisions in

18 U.S. Highway Projects

Duc A. Nguyen ([email protected]) and Edwin Gonzalez ([email protected]), Advisor: Dr. Michael J.

Garvin

Virginia Tech

Public-Private Partnerships (PPPs) are typically characterized as: long-term contracts between the

public and private sectors; where the private entity provides design, build, finance, operation, and

maintenance services; and puts private equity at risk. Risk allocation is fundamental to PPPs since

this delivery method is often justified based on risk transfer. The subject of risk has received significant

treatment in the literature covering topics such as risk categorization, risk allocation, risk management

methods, and risks and contracts. Yet, comprehensive studies of how risks are manifested and

allocated within PPP project contractual provisions are generally absent. This investigation remedies

this within the context of the United States by examining 18 PPP highway project contracts.

The purpose of this research is to determine how the major risks in PPP projects are defined,

allocated and managed within contracts. All U.S. highway PPP contracts, from 1993 to 2013, where

the contract period exceeded 30 years and private equity was at risk are examined.

Project risks were categorized and identified based on a comprehensive literature review.

Subsequently, a rubric for risk identification, characterization, and allocation within PPP contracts was

constructed and tested; the rubric is being applied to the 18 highway projects. Using content and

comparative analysis techniques, we are analyzing the outcomes of the rubric’s application to the

project set to contrast provision language and risk allocation by various project characteristics such as

payment mechanism, jurisdiction, and project scope.

Results provide a comprehensive overview of contractual risk allocation in U.S. PPP highway

projects. We observe that risk allocation and management provisions have changed over time and are

similar along some dimensions while varying along others. For example, risk allocation appears to be

influenced by different jurisdictions: while latent defects risk in the Presidio Parkway project (California,

2012) is held solely by the public authority, that same risk is shared using a deductible scheme

between the public authority and the private developer in the I-595 project (Florida, 2009) although

both use availability payments. Contrasting two toll projects–the I-495 project (Virginia, 2007) and the

I-635 project (Texas, 2010)–indicates interesting differences as well. In I-635, the public authority

bears site acquisition risk while shares network, material adverse, and latent defects risks with the

private developer. Yet in I-495, the private developer solely bears all of these risks. The Presidio

Parkway is one of the most recent projects, and its contract introduces an innovative flexibility provision

by giving the private developer an option to choose either availability payments or tolling mechanisms.

The research is significant since it is one of the first systematic and comprehensive examinations of

PPP contractual provisions done. It has also demonstrated the importance of jurisdictional preferences

and market precedence on PPP contracts. It will also provide a baseline understanding of risk

allocation in U.S. highway PPPs. Additional investigation of the relationships among the risks,

socioeconomic factors and project characteristics can reveal patterns for successful risk allocation in

PPPs. Further, the baseline developed and the comparisons drawn have important practical value

since practitioners may examine how contractual provisions and risk allocation have evolved and been

implemented in various jurisdictions and types of projects.



Page 26 of 84

18. Extending Building Information Modeling (BIM) Interoperability to Geo-Spatial Domain

using Semantic Web Technology

Ebrahim P. Karan ([email protected]), Advisor: Javier Irizarry


The decision making process in a construction project is based on available information (usually

extracted from different sources) coupled with the domain knowledge possessed by an individual.

Each representation of an object or input in the individual’s mind is tagged with a meaning. When

making a decision, it is often not enough to merely access information; rather, it is necessary to

understand the meaning (or semantic) of the acquired information. Thus, the semantic web technology

is used in this study to discover the relationships between these different sets of semantics and depict

whether the pair of concepts are similar or not. Currently, the results of Semantic Web queries are not

supported by any building information modeling (BIM) authoring tools.

The purpose of this study is to extend the interoperability of BIM authoring tools in construction

domain by employing semantic web technology. This research develops an ontology based on

Industry Foundation Classes (IFC) schema to publish BIM data as the semantic web data format and

also provides a query rewriting method to translate query results into the XML representations of the

IFC schema and data.

Using the concepts and relationships in an IFC schema, we first develop ontology for

construction operations to translate building's elements into a semantic web data format. Then, a

mapping structure is defined and used to integrate and query the heterogeneous spatial and temporal

data. Finally, we use a query language to access and acquire the data in semantic web format and

convert them into the XML representations of the IFC schema, ifcXML. Through two scenario

examples, the potential usefulness of the proposed methodology is validated. Also, the resulted ifcXML

document is validated with an XML schema validating parser and then loaded into a BIM authoring

tool.

The research findings indicate that the format of the output is the most important component

of the query results. The developed interface for representing IFC-compatible outputs allows BIM

users to query and access building data at any time over the web from data providers. Linked data, as

a concept that arises within the paradigm of semantic web, helps to overcome interoperability

challenges to enhance information exchange in the construction domain. Also, it is concluded that

Semantic web technology can be used to convey meaning, which is interpretable by both construction

project participants as well as BIM applications processing the transferred data.

The models developed for extending interoperability between BIM and geo-spatial analysis

tools are focused at the syntactic level, in which the emphasis is on integrating two or more data

models into a single, unified, model. A higher level of integration (i.e. semantic interoperability) is used

at this study to share information and their meanings between BIM and Geo-Spatial datasets. Currently,

the results of Semantic Web queries are not supported by BIM authoring tools. Thus, the proposed

methodology utilizes the capabilities of ontology languages to transform the query results to an XML

representation of IFC data.



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19. Quantifying the risks of wildfire to buildings in Wildland Urban Interface: a forward view

Elmira Kalhor ([email protected]), Advisor: Dr. Vanessa Valentin

University of New Mexico

Wildfire is a complex phenomenon that reforms ecosystems, changes species habitat and changes

our lifestyle. The cause and spread of wildfire, has social, ecological, spatial, atmospheric and

economical factors all of a dynamic nature, which need to be considered when evaluating wildfire risk.

Wildfire research has spread across disciplines of science and research from ecology to sociology to

economics to management and engineering. Hence, the risk of wildfire has different meanings for

different researchers. Despite its significant role, however, wildfire research is limited in the field of

urban planning.

Wildfires often find their way to the Wildland-Urban Interface (WUI) where residential buildings

get closer to the wildland (national forests, national parks, etc.). Regardless the increase in frequency

and severity of wildfires, housing development projects progress further towards these flame zones

increasing the vulnerability of the community. The long term goal of this study is to answer the question:

how urban planning and safety management can account for the risk of wildfire to buildings, specifically

those located in WUIs?

The objective of the first component of this research is to find a distinct probability function

associated with any location of interest (present or potential buildings) which explains the likelihood of

having a fire of a precise intensity (heat and flame length) at a specific location. Fire intensities will be

translated into building damage, in order to find the total risk of wildfire as the intersection of probability

and damage.

In order to define the aforementioned probability functions, this research expands on existing

advanced fire propagation models. These models will be coded and run for a variety of scenarios to

find the frequency of occurrence of specific fire intensity for urban buildings. Significant factors

affecting the probability of wildfire occurring on a specific location will be identified.

The results of this study provide reliable probability functions for defining the likelihood that a

specific building in the WUI will be affected by a potential fire and the expected impact of this event.

The risk model is used to minimize the risk of fire by producing optimum land use designations.

Few models in the management discipline have used regression analysis to find the risk of

wildfire considering different study zones in order to prioritize the mitigation measures of federal land

agencies. The novelty of this study comes from the focus on probability for calculating wildfire risk in

urban planning applications. All possible fire events are simulated using available advanced

thermodynamic formulations. The results of this research appear in the form of a Decision Support

System (DSS) that not only is usable for urban planners but is also helpful for the insurance industry

and federal land agencies. This DSS helps managers to optimize urban design, to allocate emergency

resources, and to prioritize preventive actions.


Page 28 of 84

20. Collaboration through Innovation: A Multi-Layered Framework for the AECM Industry

Erik A. Poirier ([email protected]), Advisor: Dr. Daniel Forgues, Dr. Sheryl Staub-French

École de Technologie Supérieure

The contemporary landscape of project delivery in the Architecture, Engineering, Construction and

Maintenance (AECM) industry is being redesigned through two innovations: Building Information Modeling

(BIM) and Integrated Design and Project Delivery (IDPD). Both these innovations epitomize the radical shift

towards integration of practice, of process and of information. Consequently, they are pushing us to rethink

how and why we collaborate. Considerable research work has aimed at developing tools and processes

that foster collaboration by focusing on these particular innovations. In this light, collaboration is typically

viewed as a means to an end; its measures of improvement and success are generally limited to project

outcome. However, even through progress, collaboration remains amorphous. As a core tenet of the AECM

industry, collaboration should be examined beyond this teleological stance. From this perspective,

considerable gaps appear around the emergence and evolution of collaboration through innovation. The

principle research objective of this project is to investigate the dynamics that characterize the emergence

of collaboration through innovation. The research project aims to address the gaps that appear when

attempting to assess what is structuring, motivating, mediating and informing collaboration from an

evolutionary perspective. The focus is on integration of information, of practice and of process through

innovation, in particular BIM and IDPD. Secondary objectives mirror an iterative and grounded approach to

research: In a first cycle, a framework is built; the aim is to characterize collaboration in the AECM industry.

In a second cycle, the framework is operationalized and tested: the aim is to operationalize the framework

and assess the impact of these innovations on collaboration, its emergence and its evolution. This research

project adopts a constructivist approach to grounded theory; it is rooted in the interpretivist paradigm. The

methodology is case study based, which allows an in-depth enquiry of phenomena through a mixed-method

approach to data: both qualitative and quantitative data are being collected and analyzed. Five case studies

have been targeted representing the full project continuum. The intent is to maximize the scope of

theoretical sampling. The constructive grounded theoretical approach underlies an iterative process

through which theory emerges and can be tested in an attempt to reach theoretical saturation. Thus, the

case studies are serving as the basis to build, operationalize, test, validate and saturate the framework.

The results of this research project are articulated around two threads: developing the framework and

operationalizing it. To date, the framework has developed into three embedded layers, which have emerged

from the data - the agentic layer, which informs the structural layer, which in turn mediates the operational

layer . The emergence of collaboration and its evolution (as opposed to its finality) are at the core of the

framework. When operationalized, the framework intimates alignments amongst layers as a way to develop

collaboration. Preliminary findings suggest that alignments within and across these layers between

individual project team members tend to enhance collaboration. In this regard, we have observed a positive

emergence of collaborative behaviours and actions through integrative innovations that were implemented

within an ‘aligned’ project setting. On the other hand, misalignments have been observed to contribute to

‘misfires of innovation’, a misuse or underdevelopment of said innovations. In this regard, we have observed

a hindrance to collaborative behaviours and actions through integrative innovations that were implemented

within a ‘misaligned’ project setting. The anticipated scientific contribution of this research project is the

development of a substantive theoretical framework aimed at characterizing the emergence and evolution

of collaboration through innovative approaches to project delivery in the AECM industry. Where most

research in this field adopts a positivist stance aimed at developing tools to improve collaboration, this

research project takes a constructivist stance to characterize layered categories of collaboration and study

how innovations impact their emergence and evolution. It offers an alternative perspective while highlighting

areas for future enquiry across these categories. In terms of practical significance, this substantive

theoretical framework is grounded in practice. It is scalable and aggregatory, i.e. it can be applied across

scales and scopes. As such, its application offers a common language to foster collaboration in practice

and can serve in orienting focus when implementing innovations, which span project networks.


Page 29 of 84

21. The Virtual Construction Simulator: An Educational Game in Construction Engineering

Fadi Castronovo ([email protected]), Advisor: Dr. John I. Messner

The Pennsylvania State University

The construction of a facility is a dynamic process, governed by complicated problems and solutions.

This complex nature poses instructors the difficult task of developing pedagogical strategies to teach

engineering students how to tackle such processes. However, traditional construction planning and

management teaching methods have been criticized for presenting students with well-defined

problems, which don’t reflect the challenges present in the industry. An innovative teaching method –

educational simulation games – has shown potential in teaching students complex construction

processes, in a simulated construction environment. In addressing these instructional challenges, we

will illustrate the lessons learned in the development and design of complex serious games. The work

presented is a result of the current research efforts in the development of the Virtual Construction

Simulator (VCS) a project supported by the National Science Foundation.


Page 30 of 84

22. Predictive Emissions Models for Excavators

Heni Fitriani ([email protected]), Advisor: Dr. Phil Lewis

Oklahoma State University

Heavy duty-diesel (HDD) construction equipment consumes large quantities of fuel and subsequently

emits significant quantities of air pollutants. In most of construction activities, HDD construction

equipment is the primary source of emissions. The purpose of this poster is to demonstrate two

different predictive modeling methodologies for estimating emission rates for HDD construction

equipment particularly excavators. The model were developed using real-world data collected using

Portable Emissions Measurement System (PEMS). The modeling methodologies used are Multiple

Linear Regression (MLR) and Artificial Neural Network (ANN). These modeling techniques were used

to produce models to predict emission rates of nitrogen oxides (NOx), hydrocarbons (HC), carbon

monoxide (CO), carbon dioxide (CO2), and particulate matter (PM). Results show that in most cases,

the MLR approach produced highly precise models for NOx, CO2, and PM. The models for HC and

CO were less precise with R2 values ranging from 0.09 – 0.80. However, ANN models performed the

best with regard to precision, accuracy, and bias. Overall, the results of this study help quantify and

characterize the air pollution problems from HDD equipment used in construction. The methodologies

presented may certainly be used to develop emissions models for other types of equipment.


Page 31 of 84

23. Estimating Extreme Event Recovery with Construction Activity Change Points

Henry D. Lester ([email protected]), Advisor: Dr. Gary P. Moynihan

University of Alabama

Repairing and rebuilding structures following an extreme event requires a substantial outlay of

resources to achieve full disaster recovery. Demographic shifts toward highrisk communities are

increasingly placing both populations and the built environment at substantial loss exposure to such

extreme events. Instead of retaining extreme event risks associated with these communities, property

owners expect to transfer the risk to government regardless of the exploding municipal debt. The

shrinking municipal assets restrict availability of resources for extreme event recovery operations. The

associated construction increases in disasterprone areas dictate disaster planning to safeguard at risk

populations during the recovery phase of the disaster life cycle and this planning necessitates a

temporal recovery metric. This poster presents research employing a change point approach to

estimating extreme event built environment system recovery. Specifically, the research considers

Fisher type price adjusted spatial new singlefamily residential building permits as a time series

signifying residential construction activity. The research compares changes in this residential

construction activity with accompanying declared disasters to ascertain any relationships. The

approach examines spatiotemporal residential construction variability to determine built environment

rapidity by measuring the duration between sequential time series change points. The change point

approach and resultant temporal recovery metric allows estimation of extreme event built environment

system recovery for decisions makers to conduct operative disaster planning and municipal resource

allocation.


Page 32 of 84

24. An integrated simulation and optimization based residential construction carbon

footprint and emission assessment

Hong Xian Li ([email protected]), Advisor: Dr. Mohamed Al-Hussein, Dr. Mustafa Gül


Winter heating on residential construction sites consumes a considerable amount of energy and emits

a significant volume of greenhouse gases (GHGs) in cold regions—space heaters run continually in

winter months of construction. Taking panelized residential house construction for instance, it has

been reported that on-site winter heating accounts for as much as 34% of carbon emissions from the

framing phase. The objective of this research includes the following: (1) Quantify carbon emissions

from on-site construction, including emissions from the construction process and winter heating. A

combined discrete-event simulation (DES) and continuous simulation is employed as the quantification

approach, based on panelized construction as the construction technology. (2) Propose plans to

reduce carbon emissions from on-site construction in cold regions. Based on the simulation model,

crew sizes for labour-intensive activities are analyzed and optimized with respect to carbon emissions

from construction in cold regions. A discrete-event simulation (DES) platform, Simphony.NET, is

employed to simulate the on-site construction process, while the impact of weather on winter heating

is considered using the continuous simulation template recently developed at the University of Alberta.

Based on the construction process simulation, a genetic algorithm (GA) is employed as the

optimization approach, i.e., integrating the simulation and optimization models. The integrated

simulation and optimization methodology is illustrated using a case example in Edmonton, Alberta,

Canada, with on-site construction activities commencing at intervals corresponding to the four seasons

considered and compared. The results demonstrate that: (1) the combination of DES and continuous

simulation is an appropriate approach, capable of automatically simulating the construction process

as well as winter heating; (2) by optimizing on-site crew size, the carbon emissions of construction are

reduced significantly (a margin of 4.29 kg CO2/m2 is identified in this research); (3) the effect of

optimization depends on the construction start date. This research thus proposes a generic

methodology by which to measure and minimize the carbon emissions from on-site construction in

cold regions. This research proposes a generic methodology of carbon footprint and emissions

assessment with integrated simulation and optimization for residential construction.



Page 33 of 84

25. 3D Reconstruction of Industrial Equipment Using Combined Geometric and

Topological Information from Laser-Scanned Data

Hyojoo Son ([email protected]), Advisor: Dr. Changwan Kim

Chung-Ang University

Industrial equipment plays a vital role in operative procedures as a basic part of industrial facilities. In

the operation and maintenance phase of these facilities, it is important to ensure that the as-built

condition of each installed piece of equipment and any changes that may subsequently occur are

properly recorded and adjusted for the reference of the engineers and managers in the interpretation

process. Recently, 3D measurements of industrial facilities’ as-built conditions have been done

efficiently by using terrestrial laser scanners. The resulting detailed and often complex set of 3D point

clouds is then processed to manually create digitalized as-built models of the industrial equipment.

However, the manual creation of industrial equipment models from an acquired set of 3D point clouds

is difficult. This paper proposes a framework for an automated approach to the reconstruction of 3D

models of industrial equipment from terrestrial laser-scanned data. The proposed approach consists

of four main steps: the extraction of sets of 3D point clouds that constitute equipment and adjacent

pipelines; the representation of geometric and topological information from the extracted 3D point

cloud sets, the existing piping and instrumentation diagrams (P&ID), and the 3D database; matching

the representations from the P&ID and the 3D database to the representations from the 3D point cloud

sets; and the registration of the selected model to the 3D point cloud set. Experimental evaluation

demonstrates that the proposed approach overcomes the major limitations that arise when industrial

equipment is treated as primitives and when only geometric characteristics are considered in the

matching and retrieving step. The proposed approach could be successfully utilized to reconstruct as-

built industrial equipment during the operation and maintenance of industrial facilities. During this

process, furthermore, all object models were tagged with their information predefined in the P&ID.

Adding such information to 3D models is beneficial for many analytical and managerial purposes.

Future work will be devoted to experimentation on domains with more complex scenes, as well as a

wider range of processes for practical use purposes.



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26. Information Extraction and Automated Reasoning for Automated Regulatory

Compliance Checking in the Construction Domain

Jiansong Zhang ([email protected]), Advisor: Dr. Nora El-Gohary


Manual checking of construction regulatory compliance is time-consuming, costly, and error-prone.

Automating the process of compliance checking is expected to reduce the time and cost of the process,

as well as reduce the probability of making compliance assessment errors. Previous research and

development efforts in automated compliance checking (ACC) have paved the way, but the current

extraction and encoding of regulatory and project information into computer-processable format still

require much manual effort and there lacks the level of knowledge representation and reasoning that

is needed for compliance analysis and checking.

Develop methodologies/algorithms and corresponding knowledge representations that

automatically extract regulatory and project information from textual regulatory documents and BIM

models, respectively, encode the information in a semantic format, and automatically reason about

the information for compliance assessment.

Utilize a theoretically-based, empirically-driven methodology that leverages knowledge and

techniques in natural language processing (NLP), semantic modeling and automated reasoning to

develop methodologies/algorithms and corresponding knowledge representations for automated

information extraction (IE), automated information transformation (ITr), and automated compliance

reasoning (CR) for regulatory information and project information in the construction domain.

Preliminary Results: a) Hybrid syntactic-semantic rule-based methodology and algorithms for

automated IE from construction regulatory documents; b) Semantic rule-based methodology and

algorithms for ITr; c) Logic-based methodology and algorithms for CR.

Scientific Contributions and Practical Significance:

Offer efficient-to-develop, semantic rule-based NLP methods for IE and ITr that can help

capture domain-specific meaning;

Combine domain knowledge and NLP knowledge to achieve deep NLP;

Pioneer work in utilizing semantically deep NLP approach in the construction domain;

Provide benchmark semantic methods/algorithms for IE and ITr in the construction domain;

Offer a new mechanism (“consume and generate” mechanism) for processing and

transforming complex regulatory text into logic clauses;

Pioneer work in utilizing logic programming (LP) for the automated reasoning functionality in

ACC in the construction domain;

Reduce the time and cost of compliance checking in the construction domain;

Improve the accuracy of compliance checking;

Support other applications of automated information processing in the construction domain.



Page 35 of 84

27. Ex-ante Assessment of Performance in Construction Projects: A System-of-Systems

Approach

Jin Zhu ([email protected]), Advisor: Dr. Ali Mostafavi

Florida International University

Early and accurate assessment of performance is critical in successful delivery of complex

construction projects. Construction projects are complex Systems-of-Systems (SoS) consisting of

different interconnected networks of processes, activities, human agents, resources, and information.

The interconnections between different constituents in construction projects give rise to project-level

emergent properties that affect the ability of project organizations to cope with uncertainties, and thus,

influence project performance. Hence, better assessment of performance in construction projects

requires integrated analysis of the existing emergent properties, dynamic behaviors, complexities, and

uncertainties.

The overarching objectives of this research were to: (1) create and test an integrated

framework for bottom-up assessment of performance in construction projects using a SoS approach,

and (2) explore the emergent properties affecting the ability of project organizations to cope with

uncertainties.

The study created and tested an integrated framework using a SoS approach in which

construction projects were evaluated across four levels of analysis: base, activity, process, and project

levels. The outcomes of each level of analysis were obtained by aggregating the dynamic interactions

at the levels below. The abstraction and simulation of the dynamics of construction projects were made

at the base level, where the interconnections between the dynamic behaviors of the players,

information requirements, and resource utilization were investigated. The application of the framework

was demonstrated in a case study related to a tunneling project. A hybrid agent-based/system

dynamics model was created and validated and was then used in conducting Monte-Carlo

experimentations to investigate the impacts of the micro-level behaviors on the macro-level

performance patterns.

The results highlight the significance of incorporating the dynamic behaviors of human agents

as well as information processing in modeling performance in construction projects. In addition, the

results reveal the highly likely scenarios for enhancing the ability of project organizations to cope with

uncertainties through streamlining information processing and modifying dynamic behaviors of human

agents. The results also show that the ability of project organizations to cope with uncertainties could

be captured based on three emergent properties at the project level: absorptive capacity, adaptive

capacity, and restorative capacity. These emergent properties could be used as leading indicators for

ex-ante evaluation of project success.

This distinctive approach is the first of its kind to simulate the performance measures at the

project level based on the micro-behaviors of project constituents at the base level. The proposed

framework provides an integrated approach for investigating the performance of construction projects

at the interface between the dynamic behaviors of players, uncertainties, and complexities. Hence, it

could provide a robust basis towards creation of integrated theories of performance assessment in

construction projects. From a practical perspective, the framework and emergent properties could be

used in developing leading indicators for predictive assessment and proactive monitoring of

performance in construction projects.



Page 36 of 84

28. A Framework for Public Private Partnership Risk Mitigation in Rural Post Conflict

Environments– A Systems Approach

John T. Mitchell ([email protected]), Advisor: Dr. Yvan Beliveau

Virginia Tech

Post conflict situations regardless of locale have a sense of fragility. High social and political tension

combined with devastated infrastructure make hopes for redevelopment tenuous. Urban areas are

usually the point of focus for redevelopment action as their populations are typically inflated from the

conflict. Ordinary rural lifestyles are forced to cope with now crowded conditions in locations where

original infrastructure provisions were possibly stressed, and damaged ones much less suitable.

This doctoral research focuses on the development, design, and implementation framework to

reduce risk and increase participation in Public Private Partnerships to deliver key rural infrastructure

and generate employment and business opportunities. The research methodology is based on a multi-

sector integrated systems design framework that is comprised of: 1) sustainable energy system

component; 2) education component; and 3) a local existing economic generator. The introduction of

off-grid sustainable energy in combination with targeted education could stimulate growth of an

existing economic enterprise with viable jobs and business opportunities.

Non- conflict zone governments in resource rich locations of Africa are also challenged to

provide revenues to rural infrastructure developments much less those of post- conflict situations.

Public Private Partnership (PPP) is an ideal delivery mechanism for such investment because of its

long term contract requirement. Based on relational contracting philosophy PPP could optimize the

utilization of all stakeholders’ knowledge for infrastructure provision and efficient and effective long

term operation and income generation.

In many instances the failure of PPP is the result of under-utilization of facility output and or

inability to pay for service. The delicate nature of Post conflict Governments exacerbates the situation

and reduces funding access. It is with this understanding that the framework proposes infrastructure

implementation that will generate alternative business development and local job creation as a priority

thereby facilitating the ability to pay for service. The creation of additional business opportunities

within the construct of the PPP allows the investors to spread their investment risk across other income

generating areas. This formula is a typical investment risk mitigation strategy and in this case

depending upon the viable enterprises identified has the potential of increasing the investment

participant pool and knowledge availability, and maximizing market and customer access.

The country of Somalia is used as a base case for testing the framework. Crops, forestry, and

forage have been examined to identify constraints to livestock and farm productivity, impacts on food

security and opportunities for local economic development. A systems approach links power provision

with the required educational training and food processing systems to provide and maintain a sterile

food processing environment. This framework is to be reviewed by two separate panels of experts in

international development and infrastructure via questionnaire and semi-structured interviews from

which the preliminary results will facilitate the refinement of the framework.

The linking of biomass production and rural off-grid electrification models in Somalia is the first

attempt to enable local communities to improve environment and ecology, increase farm and livestock

productivity and produce adequate power to ensure safe food production and storage. The research

identifies possible approaches to biomass based electrical output utilizing micro turbines in

combination with gasifiers or digesters for direct electricity production, and the use of auxiliary heat in

the combined heat and power (CHP) cycle to purify and supply high temperature water to reduce food

borne pathogens so that safe food supplies can be ensured. The energy technology in combination

with required educational training will also enable local communities to safely process and store food

products that meet standards of export markets and thereby increase their business and job creation

potential.



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29. Framework for On-Site Biomechanical Analysis During Construction Tasks

JoonOh Seo ([email protected]), Advisor: Dr. SangHyun Lee


In the labor-intensive construction industry, workers are frequently exposed to manual handling tasks

involving forceful exertions and awkward postures. As a result, construction workers are at about a 50

percent higher risk of work-related musculoskeletal disorders (WMSDs) than workers in other

industries. One of the methods for assessing workers’ exposure to the risk factors of WMSDs is a

biomechanical analysis that estimates musculoskeletal stresses (e.g., corresponding joint moments

and muscle forces) as a function of human motion (e.g., postures and movements) and external forces

(i.e., exerted forces on hands and feet). Biomechanical analysis enables us to identify hazardous tasks

by comparing musculoskeletal stresses with human physical capability (i.e., strength). In previous

studies, however, the motions and forces required for biomechanical analysis have been measured

using complex motion capture (e.g., marker-based motion capture systems) and force measurement

systems (e.g., force transducers) in controlled environments. Therefore, it is difficult to consider

possible variations of construction tasks that exist under real conditions.

To address this issue, we propose a framework for on-site biomechanical analysis for

construction manual tasks by integrating markerless vision-based motion capture approaches and

motion-driven force estimation that enable us to collect motion and force data required for

biomechanical analysis under real conditions. First, the proposed framework extracts skeleton-based

motion data by tracking body parts and joints in 2D images from ordinary video cameras, or in 3D

images from an RGB-D sensor. Next, data about the forces exerted on hands and feet to perform

certain tasks is predicted according to types of tasks. For example, for lifting tasks, hand loads and

foot forces can be estimated based on the weight of an object lifted and the body weight. For more

complex tasks, such as ladder climbing—in which external forces are dynamically changing—we

propose force prediction models. For example, patterns of external forces during ladder climbing have

a significant relationship with individual (e.g., workers’ climbing styles) and physical (e.g., ladder slant)

factors, and thus the external forces can be estimated by identifying their quantitative relationships

and by modeling force patterns under dominant factors. Finally, the proposed framework performs a

biomechanical analysis using motion and force data from previous steps to identify excessive

musculoskeletal stresses beyond human capability. For the biomechanical analysis, computerized

biomechanical analysis tools such as 3DSSPPTM and OpenSim are used. As motion data from

markerless vision-based approaches is not directly available to existing tools, we also propose

automated processes to convert the motion data into available data in biomechanical analysis tools.

To test the feasibility of the proposed framework, we conducted case studies on the masonry

work and on a ladder climbing activity. The results from the case studies showed that the proposed

framework can be successfully used to perform biomechanical analyses on diverse tasks by showing

similar results from previous experimental biomechanical studies. The proposed framework has great

potential to broaden our understanding of the causes of WMSDs by estimating musculoskeletal

stresses on the human body during construction manual tasks without any invasive measures. Thus,

it can identify potentially hazardous construction tasks, contributing to minimizing the risks of WMSDs

by providing behavioral feedback to workers or by redesigning work processes and environments that

may cause excessive musculoskeletal stresses.



Page 38 of 84

30. Develop a Price Escalation Method for Single Award Indefinite Delivery/Indefinite

Quantity Contracts: AxE Bidding

Jorge A. Rueda ([email protected]), Advisor: Dr. Douglas D. Gransberg

Iowa State University

As a result of a comprehensive research conducted for the Minnesota Department of Transportation

(MnDOT) on the current Indefinite Delivery/Indefinite Quantity (IDIQ) practices adopted by different

transportation agencies across the US, the research team has identified a major issue to be addressed

before MnDOT can fully implement IDIQ contracting: cost escalation in multi-year single award IDIQ

contracts. This study introduces a new escalation methodology and terms it: Cost times Escalation

(AxE) bidding. It seeks to eliminate the need to depend on external construction cost indices or to

develop a MnDOT construction cost index by shifting the escalation risk to the contractor during

bidding and allowing it to propose its own escalation adjustment factor. The proposed process requires

competing contractors to submit a fixed annual adjustment rate, which will be used to modify bid unit

prices over time throughout the IDIQ contract’s life cycle. The adjustment rate is also factored into the

selection of the low bid in a manner similar to A+B bidding. This study evaluates different alternatives

to incorporate this rate into the selection of the successful contractor (formulas for E) and quantifies

the risk related to each alternative for different case scenarios. Additionally, AxE bidding is expected

to reduce construction costs and agency staffing requirements, as well as overcome some

disadvantages associated with using traditional price escalation methods, such as the lack of flexibility

to adapt to the nature of the contract and the inability to consider imminent future changes in the

construction industry. This study also presents an analysis of traditional price escalation methods by

applying twelve different cost escalation indexes, and one alternative method currently used by

MnDOT on its IDIQ contracts, on four case study projects over a five-year period. Outcomes from each

index were compared with observed bid prices along the same period of time. A complete analysis of

these traditional price escalation methods and historical bid data were used by the authors as a

reference to develop an escalation method that meets MnDOT needs.



Page 39 of 84

31. Construction Operations Automation using Modified Discrete Event Simulation Models

Joseph Louis ([email protected]), Advisor: Dr. Phillip S. Dunston

Purdue University

Although the automation of construction tasks has been an active research topic since the 1970s, few

actual robots can be found on the worksite today and the fundamental construction process has

remained unchanged since the pre-industrial era. On the other hand, the planning and design stages

of construction projects has benefited greatly from the advances made in simulation and modeling

technologies for construction operations and products. In this research, a novel mechanism for

leveraging information-rich simulation models to automate construction operations is developed. The

methodology presented uses Discrete Event Simulation (DES) models of operations to drive the

process in the real world using autonomous robots. The following specific research questions will be

addressed in this research to enable the automation of operations: (1) What modifications are required

to enable DES models, traditionally used only to analyze operations, to serve as control mechanisms

to orchestrate autonomous equipment and thus enable operation automation? (2) How can the DES

models, modified for operations control, be verified and validated before their use in the real world?

(3) How can the modified DES models be used to automate any construction operation, regardless of

scale and complexity? Technically, the need for a rethinking of traditional DES models is necessitated

by the fact that the durations of activities will not be known a-priori when the model is controlling a real

world operation in real time. The state of the art in construction simulation, visualization, and

automation serves as the foundation to answer the questions that pave the path towards the

overarching goal of construction automation. An initial demonstration of the proposed framework’s

feasibility was performed using modified RC models of construction equipment. A discussion of the

results and conclusions of this preliminary experiment are provided in the poster.

The primary contribution of the proposed research is the use of DES models in an

unprecedented manner to enable the construction automation. This approach would allow for the

automation of any construction operation, given the application breadth of DES models that allow it to

faithfully represent almost any construction process. Another significant advancement that the

proposed methodology enables is the possibility of remote construction in extraterrestrial, underwater,

and other hazardous environments, which is currently impossible without the presence of human

operatives. The proposed methodology, while presented in the construction context, has important

implications for the automation of any industry characterized by a less-structured and high-uncertainty

environment, wherein tasks are subject to complex interaction between disparate resources, such as

agriculture. Apart from the benefits of automation described above, the proposed research has

immediate applicability for the monitoring and control of conventional construction sites, i.e. without

the presence of automated equipment. This research also enables new visualization techniques at the

operation/ process level in field construction with the use of robot simulators. The use of reusable CAD

models in robot simulators encapsulated with their performance data and functionality would free up

the operation modeler from low level details about equipment and from the collection of activity

duration data and geometric data about the work site.


Page 40 of 84

32. Autonomous Near-Miss Fall Accident Detection Technique Using Inertial Measurement

Units on Construction Iron-Workers

Kanghyeok Yang ([email protected]) and Sepideh S. Aria ([email protected]),

Advisor: Dr. Changbum Ahn

University of Nebraska at Lincoln

In the construction industry, fall accidents are one of the leading causes of fatal and non-fatal

occupational injuries. Previous research has focused on the qualitative assessment of fall accident

risk or on the investigation of a lagging indicator to acquire a better understanding of construction fall

accidents. However, an investigation of potential fall accidents is still challenging due to the scarcity

of relevant data and of appropriate investigation approaches. These circumstances limit proactive fall

accident prevention efforts. One promising technique for investigating possible accidents is the

implementation of a near-miss accident reporting system. However, previous near-miss reporting

systems are based upon workers’ retrospective and qualitative self-reporting rather than

autonomously measured quantitative data.

The objective of this research is to introduce an autonomous near-miss fall accident detection

technique that employs wearable Inertial Measurement Units (IMU) to document the stability (i.e.,

degree of loosing balance) of construction iron-workers.

Considering iron-workers’ diverse postures and positions on steel beams during their work,

the first step in detecting near-miss fall accidents in an actual worksite requires being able to accurately

classify postures and movements to determine workers’ stability conditions. Towards this end, this

research examined the near-miss fall detection technique for workers’ walking activity. In this research,

two experiments were conducted using video data and a worker’s IMU data—in order to acquire

worker’s body-movement and stability data, an IMU was applied to the iron-worker’s sacrum to record

movements and postures. In the initial laboratory experiment, two different activities (i.e., diverse

postures and walking) were performed on a rectangular steel I-beam frame (12’ x 6’) for posture

classification and near-miss fall detection. Subsequently, a second experiment translated only the

near-miss fall detection methodology into a site-level experiment to verify the technique’s potential for

implementation in an actual construction site. In the data processing stage, this research extracted 38

features (i.e., Mean, Standard Deviation, Max, Correlation, Spectral Entropy, and Spectral Centroid)

from the raw IMU data. Based upon these features, this research implemented machine learning

techniques (i.e., a Support Vector Machine and Laplacian-Support Vector Machine) for

posture/movement classification and near-miss accident detection.

Through the experiments and the machine learning techniques, five different worker’s postures

could be classified. In the laboratory experiment, posture classification using the support vector

machine had over 95% overall accuracy. For near-miss fall detection the Laplacian support vector

machine managed 98% accuracy in detecting near-miss accidents during walking motions. As would

be expected considering worker’s expertise, only a small number of near-miss accidents could be

placed during the site experiment. Through the same near-miss fall detection technique, a 99%

accuracy was achieved in the site-level experiment data.

The near-miss fall accident detection technique introduced in this research will help to identify

potential fall accidents at an individual level and inform those at risk. By aggregating individual data,

this technique could also facilitate the locating of dangerous spots in a worksite, thereby empowering

workers to install additional fall protection measures. Moreover, autonomous detection of near-miss

accidents will enhance the overall understanding of fall accident in construction.


Page 41 of 84

33. Managing Water and Wastewater Infrastructure in Shrinking Cities

Kasey Faust ([email protected]), Advisor: Dr. Dulcy Abraham

Purdue University

The research presented in this poster highlights select water and wastewater infrastructure

management challenges in shrinking cities, proposes management options to address these

challenges, and evaluates the feasibility of a decommissioning, one of the management options. Major

economic downturns in once vibrant industrial cities have resulted in the loss of significant populations

and tax bases. This phenomenon, termed as shrinking cities, introduces enormous challenges to

managing major infrastructure systems. When cities experience catastrophic economic conditions,

consequentially causing extreme population loss, maintaining critical infrastructure at original levels of

operation becomes unsustainable. While this study focuses on water and wastewater infrastructure,

other forms of infrastructure (e.g., power and gas utilities) are equally susceptible.

A key challenge affecting shrinking cities is the fixed costs of operations (approximately 75-80

percent of total cost) in spite of significantly declining populations and tax bases. As population

continues to decline, the per capita cost for service increases. Decommissioning the excess,

underutilized water infrastructure has the potential to reduce or stabilize these per capita service costs.

A separate challenge impacting the wastewater system is reducing the quantity of runoff entering the

combined sewer systems that are present in many shrinking cities. The quantity runoff may be reduced

through decommissioning impervious surfaces and allowing the water to infiltrate the ground. Cities

experiencing drastic urban shrinkage have the potential to shift land uses and selectively transition

excess land from impervious to pervious surfaces to aid in meeting local, state, and federal regulations.

The viability of decommissioning infrastructure components is examined for water infrastructure and

wastewater infrastructure. EPANET, SWMM, L-THIA, and GIS are the primary tools used for analyses.

A network analysis was performed using EPANET to examine how altering the topology of and

changing demands within the network impacts the system’s performance using the metrics of

adequate system pressures and fire flow capabilities. SWMM and L-THIA were used to evaluate the

impact of decommissioning surfaces that contribute to runoff based on the percentage change in runoff

from the status quo. The results yielded in SWMM and L-THIA were compared to estimate the

differences in runoff quantities across different tools and assumptions. The metrics used and

comparison of two tools allows for insight into decommissioning implications on system performance.

Data incorporated in the models are gathered from city GIS databases and published literature. One

Midwestern shrinking city that has experienced a loss of more than 40% of its population is used to

demonstrate the feasibility of implementing the alternatives. The models were verified and validated

by subject matter experts from Indiana and Michigan with backgrounds on issues inherent to shrinking

cities, and water and wastewater infrastructure management or modeling experience.

The results of the feasibility analysis for decommissioning water distribution pipelines and

impervious surfaces illustrate the viability of proactively managing infrastructure, while providing

adequate service levels, assisting in meeting regulations, improving aesthetics of the city, and

potentially reducing or stabilizing service costs. By identifying issues inherent to shrinking cities and

management options, city officials and decision makers are provided with insight that can be used to

ensure effective and efficient long-term operation and management of water and wastewater

infrastructure. Examining this new paradigm within cities of urban decline, aids in moving towards

flexible infrastructure planning to accommodate future population trends, whether shrinking, static, or

growing.



Page 42 of 84

34. Monitoring Construction Progress at the Operation-Level using 4D BIM and Site

Photologs

Kevin K. Han ([email protected]), Advisor: Dr. Mani Golparvar-Fard


Measuring construction progress is an important indicator for project control. It provides practitioners

with the information they need to easily and quickly detect performance deviations and decide on

control actions that can avoid them or minimize their impacts. Nevertheless, current practices are

costly, prone to error, and performed intermittently. To address these limitations, research has focused

on creating methods based on laser scanning, image-based 3D reconstruction, or time-lapse

photography together with Building Information Modeling (BIM). The common challenges in these

emerging methods are 1) detecting operation-level progress in presence of static and dynamic

occlusions which produce both missing or incomplete data and challenge reasoning about progress

under limited visibility; 2) low level of development (LOD) in BIM where many elements only have a

one-to-many relationship with the schedule activities; e.g., a 3D element corresponds to both

"placement" and "waterproofing" activities; and 3) high-level Work Breakdown Structure in construction

schedules where the operational details are missing.

The over-arching objective of this work is to leverage 4D BIM and 3D point cloud models and

create a new method for monitoring operation-level construction progress based on appearance-

based classification of construction materials and formalized sequence of construction activities.

A new appearance-based method for classification of construction material and inference of

operation details and progress deviations is presented. The method leverages 4D BIM and 3D point

cloud models generated from site photologs using Structure-from-Motion and Multi-View Stereo

algorithms. In the developed system, the user manually superimposes these 3D models by assigning

correspondences, and allows the photos and the 4D BIM to be automatically brought into alignment

from all camera viewpoints. Through reasoning about occlusion, each BIM element is back-projected

on all images that see that element. From these back-projections, several 2D patches per image are

sampled and are classified into different material types. The image patches per element are then fed

into a material classification machine learning based model and a quantized histogram of the observed

material types is formed per element. The material type with the highest appearance frequency infers

the state of progress. Based on spectrum of colors, the BIM elements – queried from a web database–

are color-coded on a web-based platform enabling all project participants to be informed about the

most updated state of the ongoing activities.

For training material classification model, a new database containing 20 typical construction

materials with more than 150 images per category was assembled and an average accuracy of 91%

for 30×30 pixel image patches was reported. For progress monitoring, we assembled datasets of site

imagery and BIM for two real-world under construction concrete structures. Our experiments shows

that even in presence of static and dynamic occlusions, inaccuracies in sampling due to BIM-vs.-point

cloud registration and presence or edges and corners as artifacts, our method is able to maximize

visibility of elements by sampling a large number of image patches from different perspectives and

correctly detect and report progress on all concrete elements. Our findings shows that it is feasible to

sample and detect construction materials from images that are registered to point clouds and use that

to infer the state of progress for BIM elements. The contributions are two-fold: First, a model-based

reasoning method for operation level assessment of construction progress. This method leverages 4D

BIM and image-based 3D point clouds and infers the state of progress using an image-based material

classification method. Second, a publicly available dataset of incomplete and noisy point cloud models

assembled from construction site images and BIM with different levels of detail for future analysis and

comparison of image-based progress monitoring methods. This work has potential to enable project-

by-project learning and improves basis of design and construction planning, as well as project control.



Page 43 of 84

35. Estimating Optimal Labor Productivity: A Two-Prong Strategy

Krishna Kisi ([email protected]) and Nirajan Mani ([email protected]), Advisor:

Dr. Eddy Rojas

University of Nebraska-Lincoln

In an attempt to evaluate the efficiency of labor-intensive construction operations, project managers

compare actual with historical productivity for equivalent operations. However, this approach towards

examining productivity only provides a relative benchmark for efficiency and may lead to the

characterization of operations as authentically efficient when in reality such operations may be only

comparably efficient.

Optimal labor productivity ⎯the highest sustainable productivity achievable in the field under

good management and typical field conditions⎯ can provide an absolute benchmark for gauging

performance. This optimal labor productivity level is lower than the productivity frontier ⎯the theoretical

maximum achieved under perfect conditions⎯ because of system inefficiencies, which emerge due to

factors outside the control/influence of project managers. Conversely, optimal labor productivity tends

to be higher than actual labor productivity⎯ the productivity achieved in the field ⎯because of

operational inefficiencies, which are the result of suboptimal managerial strategies. Estimating the

magnitude of these system and operational inefficiencies will help project managers determine optimal

labor productivity.

This study develops a two-prong strategy for estimating optimal labor productivity. The first

prong represents a top-down approach that estimates optimal labor productivity by introducing system

inefficiencies into the productivity frontier. A Qualitative Factor Model is used to determine the impact

of system inefficiencies. This top-down approach yields the upper level estimation of optimal labor

productivity. The second prong is a bottom-up approach that determines optimal labor productivity by

removing non-contributory work from actual productivity in a discrete event simulation. This bottom-

up approach generates the lower level estimation of optimal labor productivity. An average of the upper

and lower limits reveals the best estimate for optimal labor productivity. The proposed two-prong

strategy for estimating optimal labor productivity was successfully applied in a simple electrical

installation project. The study analyzed actions performed by a veteran worker in “Fluorescent Bulb

Replacement” task. The Qualitative Factor Model estimated 2.15 stations per hour as loss due to

system inefficiencies. The discrete event simulation was found effective at modeling operational

inefficiency. The loss due to operational inefficiencies estimated 0.96 stations per hour. The

productivity frontier computed from this pilot study for the task was 20.23 stations per hour. Finally,

from these data, the estimated value of optimal productivity was determined to be 15.92 stations per

hour. Given that actual productivity was measured at 12.80 stations per hour, the task “Fluorescent

Bulb Replacement” achieved an efficiency of 80%. Therefore, this pilot study demonstrates that the

proposed methodology for estimating optimal labor productivity is adequate when applied to a simple

electrical operation.

This study contributes to the body of knowledge in construction engineering and management

by introducing a two-prong strategy for estimating optimal labor productivity in labor-intensive

construction operations and reporting on a pilot study performed to evaluate its feasibility using a

simple electrical installation. Accurate estimation of optimal productivity allows project managers to

determine the absolute (unbiased) efficiency of their labor-intensive construction operations by

comparing actual vs. optimal rather than actual vs. historical productivity.




Page 44 of 84

36. An Investigation of Occupant Energy Use Behavior and Interventions in a Residential

Context

Kyle Anderson ([email protected]), Advisor: Dr. SangHyun Lee


Across the globe, wide-scale efforts are being made to reduce energy consumption and carbon dioxide

emissions. To date, innumerable efforts have focused on technological approaches to reduce the energy

consumption of the building stock (e.g., installing efficient appliances); however, it is critical to remember

that, in the end, all buildings are operated by humans, and how humans decide to behave in them has a

significant and meaningful effect on their energy demand. Unfortunately, it is still not well understood how

occupants choose to behave in buildings, what contributes to their behavior decisions, and what methods

are effective at promoting lasting improvements in occupant energy use behavior. Therefore, the objectives

of this research are as follows: 1) explore occupant behavior patterns to identify opportunities for

improvement and barriers to change, 2) enhance our understanding of contextual factors that influence

occupant energy use decisions, and 3) test the durability of novel behavior intervention strategies. In order

to achieve these objectives, we have developed a multipronged research framework that employs computer

modeling techniques with longitudinal field experiments and data collection. To better understand how

occupants consume energy and the potential for behavior improvement, we collected hourly occupancy

and energy use data of seven dormitory buildings, housing over 1300 rooms, for a year. This study used

field-collected data to provide a first look into the amount of energy waste (energy used during periods of

vacancy) that occurs in residential buildings. It was found that, annually, 21.5% of all energy consumed

was spent during periods of non-occupancy. This is significant, not just due to its vast magnitude, but

because it represents the amount of energy that can be saved by simple improvements in behavior without

occupants having to forgo their comfort (e.g., lower heating set points). Further, it was found that no

meaningful relationship exists between total energy consumption and the percentage of energy that was

wasted in the room. High- and low-energy users alike wasted energy in proportion to their total consumption.

These findings have significant practical implications as they imply that interventions can be designed to

be highly non-particular—which places fewer demands on interveners—and still prove very effective given

that energy waste is proportional across residents. Although this suggests that non-particular interventions

can be very effective, applying interventions can still require significant effort, time, and expense. In addition,

it is difficult to predict how effective a given intervention will be in one setting versus another, as contextual

factors may be different. Therefore, researchers have begun developing computer models to simulate and

analyze potential intervention outcomes to improve our understanding of how complex systems (e.g., social

networks) can affect intervention success. In the previous literature, studies have just begun to explore the

impact of network structure on the diffusion of social norms, and have evaluated only social network effects

using limited social network structures and static social networks that are far from reality. Thus, to bridge

this gap we evaluated how applying normative (i.e., social comparison–based) behavior interventions are

affected by different social network structures and evolution using agent-based modeling. Results indicate

that social network structure has a significant effect on the amount of time that interventions require to take

effect and to reach steady state behavior, and on the variance in potential outcomes. Further, static social

networks are much less volatile in their behavior and tend to have more convergent behavior relative to

dynamic social networks which have greater amounts of grouping of behavior. This is a critical because,

when proposing new interventions to facility or property managers, high levels of uncertainty in intervention

outcome success, or failure, can provide a significant barrier to implementation. Findings from the proposed

research contribute both in a practical and theoretical manner. First, the results from the studies add to our

knowledge of pro-environmental behavior interventions. In addition, they enhance our understanding of

how occupants behave in buildings and how contextual factors affect the spread of culture and behaviors.

Second, the developed models help to advance the state-of-the-art simulations of behavior interventions,

and provide interveners with new tools in determining which courses of action should be taken to meet

sustainability targets.


Page 45 of 84

37. Measuring the Complexity of Mega Construction Projects in China—a Fuzzy

Analytic Network Process

Lan Luo, Advisor: Dr. Qinghua He

Tongji University

The number of mega construction projects in China has considerably increased in the past decades.

These projects are usually very complicated in nature, and understanding these complexities is critical

to the success of these megaprojects. However, empirical studies related to the measurement of the

complexity of megaprojects remain lacking. This paper reports the development of a complexity

measurement model based on the Shanghai Expo construction project in China using the fuzzy

analytic network process (FANP). First, a complexity measurement model consisting of 28 factors that

are grouped under six categories, namely, technological complexity, organizational complexity, goal

complexity, environmental complexity, cultural complexity, and informational complexity, is identified

through the literature review and content analysis. Then, the model is refined by a two-round Delphi

survey conducted in the aforementioned megaproject. Finally, a refined model, combined with

suggestions for its application, is provided based on the survey results. The complexity measurement

model based on the FANP can be used to determine the level of complexity of mega construction

projects. These findings are believed to be beneficial for scholars and may serve as reference for

professionals in managing megaprojects in China.


Page 46 of 84

38. Decision Support System for Sustainable Labor Management in Masonry Construction

Laura Florez ([email protected]), Advisor: Dr. Daniel Castro-Lacouture


Masonry construction is labor-intensive. Its operations involve little to no mechanization and require a

large number of crews made up of workers with diverse skills. Relationships between crews are tight

and very dependent. Often tasks have to be completed concurrently and crews have to share

resources and work space to complete their work. One of the problems masonry contractors face is

the need to design crews, that is, determine the number of crews and the composition of each crew

to be effectively used in the construction process to maximize workflow. This study proposes the

framework (see Figure 1) for a decision support system to assist contractors in the allocation of crews

in masonry projects. The proposed system can be a valuable tool to assist masonry contractors in the

process of allocating workers while meeting worker’s needs and expectations.

Figure 1. Optimization model framework

Optimization module

Quasi-ethnographic observations

Optimization procedure

Reporting module

Project Gantt chart

Laborconsumption

Workers' allocation

Masonry practices

Planning horizon

Workers' needs

Availability of resources

Workers' parameters:- Skills- Costs

- Production rate

Data input module

Contractor's

requirements-Rule 1 to Rule 7



Page 47 of 84

39. BIM-based Integrated Approach for Optimized Construction Scheduling under

Resource Constraints

Hexu Liu ([email protected]), Advisor: Dr. Mohamed Al-Hussein, Dr. Ming Lu


Building Information Modeling (BIM) has been recognized as a potential information technology with

the potential to markedly change the Architecture, Engineering, and Construction (AEC) industry, and

drew has drawn much attention from numerous scholars within the construction domain. Despite the

reported advancements pertaining to BIM in previous studies, the extended use of BIM in automated

construction scheduling has not yet reached its potential. In current practice, BIM in most cases

functions as a database of 3D building components and provides only limited information (e.g., quantity

take-offs) of each component for the downstream scheduling analysis, and rich building information

embedded in BIM is not being utilized in order to facilitate the automatic generation of project

schedules, entailing that massive manual work is required in the process of BIM-based project

scheduling, especially in information exchanges between BIM modeling tools and scheduling tools.

Any change of the project design or scope can lead to re-planning, an automated system is thus

required to improve project planning efficiency. This research proposes BIM-based integrated

scheduling approach which facilitates the automatic generation of optimized and detailed activity-level

construction schedules for building projects under resource constraints, by achieving an in-depth

integration of BIM product models with work package information, process simulations, and

optimization algorithms. The integration is realized through the enriched information entity which

extracts rich building product information of building components from BIM and WBS information from

MS Access, and moves through the process simulation model in accordance with that enriched

information, thereby yielding construction schedules. Meanwhile, evolutionary algorithms, such as

particle swarm optimization, are also incorporated into the methodology to optimize the construction

sequences with respect to the specified objective (e.g., minimum project duration). To implement the

proposed methodology, a prototype system for scheduling panelized building projects is developed as

an add-on tool for Autodesk Revit, and further is demonstrated by a case study. Building on the existing

body of research in this field, the key contribution of this research is the integration of BIM product

model with work package information, process simulations, and an optimization model, which provides

solutions addressing the challenges of the existing practice with respect to automated scheduling of

construction projects.



Page 48 of 84

40. Increasing mindfulness of coordination practices in inner city utility projects: the role

of new (BIM) technologies

Léon L. olde Scholtenhuis ([email protected]), Advisor: Dr. T. Hartmann

University of Twente

Subsurface assets are nowadays owned, operated and reconstructed by distinctive organizations.

This challenges the alignment of the many service providers (clients) and contractors involved in inner

city subsurface reconstruction works. Although stakeholders negotiate and try to communicate while

manually aligning their plans in meetings, absence of coordination hierarchy complicates these

processes. Oftentimes, structures are missing to scope, explicate and formalize; and integrate plans

effectively, resulting in suboptimal stakeholder alignment and error prone scheduling processes.

To eventually enhance stakeholder alignment, this qualitative research evaluates how (BIM)

technologies support practitioners’ focus on potential errors and holdups. To this end, we borrow the

mindfulness-lens from the research domain of high-reliability organizing (cf. [1-6]). In this domain,

mindfulness is defined as the organizational capability of being aware of discriminatory detail and

capabilities to contain unforeseen circumstances [5]. To anticipate unforeseen situations, mindfulness’

anticipation principles, for example, suggest organizations to continuously spend attention to potential

failures and to resist simplifying interpretations of reality. Additionally, mindfulness’ containment

principles pursue organizations to defer to expertise in problem situations and build structures for

resilience.

We follow the iterative ethnographic-action research approach [7] to study how practitioners’

behavior changes along the mindfulness principles as we implement technologies. In our fieldwork we

first observe current coordination practices, develop and implement new technologies - such as 4D-

CAD process visualizations – in multi-stakeholder project meetings. To analyze how the technologies

influence mindful behavior we first tape- and video-record our fieldwork activities, and then use

qualitative data analysis software to theorize from the observations.

To date, we observed eight projects of which four where supported with 4D-CAD tools. In the

first projects, we identified recurring coordination discussion topics and issues. Based on this we

created a domain ontology that allowed us to develop customize 4D-CAD models in our four latest

projects. In the last four projects, we observed how 4D-CAD provided visual, detailed overview of

complex multi-stakeholder construction plans that are regularly hard to grasp in the mind. This

supported clash and interface analyses, schedule shortcomings discussions, and evaluations of

process delay. In terms of mindfulness these features strengthen behavior along pre-occupation with

failures and reluctance to simplification principles. Also for containment, practitioners suggested that

the tools could help to quickly evaluate delay mitigation plans, and therewith enhance resilience.

We plan to continue our analysis on how tools such as 4D-CAD influence mindful coordination.

Since our findings provide first evidence that 4D-CAD tools enhance mindfulness, and therewith,

reduces errors and improve process reliability, we also plan to conduct valorization by providing

customized 4D-CAD coordination trainings for utility project professionals. Additionally, we plan to

show construction managers how possible (technology) innovation strategies can contribute to

enhanced mindfulness in their projects. For practice, enhanced mindfulness eventually helps to

increase stakeholder alignment and therewith reduces holdups and project overruns.



Page 49 of 84

41. A systematic risk analysis approach against tunnel-induced building damages

Limao Zhang ([email protected]), Advisor: Dr. Xianguo Wu

Huazhong University of Science and Technology

Due to a continuous growth in urbanization worldwide, a large number of new tunnels are being

constructed or planned for high-speed railways within congested urban areas, especially in developing

countries, like China. The tunneling excavation works in soft ground inevitably lead to ground

movements, which may cause adjacent surface buildings to deform, rotate, distort, and possibly

sustain unrecoverable damages, especially those founded on shallow foundations. The exploitation of

urban underground space presents several geotechnical engineering problems, one of which is the

effect of underground tunnel excavation on surface and subsurface buildings. The impact of the tunnel

excavation on adjacent buildings is of major interest for tunneling construction in urban areas, due to

complex tunnel-soil-building interaction. In order to assure the safety and serviceability of nearby

buildings in tunneling environments, it is necessary to explore the safety risk mechanism of the tunnel-

induced damage to nearby buildings. The main works of this dissertation are as follows:

(1) A static risk analysis model based on Extended Cloud Model (ECM) is proposed to analyze the

safety of existing buildings in design phase. ECM is an organic integration of Extension Theory (ET)

and Cloud Model (CM), where ET is employed to flexibly expand the variable range from [0, 1] to (-∞,

+∞), and CM is used to overcome the uncertainty of fuzziness and randomness during the gradation

of evaluation factors. An integrated interval recognition approach to determine the boundary of risk

related intervals is presented, with both actual practices and group decisions fully considered. The risk

level of a specific adjacent building is assessed by the correlation to the cloud model of each risk level.

A confidence indicator is proposed to illustrate the rationality and reliability of evaluating results.

Compared with other traditional evaluation methods, ECM has been verified to be a more competitive

solution under uncertainty.

(2) A dynamic risk analysis model based on Bayesian Networks (BNs) is proposed to analyze the

safety of existing buildings in construction stage. At first, priori expert knowledge is integrated with

training data in model design, aiming to improve the adaptability and practicability of the model

outcome. Then two indicators, Model Bias and Model Accuracy, are proposed to assess the

effectiveness of BN in model validation, ensuring that model predictions are not significantly different

from actual observations. Finally, adopting the forward reasoning, importance analysis and

background reasoning techniques in Bayesian inference, decision-makers are provided with support

for safety risk analysis in pre-accident, during-construction and post-accident control processes in real

time.

(3) A decision support system, composed of sensing, processing and application layers, is presented

to manager the safety of adjacent buildings in tunneling environments. The function design of the

proposed support system is explored according to actual engineering requirements. This system is

capable of realizing the risk monitoring, risk early-warning, and risk diagnosis for the safety assurance

of adjacent buildings in real time throughout the overall tunneling works. The decision-making

efficiency can be improved, and the dependence on domain experts existing in traditional expertise-

based approaches is lowered, making efforts to perfect the safety monitoring and forewarning

mechanism in tunnel construction. This system can be used by practitioners in the industry as a

decision support tool to investigate the accident evolution patterns regarding the building safety in

tunneling environments, as well as to increase the likelihood of a successful project in tunnel

construction.


app:lj:%E5%85%88%E9%AA%8C%E7%9F%A5%E8%AF%86?ljtype=blng&ljblngcont=0&ljtran=priori%20knowledge


Page 50 of 84

42. Modeling and Visualizing the Flow of Trade Crews in Construction Using Agents and

Building Information Models (BIM)

Lola Ben-Alon ([email protected]), Advisor: Dr. Rafael Sacks

Technion IIT

In construction management research there is often a need for experiments to assess the impacts of different production control methods and information flows on production on site. Simulation methods are useful for such experimentation because field experiments suffer difficulties with isolating cause and effect. Existing methods such as DES (Discrete Event Simulation) are limited in terms of their ability to model decision-making by individuals who have distinct behavior, context and knowledge representation (Brodetskaia et al. 2012, Sawhney et al. 2003, and Watkins et al. 2009). The main objective is to develop an agent-based simulation model for studying and improving production control in construction processes, which accounts for individuals' decision making process and acquired knowledge. The simulation will exhibit the interdependence of individual workers and crews as they interact with each other and share resources. The goal will be to make the model robust and valid by attempting to calibrate it with field observations. Unlike the few existing research models, the simulation will be situated in a realistic virtual environment modeled using BIM, allowing future experimental setups that can incorporate human subjects. The method employs agent-based simulation (ABS), with a “bottom-up” approach to model the interactions between individual agents. It uses BIM models to define the physical and the process environment for the simulation. We apply agents programmed with decision making rules and utility functions to a to-be-built-environment represented as a BIM. By varying parameters such as reliability between workers, thresholds for information gathering and approach to making-do in terms of risk, it is possible to generate aggregate system performance similar to those found in an actual building context in the construction site. The research has two main steps: 1. Development of an agent based model to simulate the process incorporated in the LEAPCON game (LEAPCON 2005). This step has been implemented using the Starlogo TNG tool for multi-agent simulations which provides a development environment within a 3D visual context. The agent-based simulation of the LEAPCON game was developed with agents for the four independent specialty subcontractors, the client representative and the quality controller, and for each of the 32 apartments considered. The results show good calibration with existing observed field data, and to the existing DES. The effects are shown by measuring Work In Progress (WIP), Cycle Time (CT), cash flow patterns and efficiency of the operations (Sacks et al. 2007).

2. Development of an agent-based model in UNITY 3D game engine to simulate production control of a process in a full-scale building project. This step is being pursued in collaboration with a construction company. Data on workers' motivations, behavior and performance was collected using interviews and observations of a crew performing finishing works in a high-rise residential tower project. This step presented the following challenges: observation and formulation of the variables and target function (motivations) of the agents, modeling the behavior of the agents while classifying professions, validation by calibration with actual performance. The contribution of this research lies in the development and testing of the ABS simulation. No simulations of this kind exist: previous efforts with ABS systems for construction have been limited to simplified and virtual environments that use DES that cannot reliably model the complex, emergent patterns of production behavior that result from the interaction of the myriad subcontracting teams and suppliers that perform construction work on and off site. In particular, the influence of each participants' knowledge, context and motivations on their day to day decisions about resource allocation and work sequence can be modeled in the ABS simulations, while they could not be modeled using DES. To-date, there has been no simple and reliable way to test different ideas for production control paradigms in construction.


Page 51 of 84

43. Measuring Interdependent Infrastructure Resilience under Normal and Extreme

Conditions

María E. Nieves-Meléndez ([email protected]), Advisor: Dr. Jesús M. de la Garza

Virginia Tech

Infrastructure is one of the most important elements of our built society. Failure in the infrastructure

system translates to human and economic losses, and in some cases environmental impacts that

could last for decades. Therefore, it is imperative to put efforts in making sure that the lifeline systems

are reliable in moments of stress. From the impact of natural hazards like Hurricane Katrina, to system

failures like the 2003 Northeast blackout and component failures like the I-35W Minneapolis bridge

collapse, civil engineers have the responsibility to renew and build infrastructure that can resist the

impact of a disruptive event, respond to such impact in a timely manner, and recover to the normal

operating condition. These three aspects form the concept of Resilience. A comprehensive literature

review has revealed the necessity for further research that develop standard frameworks and

techniques for measuring and increasing the resilience of the infrastructure systems. Consequently,

the objective of this research effort is to develop a framework for measuring the resilience of a ground

transportation system under normal and extreme conditions. Two existing models found in the

literature will be combined with the purpose of measuring the resilience of roadways under

predicted/anticipated traffic loads, routine intrusions, and extreme intrusions. The framework will

integrate a probability approach with a three-stage (resistive, absorptive, and restorative) resilience

model. This research can contribute to the development of techniques that identify the weaknesses in

the system and find ways to increase the resilience. The results could help engineers, state DOTs and

policy makers make investment decisions based on the resilience condition assessed. It could also

help identify and bring awareness to the risks associated with failing to renew the infrastructure

systems.



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44. Thermally Activated Clay Based Biomass Pozzolana Investigations for Sustainable

Construction in Ghana

Mark Bediako ([email protected]), Advisor: SKY Gawu and AA Adjaottor

Kwame Nkrumah University of Science and Technology, Ghana

In Ghana, as in much of Africa, cement for building construction is expensive. Agriculture and forestry

biomass are perceived as a waste resource with little technological enhancement or valorisation. In

most of farm-growing areas and the wood processing industries, waste biomass such as palm kernel

shells, maize cobs, rice husk and sawdust have created environmental nuisance and disposal

problems. On the other hand, the construction industry which depends enormously on cement also

has negative impacts on sustainability through the generation of harmful anthropogenic gas and, major

contributing factor to global warming. In this work, pozzolanic clay, which embodies less anthropogenic

gas would be produced and use to replace cement using a readily available and local source. Waste

biomass including palm kernel shells, sawdust, rice husks and maize cobs has been used as part of

the raw materials for pozzolanic material production. Experimental program drawn for the investigation

will include optimum temperature determination, chemical and mineralogical composition, mechanical

and non-mechanical properties, morphological and phase analysis of hydrated products, heat

evolution, durability, sustainability analysis and technology transfer program. Producing pozzolanic

materials from a mixture of biomass and clay is a novel and this work is expected to expand alternative

extended cements as well as alternative solutions to the disposal of waste biomass in Ghana.



Page 53 of 84

45. ASSESSMENT OF ACTIVITIES’ CRITICALITY TO CASH-FLOW PARAMETERS

Marwa Hussein Ahmed ([email protected]), Advisor: Dr. Tarek Zayed, Dr. Ashraf

Elazouni

Concordia University

Cash flow modeling is a very useful financial management tool that contractors use to run a sustained

business. Contractors manage multiple activities within a single project. The activities’ start times are

the inherent variables which determine the values of periodical negative cumulative balances and the

other cash-flow parameters of cash flow model. This work reveals a system that can identify those

activities that have the most influence on cash flow. The Monte Carlo Simulation technique has been

employed here to generate schedules and their associated cash flow models for a case study by

randomly specifying the activities’ start times. Uniform discrete probability distributions are assumed

for the activities’ start times, with the minimum and maximum values representing the early and late

start times, respectively. In addition to the randomness of the activities’ start times, the simulation

model considered the stochastic nature of the periodic cash in and cash out transactions in the cash

flow model by adjusting their values to account for the impact of 43 qualitative factors identified in an

earlier study. The results are presented as probability distributions for the project duration, Profit ,

Financing cost and analyzed based on the three scenarios; each incorporating a different number of

qualitative factors. The activities’ criticality to cash-flow parameters is assessed by evaluating the

number of times a given activity determined a particular cash-flow parameter over the number of runs.

This criticality measurement offers project managers very useful criteria with which to identify the

activities that are most urgent to be completed on time and leads to a better accuracy of forecasting

the cash flow parameters.



Page 54 of 84

46. Understanding Current Horizontal Directional Drilling Practices in Mainland China

Being Used For Energy Pipeline Construction

Maureen Cassin ([email protected]), Advisor: Dr. Samuel Ariaratnam

Arizona State University

As of 2009, China surpassed the United States as the largest global energy consumer. In 2013, coal,

crude oil, and natural gas combined for over 90% of this consumption. Moving forward, it is estimated

that China’s energy consumption needs will more than double by the year 2040, which has gained the

attention of both the Chinese government as well as industry experts world wide. This projection has

also illuminated the country’s ongoing and growing challenge to safely and efficiently supply energy

resources across the country. This includes new supplies to vast areas with developing rural

populations as well as to greatly increase supplies to rapidly growing urban areas.

To meet China’s future energy needs, buried pipeline systems have become the most selected

method of resource transmission. Pipelines are more significantly more cost effective in terms of both

construction and operation than the alternative transmission methods of railway and long-haul trucking.

In addition, construction of buried pipelines, particularly when trenchless construction methods are

applied, have significantly less impact to the surrounding environment. Finally, buried pipelines are

more sustainable than other transmission methods as they require fewer resources to construct as

well as use less overall energy to operate than above ground methods.

With this said, buried pipeline construction in China has become one of the country’s most

influential industries. In recent years, the Chinese government has implemented massive buried

pipeline construction initiatives, allowing China to become the fastest growing country in Trenchless

Technology Methods. Horizontal Directional Drilling (HDD) has played a particularly critical role in

executing these projects by providing an economical, timely and environmentally responsible method

to bypass geographical challenges along the path of the pipelines.

Understanding the role that Chinese HDD engineers and contractors have played in the overall

development of China’s buried energy infrastructure would be of great value to the global trenchless

technology community. Developing countries may benefit by Chinese advancements in large-scale

HDD execution methods, while developed regions such as North America, may benefit by improving

long established HDD practices. Thus, research of HDD’s critical role in the expansion of China’s

energy infrastructure could have a positive effect on future buried pipelines practices and energy

infrastructure construction.



Page 55 of 84

47. Optimizing the Selection of Sustainability Measures for Existing Buildings

Moatassem Abdallah ([email protected]), Advisor: Dr. Khaled El-Rayes


Buildings in the United States have significant impacts on the natural environment, national economy,

and society. According to the U.S. Green Building Council, buildings in the United States account for

41% of energy consumption, 73% of electricity consumption, 38% of carbon dioxide emissions, and

14% of potable water consumption. Furthermore, aging buildings represent a significant percentage

of existing buildings and are often in urgent need for upgrading to improve their operational, economic,

social, and environmental performance. The owners of these buildings often seek to identify and

implement building upgrade measures that are capable of improving building sustainability as well as

achieving certification under various green building programs such as the Leadership in Energy and

Environmental Design (LEED). Several green upgrade measures can be used to improve the

sustainability of existing buildings such as energy-efficient lighting and HVAC systems, photovoltaic

systems, and water-saving plumbing fixtures. Decision makers often need to select an optimal set of

these building upgrade measures in order to maximize the sustainability of their buildings while

complying with available upgrade budgets.

The main goal of this research study is to develop single and multi-objective models for

optimizing the selection of sustainability measures for existing buildings. To accomplish this goal, the

research objectives of this study are to (1) evaluate the actual operational performance of sustainability

measures in existing buildings, (2) develop a novel LEED optimization model that is capable of

achieving user-specified certification levels with minimum upgrade cost, (3) develop an innovative

environmental model for minimizing the negative environmental impacts of existing buildings, (4)

develop an economic model for minimizing building life-cycle cost, and (5) develop a multi-objective

optimization model that is capable of generating optimal tradeoffs among the building sustainability

objectives of minimizing negative environmental impacts, minimizing upgrade cost, and maximizing

LEED points.

The computations of these optimization models were executed using genetic algorithms and

quick energy simulation tool (eQUEST). The performance of the optimization models was analyzed

and verified using case studies of public buildings. The results of analyzing these case studies

illustrated the novel and unique capabilities of the developed models in searching for and identifying

optimal sets of building upgrade measures for existing buildings. These new and unique capabilities

are expected to support building owners and managers in their ongoing efforts to (1) achieve LEED

certification, (2) reduce building energy and water consumption, (3) reduce building negative

environmental impacts of greenhouse gas emissions, refrigerant impacts, mercury-vapor emissions,

and light pollution, and (4) reduce building operational and life-cycle costs.



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48. COMPETENCIES AND PERFORMANCE IN CONSTRUCTION PROJECTS

Moataz Nabil Omar ([email protected]), Advisor: Dr. Aminah Robinson Fayek


In contemporary construction environments, construction companies tend to measure how well they

perform against a set of predefined performance indicators. These performance indicators are

assumed to be governed by the ability of the company to attain necessary sets of “competencies” that

enables the successful execution of construction projects.

Competencies in general are difficult to define and measure due to the multidimensional and

subjective nature of its assessment. Competencies exhibit subjective assessments that cannot be

expressed by the traditional numerical approaches. The body of literature conducted in the area of

competencies and performance concluded a need for a more comprehensive research in this area to

identify and formulate competencies and its relationship to construction projects performance.

This research aims to: 1) identify and categorize the different sets of competencies and

performance measurements necessary for better assessment of how well construction projects

perform, 2) develop a methodology for measuring the different competencies and performance

measurements, 3) map competencies to the different performance indicators, thus identifying possible

enhancements for construction projects performance through identifying the relationship between

competencies and performance, and 4) develop a fuzzy hybrid intelligent model to predict construction

project performance based on the existing competencies on the project level.

The methodology for conducting this research is divided into three stages namely; 1)

identifying the different competencies and performance measurements through literature review and

expert’s focus groups, 2) collecting competencies and performance measures from different

construction projects in Alberta, Canada, and, 3) applying novel fuzzy-hybrid techniques for analysis

and modeling of competencies and performance.

This research is expected to have an ample scientific and practical significance. On the

scientific level, this research will investigate and apply state of the art techniques in fuzzy-hybrid

modeling through the application of prioritized fuzzy aggregation for combining experts’ evaluation for

the different competencies. A cascade fuzzy neural network will be developed for predicting project

performance based on existing competencies. On the practical level, a process for identifying and

measuring project competencies and predicting project performance will be available for construction

practitioners to assess their project competencies and how well construction projects are executed.

Additionally, the prediction model to quantify competencies and forecast different performance

indicators will function as a decision-support tool for construction practitioners when evaluating

construction projects performance.


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49. Improving Construction Cost Escalation Estimation Using Macroeconomic, Energy and

Construction Market Variables

Mohsen Shahandashti ([email protected]), Advisor: Dr. Baabak Ashuri


Recently, the accuracy of construction cost estimates has been significantly affected by fluctuations in construction costs. Construction cost fluctuations have been larger and less predictable than was typical in the past. Cost escalation has become a major concern in all industry sectors, such as infrastructure, heavy industrial, light industrial, and building. Construction cost variations are problematic for cost estimation, bid preparation and investment planning. Inaccurate cost estimation can result in bid loss or profit loss for contractors and hidden price contingencies, delayed or cancelled projects, inconsistency in budgets and unsteady flow of projects for owner organizations. The major problem is that construction cost is subject to significant variations that are difficult to estimate. The objective of this research is to create multivariate time series models for improving the accuracy of construction cost escalation estimation through utilizing information available from several indicators of macroeconomic condition, energy price and construction market. An advanced statistical approach based on multivariate time series analysis is used as a main research methodology. The first step is to identify explanatory variables of construction cost variations. A pool of 16 candidate (potential) explanatory variables is initially selected based on a comprehensive literature review about construction cost variations. Then, the explanatory variables of construction cost variations are identified from the pool of candidate explanatory variables using empirical tests including correlation tests, unit root tests, Granger causality tests, and Johansen’s cointegration tests. The identified explanatory variables represent the macroeconomic and construction market context in which the construction cost is changing. Based on the results of statistical tests, several multivariate time series models are created and compared with existing models for estimating construction cost escalation. These models take advantage of contextual information about macroeconomic condition, energy price and construction market for estimating cost escalation accurately. These multivariate time series models are rigorously diagnosed using statistical tests including Breusch–Godfrey serial correlation Lagrange multiplier tests and Autoregressive conditional heteroskedasticity (ARCH) tests. They are also compared with each other and other existing models. Comparison is based on two typical error measures: out-of-sample mean absolute prediction error and out-of-sample mean squared error. Based on the correlation tests, unit root tests, and Granger causality tests, consumer price index, crude oil price, producer price index, housing starts and building permits are selected as explanatory variables of construction cost variations. In other words, past values of these variables contain information that is useful for estimating construction cost escalation. Based on the results of cointegration tests, Vector Error Correction models are created as proper multivariate time series models to estimate cost escalation. Our results show that the multivariate time series models pass diagnostic tests successfully. They are also more accurate than existing models for estimating cost escalation in terms of out-of-sample mean absolute prediction error and out-of-sample mean square error. These findings contribute to the body of knowledge in construction cost escalation estimation by rigorous identification of the explanatory variables of the escalation and creation of multivariate time series models that are more accurate than the univariate time series models for estimating the escalation. It is expected that proposed cost escalation estimation models enhance the theory and practice of cost escalation estimation and help cost engineers and capital planners prepare more accurate bids, cost estimates and budgets for capital projects in various industry sectors.



Page 58 of 84

50. Ex-Ante Simulation and Visualization of Sustainability Policies in Infrastructure

Systems: A Hybrid Methodology for Modeling Agency-User-Asset Interactions

Mostafa Batouli ([email protected]), Advisor: Dr. Ali Mostafavi


The research presented in this poster focuses on the creation and testing of a new paradigm for sustainability assessment in urban infrastructure System-of-Systems (SoS). The National Academy of Engineering recently listed “restoring and improving urban infrastructure” through sustainable approaches as one of the global challenges of engineering in the 21st century. Assessment of sustainability in infrastructure systems is complex due to the existence of various actors whose adaptive behaviors and interactions affect the performance of asset networks. However, the existing methodologies (e.g., urban metabolism and life cycle analysis) for assessment of sustainability in infrastructure systems do not capture the existing complex adaptive behaviors and uncertainties, and thus, could not provide a robust basis for policy analysis and decision-making. The key missing element is an integrated methodology that captures the complex interactions at the interface between agency, asset, and user behaviors for ex-ante analysis of sustainability in infrastructure systems. The objective of this study was to create and test an ex-ante analytical framework for micro-simulation of policies related to the sustainability of urban infrastructure under uncertain conditions. This research investigated the hypothesis that sustainability in infrastructure System-of-Systems is an emergent property as a result of the coupling effects between: (1) the strategic and operational decision-making processes of the asset owners, (2) the performance of infrastructure assets, and (3) the user behaviors. A System-of-Systems approach was adopted in this study to provide an integrated framework for analysis of sustainability in infrastructure systems. This framework was based on the abstraction and micro-simulation of the interactions between the dynamic behaviors of infrastructure agencies, users, and assets, and its application was demonstrated in assessment of the sustainability in highway transportation infrastructure. Using the framework and data obtained from different sources ranging from historical records and literature reviews to case studies, the interdependencies between agency, asset, and user behaviors were explored. These interdependencies (e.g., the effects of maintenance/rehabilitation decisions of agencies on asset performance and user behaviors) were then used to develop an integrated model for micro-simulation of policies related to sustainable infrastructure management. In the integrated model, the micro-behaviors of infrastructure agencies and users were captured using agent-based modeling, the dynamic performance of infrastructure assets were modelled using system dynamics, and the environmental impacts were considered using a performance-adjusted life cycle analysis model. Using this model and Monte-Carlo experimentation, the policy landscape pertaining to the sustainability of a highway transportation network was simulated in a case study. The model was verified and validated by using sensitivity analysis and uncertainty propagation analysis. The results revealed the optimal policy scenarios based on different levels of budget, pavement types, and maintenance/rehabilitation strategies that enhance the sustainability of infrastructure systems at the network level and under different uncertain conditions. This distinctive approach is the first of its kind to simulate and visualize the policy landscape pertaining to the sustainability of infrastructure systems by simulating the dynamic behaviors at the interface between agencies, users, and assets. The framework and simulation model have the following benefits for policy analysis: (i) simulation and visualization of the outcomes of policies on the sustainability of infrastructure at the network level and at various policy horizons, (ii) comparison of the outcomes of different policies based on different infrastructure characteristics, agency priorities, and user behaviors, (iii) creation of the landscape of sustainable policies for infrastructure management, (iv) identification of the desired scenarios, and (v) quantification of the likelihood of desirable outcomes as a result of different policies.



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51. Dynamic Fatigue Model for Assessing Muscle Fatigue During Construction Tasks

MyungGi Moon ([email protected]) Advisor: Dr. SangHyun Lee


Construction is a labor-intensive industry that employs 11.1 million workers in the U.S., and that

involves repetitive manual handling works. Construction workers therefore frequently suffer from a

significant level of fatigue that heavily affects their physical capability. In particular, work-related

muscle fatigue can result in various adverse effects, such as productivity loss, human errors, unsafe

acts, muscle injuries, and work-related musculoskeletal disorders (WMSDs). Also, fatigue related to

the nervous system (i.e., central fatigue) can result in long-term performance decline, such as chronic

fatigue syndrome, burnout syndrome, and absenteeism.

There have been diverse research efforts attempting to understand and quantify fatigue.

However, they have been conducted under constrained laboratory conditions or are mainly based on

surveys, instrumental methods, and mathematical models. As a result, these research efforts may not

be suitable for assessing occupational fatigue under real work conditions due to limitations in

laboratory experiments, the possibility of bias in surveys, interference with ongoing work in

instrumental methods, and the difficulty in reflecting dynamic workloads and diverse task demands in

mathematical models.

To address these issues, we aim to estimate fatigue during construction tasks by applying System

Dynamics (SD), Discrete Event Simulation (DES), and biomechanical analysis using 3DSSPP. SD is

a simulation technique that helps to understand feedback processes by identifying variables’ cause-

and-effect relationships. DES is used to represent a certain construction task (e.g., masonry and rebar

works) showing sequential work elements and providing idling time and work time. Captured workers_

motion data using a Kinect will provide internal force data (% of Maximum Voluntary Contraction: MVC)

loaded at the workers_ body parts using 3DSSPP. As a result, localized muscle fatigue can be

estimated by the SD model as a function of construction workloads generated by 3DSSPP from

different construction tasks with an interval for force exertion (e.g., working and idling time) from DES.

The proposed model was statistically validated by comparing it with existing fatigue models in terms

of Normalized Root Mean Square Deviation (NRMSD). In addition, experimental validation was

conducted by measuring endurance times, one of the measures for muscle fatigue, from subjects

under several force exertion protocols that mimic construction masonry tasks (i.e., repetitive lifting

concrete masonry units); the endurance times were then compared with estimated endurance times

from our model. The results show that the model provides a robust estimation of localized muscle

fatigue.

This research greatly contributes to an understanding of physiological demands during

construction tasks, and identifies whether and to what extent fatigue can be estimated and quantified

for actual tasks. Further, the model can be used to adjust work schedules, providing a test tool to

estimate workers_ fatigue and eventually preventing undesirable consequences from muscle fatigue

by extending the margin of safety.


Page 60 of 84

52. Toward Sustainable Capital Transportation Infrastructure: Maximizing Performance of

Preplanning Phase

Nahid Vesali ([email protected]), Advisor: Dr. Mehmet Emre Bayraktar


The need for new and updated infrastructure has grown greatly all around the world in the last decades.

Among all infrastructures, Capital (large-scale) infrastructure projects draw more attention because of

their considerable investment and inherent complexity. One of the challenges face to the capital

infrastructure projects is finding potential solutions and identifying best-fitted option to respond the

investigated need or problem while considering three pillar sustainability issues. These processes

happen in preplanning phase of the project. Most of capital transportation infrastructure projects

experience long time preplanning phase, for example in Port of Miami Tunnel project, preplanning

phase take about ten years and selected solution alternate several times. This indicates that there are

deficiencies in this early stage phase, which need to be determined and improved.

This study creates a novel decision-making model for preplanning phase in capital

transportation infrastructure. This research reveals the deficiencies and pitfalls in preplanning phase

of capital transportation infrastructures and finds solutions to overcome them. Three main deficiencies

are determined here: 1) A transparent, formal and systematic procedure is rare for preplanning phase

in large-scale projects; 2) Project level uncertainty is not considered in preplanning phase and

alternative appraisal; 3) Selecting the alternatives is often based on subjective expert opinions and

impact of required cost and time for investigating alternatives in different level of uncertainty is not

considered. To achieve the purpose of the study, first the mechanism of preplanning phase of capital

transportation infrastructure is established and broke down to some sub-phases to find the dynamics

of the phase, which are: Identifying need for project and problem analysis; Creating ideas and defining

scope; Preliminary feasibility study and alternatives appraisal; Select the optimal alternative. The

conceptual sub-phases identified based on the literature review, analysis of ten implemented capital

transportation infrastructures and interviews. Then the important factors affecting the alternative

selection process in each sub-phase are determined. One considerable problem highlighted in

feasibility study sub-phase is dealing with high uncertainties due to lack of detailed data. To cope with

this problem, a project level uncertainty assessment with Monte Carlo simulation model is established

to convert the traditional deterministic feasibility study into probabilistic results. The outcome of this

stage is a matrix of distribution function of measured value of factors for each alternative.

Finally, the study seeks a holistic framework to find the optimal alternative of capital

transportation infrastructure by the end of preplanning phase. A decision support system, which is an

optimization model, is proposed. The model compares different alternatives based on aforementioned

distribution function of distinct factors, in the form of probabilistic decision tree objective analysis. Each

decision maker organization or governmental agency can enter its priorities among factors to the

model and find the optimal solution with specific confidence level. Using this model significantly

decreases the complexity as well as required time and cost of preplanning phase in capital

transportation infrastructures.


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53. A quantitative investigation of building micro-level power management through energy

harvesting from occupant mobility

Neda Mohammadi ([email protected]), Advisor: Dr. Tanyel Bulbul, Dr. John E. Taylor

Virginia Tech

Climate change mitigation strategies are targeting carbon pollution reduction by at least 3 billion metric

tons cumulatively by 2030. To comply with this end particularly in our residential and commercial

sectors, a correlation between the energy efficiency strategies and energy harvesting from emerging

renewable energy resources is essential. Due to high reliance on electricity in energy demands, power

management plays a significant role in the required interplay between the two strategies. While macro-

scale energy harvesting technologies such as wind turbines, hydro-electric generators and solar

panels are being employed in macro-scale power management by directly feeding the grid; we lack

an integrated micro-scale power management system at the building level which relies on energy

harvesting from ambient environment.

We intend to explore an alternative micro-scale energy harvesting system which can be

employed in buildings in support of off the grid micro-level power management. Harvesting, converting,

and storing energy from human locomotion through wearable devices have attracted commercial and

military attention; but have largely focused on relatively small scale energy conversion for personal

devices. In this research, we quantify whether the accumulated energy harvested from building

occupants’ mobility can contribute to the electrical demand, and thus offset CO₂ emissions.

We conducted a pilot study in which the data from wearable activity tracker devices was

monitored to assess the potential available energy which could be transmitted to balance the energy

consumption of an office building. Office buildings have high occupant mobility in aggregate which

could offset a meaningful portion of building CO₂ emissions.

An energy balance analysis incorporating the electrical energy harvested from occupants’

mobility has shown promising results of more than 2 tons of CO₂ emission reduction in one month for

the office building under study. This amount in larger scales can potentially offset meaningful portions

of disaggregated energy use and its consequential emissions.

We offer an exploratory analysis of the potential energy conversion and exchange in a building-

occupant system which can be produced through energy harvesting of human locomotion. Harvesting

energy generated by the human mobility patterns of building occupants may represent an important

step forward in instigating a larger renewable energy resource to draw upon, which will also infuse the

occupants' network with improved energy consumption behavior.


Page 62 of 84

54. Estimating Labor Productivity Frontier: A Pilot Study

Nirajan Mani ([email protected]) and Krishna P. Kisi ([email protected]), Advisor: Dr.

Eddy M. Rojas

University of Nebraska-Lincoln

The efficiency of construction operations is typically determined by comparing actual vs. historical

productivity. This practice is accurate if historical data reflect optimal values. Otherwise, this

comparison is a gauge of relative rather than absolute efficiency. Therefore, in order to determine

absolute efficiency, one must compare actual vs. optimal productivity. Optimal labor productivity is

the highest sustainable productivity level achievable under good management and typical working

conditions. Meanwhile, labor productivity frontier is the theoretical maximum production level per unit

of time that can be achieved in the field under perfect conditions. This level of productivity is an

abstraction that is useful in the estimation of optimal productivity for labor-intensive operations.

This paper reports on a pilot study performed to evaluate the feasibility of a dual approach for

estimating the productivity frontier for a simple electrical installation. The first approach involves the

estimation of the productivity frontier by using observed durations from a time and motion study. The

movements of a worker are captured by multiple synchronized video cameras. The actions that make

up a particular task are identified from the video frames and categorized into contributory and non-

contributory actions. The best series of contributory actions are identified based on the shortest time

taken to complete a task, and a synthetic worker model is used to determine the productivity frontier

as the sum of the shortest durations for each action. The second approach involves the estimation of

the productivity frontier by using estimated durations on the same time and motion study. The

probability distributions that best represent action durations are identified and the productivity frontier

is defined as the sum of the lowest values from each of the distributions at a 95% confidence interval.

The highest labor productivity value from these two strategies is taken as the best estimate of the

productivity frontier. Thus, the labor productivity frontier computed from this pilot study for the

“Fluorescent Bulb Replacement” task is 20.23 stations per hour.

An accurate estimate of labor productivity frontier is the first step toward developing a process

that will allow project managers to determine the efficiency of their labor-intensive construction

operations by comparing actual vs. optimal rather than actual vs. historical productivity. Toward this

end, this paper reviews relevant literature, presents the details of the proposed dual approach,

introduces results from the pilot study, and evaluates the feasibility of this methodology for estimating

the labor productivity frontier.




Page 63 of 84

55. A Decision Support System for Sustainable Multi Objective Roadway Asset

Management

Omidreza Shoghli ([email protected]), Advisor: Dr. Jesus M. de la Garza

Virginia Tech

The limited and constrained available budget along with old aging infrastructure in nation magnifies

the role of strategic decision making for maintenance of infrastructure. The challenging objective is to

maintain the infrastructure asset systems in a state of good repair and to improve the efficiency and

performance of the infrastructure system while protecting and enhancing the natural environment.

Decision makers are in need of a decision support system to consider these multiple objectives and

criteria. The research proposes and validates a framework for such decisions. Appropriate and optimal

maintenance actions will maintain infrastructure at the best possible condition by investing the

minimum amount of money while considering environmental and time constrains. The proposed model

aims at finding optimal techniques for maintenance of multiple roadway asset items while taking into

account time, cost, level of service and Green House Gas (GHG) emissions. Therefore, goal is to

answer what are the optimal combination of maintenance techniques for roadway assets to maintain

four conflicting objectives of time, cost, level of service and GHG? In other words, the main objective

is to develop a decision support system for selecting and prioritizing necessary actions for

MR&R(Maintenance, Repair and Rehabilitation) of multiple asset items in order for a roadway to

function within an acceptable level of service, budget, and time while considering environmental

impacts.

This model creates a framework for a sustainable infrastructure asset management. A two

stage approach: First a multi-objective problem with four main goals is analyzed. The objectives of the

problem are: minimization of maintenance costs, minimization of maintenance time, Minimization of

GHG and Maximizing level of service. In the second stage, the results of the multi objective

optimization will be further processed using a Multi Criteria Decision Making (MCDM) process. There

have been many studies reported in the literature presenting the application of decision-making

techniques for individual asset items. However, no previous attempts have been made to develop a

decision support system for asset management to select optimal maintenance for various asset items.

The proposed approach will simultaneously optimize four conflicting objectives along with using multi

criteria decision-making technique for ranking the resulted non-dominated solutions of multi objective

optimization.

The results of implementation of the proposed model on a section of I-64 are presented for

sub-set of asset items. Moreover, the proposed model is verified and validated using two more projects.

Results reveal the capability of the model in generation of optimal solutions for the selection of

maintenance strategies. The results of the research benefits decision makers by providing them with

optimal solutions for infrastructure asset management while meeting national goads towards

sustainability and performance-based approach. Moreover, provides a tool to run sensitivity analysis

to evaluate annual budget effects and environmental impacts of different MR&R resource allocation

scenarios. Application of proposed approach is implemented on roadway asset items but it is not

limited to roadways and is completely applicable to other infrastructure asset.



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56. SimulEICon: A Simulation-based Multi-objective Decision-support Tool for Sustainable

Building Design

Peeraya Inyim ([email protected]), Advisor: Dr. Yimin Zhu, Dr. Wallied Orabi


The significance of sustainable construction in the architecture-engineering-construction (AEC)

industry has been identified and emphasized in a wide range of research. Conventional construction

projects are complex endeavors with many professionals and parties from different disciplines trying

to meet multiple project objectives that are often different and conflicting. This complexity is

compounded by new requirements for sustainable construction. SimulEICon or Simulation of

Environmental Impact of Construction is a simulation-based tool that can aid construction

professionals in the decision-making process during the design phase of a building. This tool optimizes

the selection of materials, components, and construction methods. Currently, SimulEICon is built in

MATLAB environment in order to take advantage of its functions and toolboxes and it considers three

main objectives, which are construction time, cost, and environmental impacts, in term of carbon

emissions, throughout the life cycle of buildings. SimulEICon uses Monte Carlo simulation to account

for uncertainty and availability of data, and uses energy simulation to estimate environmental impact

of energy consumption during operating phase. Furthermore, the search for optimal design solutions

at the building level entails the consideration of millions of possible solutions; genetic algorithms are

used as optimization technique. SimuEICon can present a wide range of possible optimal or near

optimal solutions from which construction professionals may choose the most appropriate solution to

meet project goals.


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57. Construction Operations Process Data Modeling and Knowledge Discovery Using

Machine Learning Classifiers

Reza Akhavian ([email protected]), Advisor: Dr. Amir H. Behzadan

University of Central Florida

Despite recent advancements, the time, skill, and monetary investment necessary for hardware setup

and calibration are still major prohibitive factors in field data sensing. The ubiquity of mobile devices

equipped with a range of onboard sensors facilitates the emergence of context-aware applications.

Notwithstanding their applications in computer sciences, healthcare, and sports, such pervasive

technologies have not been yet fully investigated in construction and facility management domains.

The presented research explores the potential of built-in sensors of mobile devices in providing

process-level data that can lead to operations-level knowledge discovery from construction resources.

In particular, smartphone sensors are used to capture multi-modal data for equipment action

classification and recognition. Sensory data is collected in two modes: controlled (i.e. instructed) and

uncontrolled environments. In all scenarios, the device is placed inside the equipment cabin over a

near field communication (NFC) smart tag to launch the data logger application. Raw data collected

by built-in sensors such as 3-axis accelerometers, gyroscope, and global positioning system (GPS)

are used to extract key time- and frequency-domain features such as mean, peak, standard deviation,

correlation, energy, and entropy in windows with different sizes with 50% overlap. Feature

normalization and necessary dimensionality reductions will be performed on the resulting features in

the preprocessing stage. Machine learning classifiers are then employed to classify the features for

detecting various construction equipment actions and estimating durations of different activities. Also,

10-fold cross validation and paired t-test are used to evaluate the accuracy of the classifiers in

detecting activities. This is done for both controlled and uncontrolled modes; in the controlled mode,

all possible actions with a few second intervals in between performed by different construction

equipment are annotated to label the classified actions, whereas in the uncontrolled mode, equipment

perform a certain activity which may consist of multiple actions. Preliminary data collected from

different classes of construction equipment performing various actions shows certain repetitive and

distinguishable patterns in collected data while the equipment performs particular activity. The output

of the developed algorithms can contribute to the current practice of construction simulation input

modeling by providing knowledge such as activity durations and precedence, and site layout. The

resulting data-driven simulations will be more reliable and can improve the quality and timeliness of

operational decisions. Moreover, the action recognition framework can be used for field safety

improvement, productivity assessment, and equipment emission monitoring and control.



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58. The Development of an Automated Progress Monitoring and Control System for

Construction Projects

Reza Maalek ([email protected]), Advisor: Dr. Janaka Ruwanpura, Dr. Derek Lichti

University of Calgary

Project Monitoring and Control are vital to facilitate decision makers identify deviations between the as-planned vs. as-built state of the project and take timely measures where required. Monitoring is the process of collecting onsite data as a means of measuring the performance of the project. Traditionally, onsite data are collected manually, a time consuming and labor intensive task particularly in large scale projects. In practice, to justify the time and cost associated with such manual approaches, a limited amount (or frequency) of onsite data are collected, which diminishes the ability of the project manager to identify the causes of delays and cost overruns on time. Currently, site supervisory personnel spend 30-50% of their time on manually monitoring and controlling onsite data. Therefore, a novel approach towards data collection and analysis is required to help overcome the aforementioned limitations of current manual monitoring and control practices. Here, the main objective is the development of an automated monitoring and control system to assess the performance of construction work as-progressed and to predict the stochastic outcomes of the project. The proposed automated monitoring and control system consists of the following stages: 1. Automated Monitoring System: The technology capable of collecting the “scope of the work performed” is of interest. Based on the comparative evaluation of the applicable remote sensing technologies presented in, LiDAR (Light Detection And Ranging) is recommended for construction site monitoring. With respect to the nature of LiDAR data, the following three concerns are required to be addressed: (i) Optimization of the Location of the Scan-Stations: One of the goals of this research is to reduce the time and cost of manual monitoring. Therefore, the minimum number of scan stations capable of providing 3D point clouds of every structural element onsite is of the essence. The as-planned 4D model is used to simulate point clouds starting from a scan station positioned at an arbitrary location. The scanner is then moved in increments of “fuzzy” terminology and the expected point clouds are simulated. The location where the maximum number of structural facets are detected is considered as the initial scan station. The facets corresponding to the initial scan station are then removed from the as-planned model and the process is repeated for the remaining surfaces until a point cloud is assigned to every surface. (ii) Automated Feature Recognition: This stage involves the automatic identification of structural elements (i.e. Column, Slab) from the collected unorganized LiDAR point clouds. Current object-based recognition models use the planned model as a-priori knowledge to assign 3D point clouds to a structural element [6-9], which may not be reliable in cases where the location of the as-built structure differs from the planned location. In order to eliminate the dependency of the feature extraction model on the as-planned data, the “Geometric Primitives” are used to detect planar (Wall, Beam, Floor and Ceiling Slabs) and cylindrical (Column, Pipe, Cable, Reinforcement) surfaces. To reduce the effects of outliers caused by occlusions, moving objects and dust, a Robust method of Principal Component Analysis (PCA) is proposed to extract planar features through a robust estimate of the covariance matrix of a neighborhood of each point cloud (iii) Automated Feature-based Registration: Since the as-planned model is geo-referenced to a specific coordinate system during the feasibility stages, it is important to register the as-built data to the same coordinate system. For this matter, at least three (3) non-collinear point correspondences between the as-planned and as-built models are required. The extracted features are used to identify the point to point correspondences in order to perform a rigid body transformation from the scanner space to the as-planned space. 3. Automated Control System: The identified “scope of the work performed” is compared to the “scope of the work planned to be performed” in order to determine deviations between the planned and the actual state of the project. Two types of analysis are then performed where significant differences are detected. Initially a Stochastic Neural Network based Earned Value (EV) analysis is carried out to well predict the expected time and cost of completion of the project. Through project management performance enhancement tools such as Crashing, an optimized decision support solution is introduced to improve productivity. To evaluate the feasibility the proposed method for automatic development of 3D as-built models, one set of LiDAR data from a laboratory at the University of Calgary was collected. Our proposed method was able to detect the 22 walls in laboratory with a Mean Radial Spherical Error (MRSE) of 9 cm. Another set of experiment is also designed to monitor and control the expansion of the School of Engineering project for a duration of one year. The main contribution of this research is an automated construction progress monitoring and control system to improve time, cost and quality of the state-of-the-art onsite monitoring practices and to produce the opportunity for timely identification of deviations between the as-planned and as-built state of the project. The following benefits to the construction industry are denoted with the efficient implementation of the aforementioned system: (i)- reduction of the project managers' time and cost of travel to and within the site; (ii)- improving time, cost and reliability of data collection and analysis; (iii)- reduction of time and cost of preparation and analysis of progress reports; (iv)- improvement of quality inspection and management in order to minimize rework early in the project lifecycle; (v)- minimizing construction claims (vi)- development of 3D/ 4D as-built models of the construction site; and (vii)- stability control and Health monitoring of structural elements.



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59. Optimum Resource Utilization Planning in Construction Portfolios through Modeling

of Everyday Uncertainties at Certain Confidence Level

Reza Sheykhi ([email protected]), Advisor: Dr. Wallied Orabi


Planning of resource utilization can largely affect construction completion time and cost, especially when everyday uncertainties are taken into account as main sources of unexpected changes during projects. The impact is even more significant when managers should plan to supply limited pool of resources to a portfolio of concurrent projects, such as transportation network reconstruction. However existing studies in resource-constrained planning did not capture impact of day-to-day changes on time-related risk factors (e.g. weather, and trade coordination, etc.) and their associated uncertainties, and therefore, could not provide a robust and realistic basis for decision-making. On the other hand, planning of a group of project competing for limited pool of resources requires planners to examine their alternative resource sharing capabilities and policies under uncertainty, which is another important missing element in reported construction research. The objective of this research is to cover mentioned research gaps through developing a model to capture impacts of daily-changing uncertainties and alternative resource utilization policies on completion time and cost of construction project portfolios. This study investigates the hypothesis that such a model can result in (1) a more realistic trade-off between completion time and cost and (2) more distinctive and applicable optimal resource utilization solutions for resource-constrained portfolio planning in construction. Developed model aims to provide planners with a contemporary solution map of portfolio planning outcomes (time and cost) based on their resource sharing capabilities and restrictions, and with respect to their risk-taking confidence. This research adopts two main approaches to achieve mentioned objectives: (1) modeling of daily-changing uncertainties in construction network, through stochastic simulation of day-to-day changes in crew productivity instead of activity duration, and (2) finding optimal portfolio time-cost trade-off through a multi-objective optimization model. To this end, Monte Carlo Simulation Method has been employed to capture stochastic nature of crew productivity and to develop portfolio completion time and cost distributions. In addition, the NSGA-II multi-objective optimization has been implemented to find optimum solutions based on (1) alternative project prioritizations (in order to consume limited resources), and (2) alternative overtime working policies. The optimization model eventually intends to minimize overall time and cost of the portfolio, which are obtained from distributions based on planners predefined confidence level. The developed model has been employed for modeling reconstruction of a real case study of 5 roadway projects within a portfolio in United States. The resulting solution map (1) verifies model results being more reliable and realistic comparing to deterministic planning of construction, and (2) enables decision makers to pick their desired optimum solution for resource planning based on their actual availabilities and desired risk-taking confidence level. Results suggest that using alternative overtime policies may vary portfolio duration and cost up to 50% and 5%, respectively. However, using overtime policy alternatives with lower productivity adjustments does not necessarily result in longer projects, and thus the amount of weekly performance of crews should be considered in estimation of total duration. It is also found that higher weekly performance per unit expenditure (applying regular overtime working policies) results in longer portfolios with lower total cost under uncertainties. This research, in general, helps planners select their preferred combination of resource planning options to achieve optimal completion time and cost under uncertainty. To this end, the model provides planners with practical decision support material in order to answer the following questions: (1) how much of each resource should be available in work periods, such as weeks, and (2), how to share this limited available resource pool among projects. This research is the first of its kind (1) to capture impacts of daily-changing uncertainties and alternative resource utilization policies on completion time and cost of construction, and (2) to model planning of a group of project competing for limited pool of resources by examining alternative resource sharing capabilities and policies.



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60. Using STEP Approach to Achieve Successful Outcomes on Complex Projects

Ron Patel ([email protected]), Advisor: Dr. Edward J. Jaselskis

North Carolina State University

The basic underlying issue of this research relates to complex projects not achieving their desired

level of performance in the areas of operational success, cost, schedule, and safety. Complex projects

are unique in many respects compared to more traditional ones by nature of their more diverse

stakeholder objectives, complicated organizational structures, unique financing arrangements,

unproven technologies, and challenging project locations. This research organizes leading project

management practices through a STEP approach in an easy to follow guide for project stakeholders

to achieve better project performance on complex projects. STEP stands for Strategy, Team,

Execution procedures, and Performance Monitoring; this approach embodies much of the research

that has been performed in the area of construction project success on complex projects. First,

developing the right Strategy creates a stable foundation for achieving successful complex project

performance. This involves selecting the right project delivery approach, contract type, and

understanding the risks and uncertainties and developing plans to proactively deal with them.

Selecting the right Team involves building a cohesive organization with the requisite level of knowledge,

experience, and size in terms of number of people and hierarchy. Executing industry proven standard

work process procedures are also important as they that provide consistency to the Team in terms of

how its members interact with one another and how work is to be accomplished. Lastly, Performance

monitoring using appropriate metrics is accomplished throughout all phases of the project to ensure

that the project is tracking according to plan. The STEP procedure was investigated from different

phases of the project life-cycle. The research methodology includes two stages: the first stage involves

building the STEP model. Literature review on existing research pertaining to project success on

complex projects and leading project management practices are used to build the STEP model. The

second stage involves validation of the STEP approach. First, a questionnaire design and expert

survey is planned to ensure these guidelines have included all relevant factors in the model. Second,

a case study approach will be implemented to correlate attributes found in the STEP approach to

project outcome. These guidelines and procedures can be used by owners and contractors on future

complex projects to increase their likelihood of success.


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61. Quantifying Human Mobility Perturbation under the Influence of Tropical Cyclones

Qi Wang ([email protected]), Advisor: Dr. John E. Taylor

Virginia Tech

Climate change has intensified tropical cyclones, resulting in several recent catastrophic hurricanes

and typhoons and making them more difficult to predict. Therefore, understanding and predicting

human movements plays a critical role in disaster evacuation, response and relief. Existing research

has found that human mobility can be captured by the Lévy Walk model, a bio-inspired movement

model. However, we lack knowledge whether the model can predict human movements in affected

areas during tropical cyclones.

In this research, we attempt to quantify the influence from tropical cyclones on human mobility

patterns in coastal urban populations. The research objective is to measure human mobility

perturbation using high accuracy individuals’ movement data collected from Twitter and compare that

with Lévy Walk model predictions.

We selected three significant recent tropical cyclones which struck three coastal urban areas.

These included:

(1) Hurricane Sandy in New York City, (2) Typhoon Wipha in Tokyo, and (3) Typhoon Haiyan in

Tacloban, Philippines. We analyzed the human mobility patterns in each city before, during and after

the tropical cyclones, comparing the perturbed movement data to steady state movement data. We

analyzed travel frequencies, movement distribution, duration and strength of human mobility

perturbation.

We discovered that while tropical cyclones changed travel frequencies and distances of urban

dwellers, power law still dominates human mobility, and therefore, the animal-derived Lévy walk model

is still able to describe human movement even in perturbed states. We also found that the

intensification of tropical cyclones is directly related to the strength and duration of human mobility

perturbation.

The study deepens our understanding about the interaction between urban dwellers and civil

infrastructure. It may improve our ability to predict human movements during natural disasters and

help policymakers and practitioners to develop disaster evacuation and response plans.


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62. Construction Workers’ Behavior Influenced by Social Norms: A Study of Workers’

Behavior Using Agent-Based Simulation Integrated with Empirical Methods

Seungjun Ahn ([email protected]), Advisor: Dr. SangHyun Lee


Due to the labor intensive nature of construction processes, workers’ attitudes and behavior

significantly affects construction project performance such as productivity and safety. Among the

factors that affect worker behavior, social norms in a workgroup may act as motivational capital and

play an important role in shaping workers’ behavior. However, our knowledge of the emergence and

the exertion of social norms in transient construction workgroups has been very limited. With a goal to

enhance our knowledge in this regard, this poster presents an interdisciplinary approach to worker

behavior influenced by social norms using an integrated methodology incorporating the agent-based

modeling and simulation of human behavior and empirical methods. In this approach, individual-level

worker behavior influenced by both formal rules and informal rules in projects is modeled as an agent

behavior rule, and a simulation unfolds the dynamic, complex systems behavior of workgroups.

Empirical methods based on survey data are used to support the agent-based modeling and simulation

in this research. The empirical data collected from construction workers are categorized, then

compared with simulation data, and are used to create empirically supported, specific agent-based

models of worker behavior. The result of this interdisciplinary effort reveals that construction workers’

behavior is indeed under the influence of social norms despite the transient nature of construction

worker employment, and that workers’ self-categorization is the main mechanism of the social control

in workgroups. This research also identifies the keys to the emergence of positive social norms in

workgroups using simulation experiments. The findings of this research provide important implications

for managing workforce in construction regarding how injunctive norms and descriptive norms can be

used to manage worker behavior in construction. In addition, this work shows how empirical data

collected by survey questionnaire can be used as a basis for creating empirically supported agent-

based model of human behavior. Further, the applicability and extensibility of the agent-based

modeling approach in construction worker behavior research is discussed in this poster.


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63. Construction site layout planning using simulation

SeyedReza RazaviAlavi ([email protected]), Advisor: Dr. Simaan AbouRizk


Site layout planning is performed in the planning phase of each construction project, and has

significant impacts on project safety, productivity, cost and time. This study focuses on two main tasks

of construction site layout planning: identifying the size and determining the location of temporary

facilities, and aims to develop a comprehensive model for estimating the size of the temporary facilities

and locating them on sites in order to improve project productivity.

On construction sites, size of some facilities (e.g. batch plants and equipment) is

predetermined and fixed, while size of other facilities (e.g. material laydowns and stock piles) is

variable and should be determined. The latter group mostly concerns facilities that temporarily contain

materials. Properly estimating size of such facilities is critical, particularly on congested sites, because

inaccurate estimation can result in loss of productivity and safety, and incur extra costs to projects.

For sizing this kind of facility, system production rate is a significant component. However, estimating

production rate is a complex process, due to interdependency of diverse planning decisions,

construction uncertainties, and dynamics of construction projects. To address this complexity, a hybrid

discrete-continuous simulation technique is proposed for modeling purposes. Simulation is superior in

modeling construction operations and estimating system production rate by capturing uncertainties

and dynamic interactions between variables. The combination of discrete and continuous simulation

is used to enhance more accuracy in sizing facilities while high computational burdens are avoided.

In order to efficiently use space on construction sites, possible variation of the facility size is also

dynamically modeled as one of the managerial actions, which is often overlooked in the existing

construction site layout planning methods.

Additionally, locations of facilities influence project productivity and safety. Although many

methods have been developed for optimizing facility locations, the efficiency of these methods in

practice is in question. In this research, simulation is also proposed to mimic the real world of

construction projects, model the interaction among influencing factors, and ultimately examine the

effectiveness of different layouts.

The main contribution summarized in this research is development of a holistic model enabling

planning site layout along with the construction process and optimizing their corresponding variables.

The proposed approach is able to integrate parameters and constraints of different disciplines

including site layout, material management, logistics and construction process planning in a unified

model for sizing facilities considering uncertainties and managerial actions taken to resolve space

shortages. To this end, simulation is employed to sophisticatedly model the construction process and

interactions between various parameters considering inherent uncertainties and managerial actions.

Using an optimization engine integrated with simulation facilitates finding optimum size and location

of facilities, and aids other planning decisions. The proposed approach can be applied to diverse types

of construction projects such as steel structure, tunneling, earthmoving, and industrial construction, to

demonstrate its adaptability and suitability.



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64. 4-Dimensional Process-Aware Site-Specific Construction Safety Planning

Sooyoung Choe ([email protected]), Advisor: Dr. Fernanda Leite

The University of Texas at Austin

Construction remains the second most hazardous industry especially due to the dangerous

combination of pedestrian workers and heavy construction vehicles and machinery, such as dump

trucks, dozers, and rollers. The Bureau of Labor Statistics reported that, in 2011, 15.7% of industrial

fatal work injuries were in the construction industry, and 39.3% of construction industry fatalities were

related to construction vehicles and machinery. Since the Occupational Safety and Health Act of 1970

was established, which places the responsibility of construction safety on the employer, various injury

prevention strategies have been developed and resulted in a significant improvement of safety

management in the construction industry. However, during the last decade, construction safety

improvement has decelerated and, due to the dynamic nature of construction jobsites, most safety

management activities have focused on safety monitoring during the construction phase. In addition,

the majority of hazards are generated from specific site conditions, but current safety planning

activities lack site-specific information and most safety decisions are made based on previous

experience. Consequently, as projects become more complex and schedule pressure increases,

potential site-specific hazards including the safety impacts of concurrent activities are not effectively

identified and safety personnel cannot prepare or minimize jobsite hazards in advance. The objective

of this research is to systematically formalize the construction safety planning process in a 4-

dimentional (4D) environment to address site-specific temporal and spatial safety information by

leveraging project schedules and information technology to improve current construction safety

management practices. The proposed safety planning approach includes: (1) data-driven safety

database development, (2) site-specific temporal information integration (safety schedule), and (3)

spatial information integration (safety 4D). In order to improve construction vehicle and machinery-

related safety, this research is focusing on horizontal construction projects which involve construction

equipment-intensive activities. From the safety database, initial safety risks of project activities will be

estimated and prioritized based on the combination of struck-by accident attributes and activities’

resources. The proposed safety schedule dynamically linking safety database and a project schedule

will estimate the final activity risk by considering work duration and predict risky work periods. In

addition, safety 4D linking the safety schedule and a project 3D model will analyze the safety impacts

of concurrent activities and predict risky work zones by adding project spatial information. This safety

planning approach was tested with a demo project and will be validated with a real-world elevated

roadway project in future. This research will advance safety knowledge, integrating site-specific

temporal and spatial information, and significantly affect the construction safety planning process. The

proposed safety planning approach can provide safety personnel with a site-specific proactive safety

planning tool that can be used to better manage jobsite safety by predicting activity risk, work period

risk, and work zone risk in advance. In addition, visual safety materials can also aid in training workers

on safety and, consequently, being able to identify site-specific hazards and respond to them

effectively.



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65. The Impact of Business-Project Interface on Capital Project Performance

Sungmin Yun ([email protected]), Advisor: Dr. Stephen P. Mulva, Dr. William J. O’Brien

University of Texas at Austin

A capital project represents a significant investment by a firm to create future economic benefits. Since the global economic recession, many corporate owners have been suffering from misaligned projects and a lack of systematic approach to align project management with business strategy. Corporate owners, therefore, have paid increased attention to business-project interfaces with the aim of improving alignment between business strategy and capital project planning and execution. Despite its importance, the interfaces between business and project functions has not been adequately identified and quantitatively measured. This study intends to identify which interface exists between business and project functions throughout capital project planning and execution process, and to quantify how the business functions get involved in the process and interact with project functions. Using the quantified interfaces, this study also aims to show how the business-project interface accounts for performance outcomes in terms of cost, schedule, change, and achievement of business objectives. To achieve these objectives, this study was conducted through a correlational study based on quantitative approach. A conceptual framework was developed to measure business-project interfaces throughout capital project lifecycle in terms of the interface components: management personnel, phase, work functions, and management efforts. Based on the framework, a questionnaire was designed to identify and quantify personnel involvement and task interaction on the interfaces between business and project functions. Survey was carried out targeting industrial capital projects which have been submitted in the Construction Industry Institute (CII) performance assessment database. The performance data of the projects which responded the survey were extracted from the CII database. Data analyses were conducted through categorical data analysis methods such as Pearson Chi-square test, Somers’ d test, Fisher’s exact test, and interaction effect analysis using factorial analysis of variance. The results of the data analyses indicate that project sponsor, finance, facility/maintenance, operations/production are major business functions which are highly involved in the capital project planning and execution process. Business units interact with project units in about 60% of the work functions. Funding requests during project execution received the highest level of interaction between business and project units among all work functions. Most work functions with higher levels of interaction belonged to front end planning phases such as project scoping, capital budgeting, business objective setting, manufacturing objectives criteria setting, economic feasibility study, and technical feasibility study. In addition to that, effective business-project interface has synergy effects on performance outcomes such as block-and-tackle system. The projects with high involvement of business functions and high interaction between business and project functions have better cost, schedule, and change performance. Moreover, the business-project interface has leverage effects on performance improvement when adequate management practices are highly implemented. The projects with high involvement of business functions and high implementation of the management practices show better performance outcomes. This study provides empirical evidences for the ontological arguments of the business-project interface throughout capital project lifecycle. The study provides assessment tools to quantitatively measure the level of involvement and interaction throughout capital project planning and execution. Industry practitioners now have a quantitative assessment tool that can be used to measure the business-project interface in terms of personnel involvement and task interaction. This tool enables industry practitioners to identify and quantify the current state of the business-project interface within their organizations during the development of a capital project. In addition, the assessment tool helps them understand the interfaces by which management personnel are involved in a capital project, and which tasks require interaction between the business and project unit. The descriptive statistics from the assessment can be used as benchmarks to compare their organization’s current level to others and will be used to examine the correlation between business-project interfaces with project performance outcomes.



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66. Exploring a PREFERENTIAL Framework for Future Project Opportunities

Timothy W. Gardiner ([email protected]), Advisor: Dr. Yvan J. Beliveau

Virginia Tech

A project delivery method is defined as “a comprehensive process by which Designers (A/E), Constructors (GC), and various consultants provide services for design and construction to deliver a complete project to the Owner (O).” Design-bid-build (DBB) acts as the most common project delivery system in the United States (U.S.) today, followed by other transactional methods of construction management at risk (CM@R) and design-build (DB). The choice of delivery method has been found to have a defining impact on project results within the U.S. construction industry which still suffers from suboptimal performance including the lowest measured domestic productivity for (almost five (5)) decades. As a result, construction practitioners and academics are in endless search of alternative approaches such as Integrated Project Delivery (IPD) that might meet evolving needs and serve to counteract chronic industry challenges. In its “purest” form as a relational project delivery approach, IPD distinguishes itself from (aforementioned) transactional counterparts on a continuum of collaboration through recognizing all of the following attributes: (1) Early involvement of key participants, (2) Shared risk and reward, (3) Multi-party contract, (4) Collaborative decision-making, (5) Liability waivers, and (6) Jointly developed goals. IPD, with its trademark in 2005, has been considered an emerging delivery method with expected widespread use in the U.S. construction industry despite remaining limited in application. The research objective has been framed on the background of established “elements” found for (real estate and e-business) organizations as well as (integrated and “through-life”) project delivery approaches. With an enabler of a continuum of collaboration metric, these elements can be observed and form the basis for determining a “readiness” for transition to relational project delivery: • To evaluate the impact of “teamwork” (People); • To investigate the evolution of Information and Communication Technologies “ICT” (Technology); • To assess project life cycle “achievement” (Process); and • To critically appraise the established “language” (Legal and Commercial Structure). Current Research Question – How does each transactional project perform based on established metrics and do these (individual and collective) outcomes warrant a move to relational delivery for future work opportunities? The case study methodology has been selected to explore the potential for IPD (“contemporary phenomenon”) within a real-life context. With the organization acting as the primary unit of analysis, the case study will rely on sources of evidence from documentation and interviews of project stakeholders including O, A/E, GC as well as Specialty Contractors (SC) and Manufacturers (M). An instrument developed by literature and tested through pilot study will be employed to establish the readiness of an organization on subject projects in adopting relational principles. The findings will be verified for validity by an expert panel. Within the first decade of the 21st century, five (5) ground-up new construction projects have been completed within an approximately one hundred (100) acre business park, consisting of six (6) flex buildings and three (3) annex warehouse one-story buildings totaling 264,113 gross square feet (SF). The assemblage of five (5) projects has established the following results: • Estimated under a pre-construction arrangement and constructed under CM@R; • Designed, permitted and constructed by the same group of key stakeholders – O, A/E and GC; and • Engaged select SC and M either on multiple (more than one) or (in select instance) all (five) projects. The proposed contribution involves developing a framework for understanding the “limited in application” IPD against project delivery choices that remain prevalent today. This model identifies as a Project Readiness Evaluation Framework Emphasizing Relational Elements Named Teamwork, ICT, Achievement and Language (PREFERENTIAL) approach. Flex-type buildings offer one potential for proposed application as one of nine (9) primary property types in the domestic commercial real estate market, totaling nearly three (3) billion SF.



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67. Envisioning More Sustainable Infrastructure through Choice Architecture

Tripp Shealy ([email protected]), Advisor: Dr. Leidy Klotz

Clemson University

This project evaluates the impact of how decisions are presented, or “choice architecture,” in a

prominent infrastructure-planning tool, the Envision rating system for sustainable infrastructure. The

Envision system is used to evaluate, grade and reward projects for meeting sustainability criteria such

as reductions in greenhouse gas emissions, preservation of wildlife habitat, and accessibility to

community cultural resources. Plans to meet these sustainability criteria can receive points in five

increasing levels. For example, a project earns 4-points by reducing water consumption by 25% and

17-points for 100% reduction. As currently arranged, the points in Envision reward for sustainable

development plans above the industry norm default option (which receives 0-points). However, by

giving points for slight improvements, Envision may unintentionally discourage the even higher levels

of sustainability performance that are possible. This project explores the impact of shifting the default

from industry norm, 0-points, to the level of achievement that received 17-points in the water

consumption example.

We draw connections from behavioral science literature, and how risk framing, reference

points, status quo, defaults, and order partitions are represented in Envision. We conducted empirical

studies examining the effects of default changes to the Envision point system. Undergraduate civil

engineering students receive a case study depicting a brownfield site and stream restoration project

in rural Alabama. They are told to complete the project design using the Envision rating system. Half

of the participants receive the industry norm, 0-point, default and the other half receive the higher point

default. Once the rating is complete, participants answer survey questions measuring for control

variables and post-task opinions on motivation and confidence.

Preliminary results indicate shifting the default to a higher level of achievement encourages

users to achieve more points. Those who received the higher set default perceived the default points

as greater value and losing these points is a perceived risk. Currently, we are testing default changes

with a professional engineering and construction cohort.

Small changes to the decision-making framework for infrastructure can impact the projects

overall sustainability. We detail how choice architecture is applicable to the current version of Envision

and call for more research in this area. Identifying the highest-impact decisions and their determinants

at individual, organizational, and societal levels are primary research needs. This presentation opens

the discussion, provides a path for future research, and details methods for using choice architecture

in the current version of the Envision rating system.



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68. Segmentation and Recognition of Roadway Assets from Car-Mounted Camera Video

Streams using a Scalable Non-Parametric Image Parsing Method

Vahid Balali ([email protected]), Advisor: Dr. Mani Golparvar-Fard


Efficient data collection of high-quantity and low-cost roadway assets such as traffic signs, light poles,

and guardrails is a critical component in the operation, maintenance, and preservation of transportation

infrastructure systems. Nevertheless, current practices of roadway asset data collection are still time-

consuming, subjective, and potentially unsafe. In addition, the subjectivity and experience of the raters

have an undoubted influence on the final assessments. The high volume of the data that needs to be

collected can also negatively impact the quality of the analysis. For many of these roadway assets,

the accurate records of locations and the most updated status are either unavailable or incomplete.

Frequent reporting of up-to-date status of these assets can help practitioners in their decision makings

to improve their condition and/or help avoid damages for further analysis and condition assessment

purposes.

To address current limitations, this research presents a non-parametric image parsing method

for segmentation and recognition of roadway assets from 2D car-mounted video streams. The

proposed non-parametric video-based segmentation method can easily and efficiently segment and

recognize roadway assets from video streams and labels image region with their categories of

roadway assets.

The method can be easily scaled to thousands of video frames captured during data collection, and

does not need training. Instead, it retrieves a set of most relevant video frames (e.g. highway vs.

secondary road) which serve as candidates for superpixel-level annotation. Using a fast graph-based

segmentation algorithm, superpixels are then obtained from each video frame and their visual

characteristics are computed using a histogram of different shape, appearance, and color descriptors.

Based on a Nave Bayes assumption, a likelihood ratio score is obtained for each superpixel and an

asset label that maximizes the ratio is assigned. Given a video frame to be interpreted, the algorithm

performs global matching against the training set, followed by superpixel-level matching and efficient

Markov Random Field (MRF) optimization for incorporating neighborhood context and two types of

labels: 1) semantic (e.g. guardrail) and 2) geometric (e.g. horizontal) are simultaneously assigned to

the superpixels.

Experimental results are presented on testing the proposed method along the U.S. Interstate

I-57. The inspection vehicle can travel and collect videos at highway speeds. There are five cameras

including three front view, one rear view and one down shot for pavement view that can capture images

at a rate of 200 images per view per mile. The results with an average accuracy of 88.24% for

recognition and 82.02% for segmentation of 8 types of asset categories reflect the promise of

applicability of the proposed approach that the local visual features provide acceptable performance,

while the method overall does not require any significant supervised training.

The contribution of this research is the video-based parsing and segmentation methods that

can leverage motion cues and temporal consistency to improve the performance of 3D roadway assets

recognition. This scalable method has potential to reduce the time and effort required for developing

road inventories, especially for those such as guardrails, and traffic lights that are not typically

considered in 2D asset recognition methods.



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69. Integrated Computational Model in Support Of Value Engineering

Yalda Ranjbaran ([email protected]), Advisor: Dr. Osama Moselhi


Value Engineering (VE) is frequently applied to construction projects for better recognition of project

scope and for elimination of unnecessary cost without impacting the functional requirements of

individual components of constructed facilities. A critical phase in the application of value engineering

is the multi-attributed evaluation of generated alternatives in the speculative phase. Cost is an

essential criterion that plays an important role in the selection of the optimum or near optimum

alternative that guarantees best value based on the criteria used in this process. Limited work has

been carried out for automation of this process but yet without adequate visualization for the

components being considered.

The main objective of the current research is to propose an integrated model for building

construction that provides professionals, owners and members of VE teams with automation

capabilities to evaluate and compare different design alternatives of project components using multi-

attributed criteria as well as integrating that model with visualization capabilities to assist designers

and stakeholders in making related decisions. A set of tools and techniques have been integrated in

this decision making model in order to assess several alternatives and support designers/owners in

making value driven selection among generated alternatives.

The methodology is to develop a multi-attributed decision environment applying the Analytic

Hierarchy Process (AHP) to evaluate competing alternatives. A BIM model, allowing 4D presentation

of the project alternatives is implemented in the proposed model to automate data extraction for project

cost estimating and to facilitate and support the visualization capabilities. The output which has been

classified based on Uniformat division would be linked to the model. The model is expected to assist

members of VE teams not only in costing each alternative being considered, but also in ranking

competing alternative using multi-attributed criteria in a timely manner. The report can always be

tracked and modified using the automated model.

A prototype model that integrates the project BIM model with RSMeans cost data and AHP

has been developed. Cost estimates are generated making use of direct link with RSMeans and the

ranking of alternatives is performed using the Analytic Hierarchy Process. The developed model allows

users to specify different evaluation criteria for each group of project components. The model has

been applied to a case project to demonstrate its use and capabilities. The model evaluates and ranks

generated alternatives in its output report.

Large buildings projects require commitments of considerable large resources and the

application of models such as that developed in this research can be of help in developing better

understanding and appreciation of project scope of work and in reducing unnecessary cost without

impacting the required functions of projects components being considered.



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70. Improving Campus Building Energy Efficiency and Occupants Satisfaction through

Application of Artificial Intelligence into Campus Facility Management

Yang Cao ([email protected]), Advisor: Dr. Xinyi Song


With the concept of sustainability continues to grow in importance and prominence, more researches

have been focused on improving energy efficiency during a building’s operation phase. However, the

significant challenge is how to improve energy efficiency while considering productivity and profitability

with limited resources, budget pressures and tight schedules. Academic settings, such as university

facilities, also pose other unique set of problems to facility managers and administrators. Unlike

commercial, residential or industrial buildings, campus facilities are composed of different building

types with different requirements for indoor air quality, humidity, temperature and ventilation from

continuously changing occupants. The current practice with campus facility management (FM)

requires facility managers to manually schedule tasks, often based on the order of incoming requests

without considering their impact on building energy consumption.

To change this situation, authors conducted a series of researches on applying artificial

intelligence (AI) into campus facility management. First, the proposed agent based framework can

facilitate system-wide decision making for facility managers. The framework was developed to help

facility managers analyze and prioritize tasks according to factors such as degree of emergency,

energy impact, and occupant satisfaction level, etc. The Anylogic software with self-defined java

helped to build the interactive decision framework.

Moreover, AI helped to build a knowledge database for FM, which included two main parts:

basic information on common daily work request and work instructions; and also the impact on building

energy performance. The case based reasoning (CBR) was achieved through text mining. CBR could

help facility managers to retrieve the historical cases and then get the instructions and make analysis

based on past experience. It could be combined with the ABM to better analyze and prioritize future

work requests based on factors such as safety, energy consumption impact, occupant satisfaction,

etc. A case study was conducted on campus to validate the system with the focus on HVAC tasks.

The preliminary result was the implementation of searching with simple FM text. With the foundation

of these works and future endeavors, the traditional manual FM work will get much higher working

productivity while improving energy efficiency and occupants’ satisfaction.



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71. A Bio-Inspired Solution to Mitigate Urban Heat Island Effects

Yilong Han ([email protected]), Advisor: Dr. John E. Taylor

Virginia Tech

Over the last decade, rapidly growing world energy consumption is leading to supply difficulties,

exhaustion of fossil energy resources, and global environmental deterioration. Contributing to these

trends, 32% of total final energy consumption and nearly 40% of primary energy consumption are

attributable to buildings. Urbanization is escalating these trends with tighter building spatial

interrelationships. It is intensifying building energy consumption due to the mutual impact of buildings

on each other and, as a result, exacerbating Urban Heat Island (UHI) effects.

In this research, we attempted to seek solutions to this significant engineering issue from

nature, and discovered a similar heat island effect in flowers, namely the “micro-greenhouse effect”.

Although warmer intrafloral areas have evolved for botanic productive success, a cooling effect has

been observed in a peculiar temperate flower—Galanthus nivalis—which generates cooler intrafloral

temperatures. Our research objective is to study the special reflectance property of this flower’s shiny

petals, which has been suggested as a possible contributor to this cooling effect, and develop a bio-

inspired reflective pattern for building envelopes.

We designed a macro-level pattern with retro-reflective arrays, and conducted energy

simulation of a network of buildings in EnergyPlus in eight different geographical locations. We first

analyzed the temperature variations of the diffusive building surface of the control building when our

proposed retro-reflective facade is applied to its neighboring surface. We then analyzed how the

energy performance of the control building was affected by its surrounding network buildings with

different building façades.

We found that building surface temperatures dropped considerably when neighboring buildings were

retrofitted with our retro-reflective façade surface. This resulted in less solar radiation being reflected

from the surrounding network buildings, and therefore, mutual reflection was reduced. The results also

demonstrated that total energy consumption by HVAC systems and cooling energy consumption were

reduced by up to 4.4% and 6.6%, respectively, in different metropolitan areas.

The study demonstrates that a bio-inspired cubic-corner-like retro-reflective façade can reduce

inter-building effects, and, as a result; (1) lessen the reflected heat of solar radiation in spatially-

proximal buildings leading to reduced UHI, and (2) reduce the energy required for cooling and,

therefore, energy consumption.


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72. Forecasting Long-Term Staffing Requirements for State Transportation Agencies

Ying Li ([email protected]), Advisor: Dr. Timothy R. B. Taylor

University of Kentucky

The US transportation system is vital to the nation’s economic growth and stability, as it provides

mobility for commuters while supporting US ability to compete in an increasingly competitive global

economy. State Transportation Agencies across the country continue to face many challenges to

repair and enhance roadway infrastructure to meet the rapid increasing transportation needs. One of

these challenges is the selection of agency staff. With a large number of employees retiring from the

transportation workforce and deceasing interest in a career in transportation engineering among young

people, State Transportation Agencies have to make every effort to attract potential employees.

Various workforce development programs are designed to prepare staffing pool for 10-15 years into

the future. In order to effectively plan for future staffing levels, State Transportation Agencies need a

method for forecasting long term staffing requirements. However, current methods in use cannot

function without well-defined projects and therefore making long term forecasts is difficult.

The current work seeks to answer the question: How do factors like transportation system

demand, current transportation system performance, funding, and staffing strategies impact staffing

level requirements at State Transportation Agencies? To be more specific, the current work seeks to

identify: 1) what feedback structures link future transportation system demand, current system

performance, funding, staffing strategy and future staffing level requirements; 2) what are the main

drivers and constraints that determine future staffing levels and how these drivers and constraints

impact State Transportation Agencies’ staffing strategies; 3) how strategy developers can effectively

address potential staffing shortages and overflows in State Transportation Agencies.

System Dynamics modeling methodology will be used to conduct the current research. The

researchers are collecting data through literature review, survey and interviews with State

Transportation Agency personnel to identify how various factors impact long term staffing needs. Data

collected will be used to construct a system dynamics model which maps out causal links and feedback

structures within the system. Once the model is empirically tested and validated, the model will be

able to predict long term trends of future staffing needs as well as run simulations to reflect different

staffing strategies.

The authors are currently in the process of constructing the model. Some preliminary findings

include: (1) State Transportation Agencies are managing larger roadway systems with fewer in-house

staff than they were 10 years ago; (2) Outsourcing is becoming more common mainly due to limited

availability of qualified in-house personnel; (3) At this moment, the adoption of mobile information

technology within State Transportation Agencies appears to be limited and the impact of those

technologies on staff efficiency is also limited.

The system dynamics model being developed for the proposed research will hopefully fill in

the blank of long term forecasting method for transportation workforce needs. The model will provide

insights on long term trends and fluctuations in transportation workforce needs. State Transportation

Agencies may benefit from the proposed research by gaining knowledge about the system that impact

staffing requirements in order to effectively develop staffing strategies. Researchers and engineers in

other disciplines may modify the model to forecast work volumes and staffing needs for other industries.



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73. Vision-based Building Energy Diagnostics and Retrofit Analysis using 3D

Thermography and BIM

Youngjib Ham ([email protected]), Advisor: Dr. Mani Golparvar-Fard


Accurate quantification of the energy cost savings associated with retrofitting performance problems in existing buildings can minimize the financial risks in retrofit investments. Nevertheless, the industry continues to face several technical challenges in identifying potential areas in building envelopes for retrofit and providing recommendations based on sound cost-benefit analysis of the retrofit alternatives. To address the current needs in energy diagnostics and retrofit decision-makings, infrared thermography and BIM-based energy analysis tools (e.g. EnergyPlus) are being widely used. However, current practices of manually interpreting large amounts of visual thermal data and leveraging as-designed building conditions declared in industry standard databases in the current BIM-authoring tools often lead to subjective and inaccurate assessments. For existing buildings, without considering the diminishing thermal properties of building elements caused by deteriorations and updating the associated material properties in BIM, the results from BIM-based energy analysis will not be trustworthy. This research aims to create and validate an easy-to-use tool and automated methods based on 3D thermography and BIM to support reliable cost-benefit analysis of building energy efficiency retrofits and improve the reliability of BIM-based building energy performance analysis. First, by using a consumer-level hand-held thermal camera, practitioners collect large numbers of unordered thermal images from building environments under inspection. Then, using a new computer vision based algorithm, 3D thermal models are generated wherein actual surface temperatures are modeled at the level of 3D points. 1) Building areas with potential thermal deteriorations are detected by comparing the actual measurements with the energy performance benchmark resulting from a numerical analysis. By using 3D thermal distribution and environmental assumptions that the indoor heat transfer is attributed to thermal convection and radiation under a quasi-steady-state condition, actual thermal resistances (R-value) are calculated at the level of 3D points. Then, based on the ‘degree days’ data, we estimate energy saving costs when thermal resistance of defective areas are increased to their recommended level. 2) We automatically map the actual thermal resistances at the level of 3D points to their corresponding BIM elements in gbXML schema. This is done by discretizing building elements in BIM into a mesh and using the nearest neighbor searching algorithm. We then derive a single actual R-value for each building element, and automatically update the corresponding entry for the thermal resistance in the as-designed BIM. The outcome can be used as an input of BIM-based energy analysis tools for more accurate analysis. We have conducted several experiments on two real-world residential and instructional buildings in Virginia and four hypothetical cases in Minnesota and Florida. Our findings on the difference between the actual thermal resistance measurements and the notional values declared in standard methods such as ISO 6946 were about 10%. Our experimental results for cost-benefit analysis show that the proposed method can reliably estimate ROI associated with retrofitting thermal performance problems and has potential to improve today’s practices of financial feasibility analysis on building retrofits. Also, by shortening the existing gaps in knowledge about energy performance modeling between the architectural information in the as-designed BIM and the actual building conditions, this research enables reliable BIM-based energy performance analysis. The primary scientific contributions are as follows: 1) an automated method for comparing actual and expected building energy performance in 3D and analyzing the deviations to detect potential performance problems; 2) a method for measuring actual thermal resistances of building assemblies in 3D; 3) a method for automated association of actual thermal property measurements to building elements in BIM; and 4) an automated method for updating their corresponding thermal properties in gbXML schema of BIM. Over the next 30 years, about 150 billion S.F. (roughly half of the U.S. building stock) will require retrofit to meet the new rigorous energy standards. Non-compliance with the new energy standards is not limited to existing buildings. About a quarter of the newly constructed and certified buildings do not also save as much energy as their designs had originally predicted. Construction companies can leverage the findings of this research and the developed tools to create new workflows for building commissioning –particularly for LEED certified buildings– and also new workflows for energy efficiency retrofit assessment processes.



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74. Multi-Tiered Selection of Project Delivery Systems for Capital Projects

Zorana Popić ([email protected]), Advisor: Dr. Osama Moselhi


In this research, a decision support system (DSS) for selecting most suitable project delivery systems

(PDSs) for capital projects is proposed. Project delivery systems continue to evolve, to meet

challenging project objectives. Selecting a PDS is an early project decision, which can greatly affect

the project execution process and its outcomes. Existing methods for PDS selection do not consider

all the important recent developments in project delivery, including Integrated Project Delivery (IPD)

and Public-private Partnership (PPP), within a single comprehensive decision support system. The

objective of this research is to develop a decision support system for selecting the most suitable project

delivery systems for capital projects, which ranks the available PDS alternatives in order of suitability.

Research method includes an in-depth analysis of 15 case studies of projects constructed in the USA

and 207 projects in Canada, which utilized PPP delivery methods. The selection criteria were

developed utilizing related literature and the findings of the analysis of the case studies. As a result,

the proposed DSS encompasses a multi-tiered process. It operates in two distinct modes; elimination,

first, to narrow the search field, and ranking, second, to find the most suitable delivery method. In the

first mode, the suitability of PPP is identified and a number of PDSs are eliminated based on a set of

key project characteristics. In the second mode, evaluation and ranking of the remaining PDSs are

performed using multi-attributed decision method. The decision maker provides project-specific inputs

including project and owner characteristics, and judgments regarding the importance of specific

evaluation and selection criteria. The multi-attributed decision method (MADM) model utilizes relative

effectiveness values (REVs) of PDSs in the evaluation process. These values build upon those

developed by the Construction Industry Institute (CII, 2003) to account for PDSs and selection factors

beyond those considered in the CII study. Three case projects were analyzed using the proposed DSS,

including one private sector project and two public sector projects. In two of the three cases, the

selected PDS was recently developed integrated project delivery (IPD). The principal contributions of

this research consist in providing a decision method which introduces and makes available newly

developed PDSs, including IPD and the family of PPPs, as well as the criteria for PDS selection which

take into account current tendencies in the construction industry. The proposed DSS defines specific

screening criteria, to eliminate from further consideration non-applicable or impractical alternatives, so

that detailed evaluation can be concentrated on the most relevant alternatives. Hierarchy and network

structures for application of analytical hierarchy process (AHP) and analytical network process (ANP)

have been developed, which incorporate 67 factors for selecting among non-PPP alternatives and 61

factors for selecting among PPP alternatives. Preliminary relative effectiveness values for 16 non-PPP

alternatives with respect to the 67 selection factors are proposed. An automated software tool was

developed to facilitate the use of proposed DSS. The proposed DSS is intended for decision makers

of owner organizations, and their consultants, who seek a rational, knowledge-based approach to PDS

selection decision.


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