USE OF AUGMENTED REALITY TECHNOLOGY TO ENHANCE...
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1 USE OF AUGMENTED REALITY TECHNOLOGY TO ENHANCE COMPREHENSION OF STEEL STRUCTURES CONSTRUCTION By FOPEFOLUWA BADEMOSI A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN CONSTRUCTION MANAGEMENT UNIVERSITY OF FLORIDA 2016
Transcript of USE OF AUGMENTED REALITY TECHNOLOGY TO ENHANCE...
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USE OF AUGMENTED REALITY TECHNOLOGY TO ENHANCE COMPREHENSION OF STEEL STRUCTURES CONSTRUCTION
By
FOPEFOLUWA BADEMOSI
A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE IN CONSTRUCTION MANAGEMENT
UNIVERSITY OF FLORIDA
2016
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© 2016 Fopefoluwa Bademosi
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To my family and friends
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ACKNOWLEDGMENTS
First and foremost, I give thanks to the Almighty God for the strength to complete
this study. I would like to express the deepest appreciation to my advisor and thesis
committee chair, Dr. Issa for his continual guidance throughout the process of this
research. I would like to acknowledge the support received from my thesis committee
members, Dr. Muszynski and Dr. Gheisari. I would also like to acknowledge the help
and guidance I received from Hamzah Shanbari and Nathan Blinn during this project.
I want to extend my gratitude to the students who participated in this study,
faculty members and staff of the Rinker School of Construction Management, University
of Florida. Finally, I would like to thank my family and friends for their encouragement
and support in all I have been able to accomplish so far.
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TABLE OF CONTENTS page
ACKNOWLEDGMENTS .................................................................................................. 4
LIST OF TABLES ............................................................................................................ 8
LIST OF FIGURES .......................................................................................................... 9
LIST OF ABBREVIATIONS ........................................................................................... 10
ABSTRACT ................................................................................................................... 11
CHAPTER
1 INTRODUCTION .................................................................................................... 13
Purpose of Study .................................................................................................... 13 Objective of the Study ............................................................................................. 14
Research Hypothesis .............................................................................................. 14 Research Methodology ........................................................................................... 15 Scope and Limitation .............................................................................................. 15
Research Organization ........................................................................................... 16
2 LITERATURE OVERVIEW ..................................................................................... 18
Overview ................................................................................................................. 18
Augmented Reality ................................................................................................. 19
Definition .......................................................................................................... 19 Augmented Reality and Virtual Reality ............................................................. 20 Augmented Reality Technology ........................................................................ 21
Historical Background ...................................................................................... 22 Brief history of AR and recent developments ............................................. 22
AR System Technologies ................................................................................. 25 The processing device ............................................................................... 25 The visualization device ............................................................................. 26
The positioning device ............................................................................... 26 AR Enabling Technologies ............................................................................... 27
Displays ..................................................................................................... 27
Tracking and registration............................................................................ 28
Calibration .................................................................................................. 28 Applications of Augmented Reality ................................................................... 29 Limitations of AR Technology ........................................................................... 31
ART in Education .................................................................................................... 33 Applications of AR in Education ....................................................................... 33
Available Tools for AR Applications in Education ............................................. 35 Potential Benefits of AR to Education ............................................................... 36
ART in the Construction Industry ............................................................................ 37
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Applications of ART in the Construction Industry ............................................. 37 Potential Benefits of AR to the Construction Industry ....................................... 39
AR in Construction Management Education ........................................................... 40
Challenges of Construction Management Education ........................................ 40 ART as an Educational Tool ............................................................................. 41
3 METHODOLOGY ................................................................................................... 43
Overview ................................................................................................................. 43 Survey Questionnaire Design ................................................................................. 43
Demographic and Background Questionnaire .................................................. 44 Problem Solving Skills Questionnaire ............................................................... 46
Sample Population .................................................................................................. 48
Augmented Reality Test Case ................................................................................ 49 Selected Sample Project .................................................................................. 49 Steel Construction ............................................................................................ 52
Augmented Procedures .......................................................................................... 52 Augmentation Procedure .................................................................................. 52
Steel Component Augmentation ....................................................................... 54 Experimental Procedures ........................................................................................ 57 Method of Analysis ................................................................................................. 59
4 SURVEY RESULTS................................................................................................ 61
Demographic and Background Survey Results ...................................................... 61
Question DB-1: What is your age? ................................................................... 61
Question DB-2: Sex .......................................................................................... 62
Question DB-3: Have You Been a United States Resident for the Last 10 Years? ........................................................................................................... 62
Question DB-4: Are You Concurrently Enrolled in an Academic Degree Program? ...................................................................................................... 63
Question DB-5: What is your Current Classification Level in the BSCM program? ....................................................................................................... 63
Question DB-6: Have You Visited Construction Sites? .................................... 64 Question DB-7: Have You Worked in any Capacity in the Construction
Industry? ....................................................................................................... 65 Problem Solving Skills Survey Results ................................................................... 68
Question PS-1: Main Elements of Structural Steel Assembly........................... 68
Question PS-2: Possible Tasks Required to Build the Structural Steel Assembly ....................................................................................................... 71
Question PS-3: Installation Sequence of Tasks Required to Build the Structural Steel Assembly ............................................................................. 74
Question PS-4: Tasks that can be Going On in Parallel ................................... 78 Question PS-5: Recommendations to Improve the Efficiency of the
Construction Process .................................................................................... 80
5 CONCLUSIONS AND RECOMMENDATIONS ....................................................... 83
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Conclusions ............................................................................................................ 83 Results to Investigation Objectives ......................................................................... 85
Objective 1: Investigate the current use of ART in the construction industry .... 86
Objective 2: Assess the current use of ART in education ................................. 86 Objective 3: Assess the current use of ART in construction management
education....................................................................................................... 86 Objective 4: Determine the effectiveness of ART in the comprehension of
the use and erection of steel components among construction management students ................................................................................... 86
Improvements to the Survey ................................................................................... 87 Recommendations for Future Research ................................................................. 87
LIST OF REFERENCES ............................................................................................... 89
BIOGRAPHICAL SKETCH ............................................................................................ 92
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LIST OF TABLES
Table page 3-1 Group designations and associated information streams ................................... 58
4-1 Age of study participants .................................................................................... 62
4-2 Sex of study participants .................................................................................... 62
4-3 Residency status of study participants ............................................................... 63
4-4 Academic program of study participants ............................................................. 63
4-5 Classification level of study participants ............................................................. 63
4-6 Participants who have visited construction sites ................................................. 64
4-7 Nature visit to construction sites ......................................................................... 64
4-8 Number of times study participants have visited construction sites .................... 64
4-9 Work experience of study participants ................................................................ 66
4-10 Length of work experience ................................................................................. 66
4-11 Percentage of time spent on tasks performed .................................................... 67
4-12 Test results for difference in element identification ............................................. 71
4-13 Test results for difference in task identification ................................................... 74
4-14 Test results for difference in task sequencing .................................................... 77
4-15 Tasks that can occur in parallel .......................................................................... 78
4-16 Recommendations on how the structural steel assembly process can be efficiently improved ............................................................................................. 80
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LIST OF FIGURES
Figure page 2-1 Reality-Virtuality (VR) Continuum ....................................................................... 21
2-2 History of Augmented Reality ............................................................................. 24
3-1 Overall construction progress image as of August 2014 captured via an unmanned aircraft ............................................................................................... 51
3-2 Example of project documentation, structural steel assembly ............................ 52
3-3 Structural steel column augmentation over on-site construction progress documentation .................................................................................................... 54
3-4 Augmentation of concrete foundation footings over existing as-built site conditions ........................................................................................................... 55
3-5 Augmentation of steel structure over existing as-built site conditions ................. 56
3-6 Captured image of the installed metal decking ................................................... 56
3-7 Research methodology ....................................................................................... 60
4-1 Construction sites visits ...................................................................................... 65
4-2 Construction work experience ............................................................................ 67
4-3 Number of observations and sample proportions of structural steel elements ... 69
4-4 Number of observations and sample proportions of possible tasks .................... 72
4-5 Number of observations and sample proportions of installation sequence ......... 75
4-6 Tasks that can occur in parallel .......................................................................... 79
4-7 Recommendations on improving the structural steel assembly process ............ 82
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LIST OF ABBREVIATIONS
AEC
AR
Architecture/Engineering/Construction
Augmented Reality
ART
CM
GPS
HMD
Augmented Reality Technology
Construction Management
Global Positioning System
Head-Mounted Display
UAS Unmanned Aircraft System
VR
Virtual Reality
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Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Science in Construction Management
USE OF AUGMENTED REALITY TECHNOLOGY TO ENHANCE COMPREHENSION OF STEEL STRUCTURES CONSTRUCTION
By
Fopefoluwa Bademosi
August 2016
Chair: R. Raymond Issa Major: Construction Management
The future of the construction industry is highly dependent on the competence of
new employees. Therefore, it is very important for new employees to go into the labor
market after years of educational training in colleges and universities around the world
with the abilities required to resolve the intricate complications ingrained in the
construction process. However, the inadequate exposure of Construction Management
students to construction processes and procedures on the job-site is detrimental to their
abilities to solve problems. The result is a minimal comprehension of the spatial and
temporal constraints which exist during the process of construction, limiting the
productive level of students.
Advanced teaching techniques that can provide greater insight to students are
needed to enhance the educational experience of construction management students.
One of these new methods of teaching is showing real time videos that highlight the
various elements of importance in the classroom lecture, thereby dispensing a more
effective learning experience. This study uses Augmented Reality Technology (ART)
and a layer of artificial visualizations to simulate the environmental context and spatio-
temporal constraints. Augmented Reality (AR) is an emerging technology in which the
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real-world is amplified by computer-generated content linked to specific locations and
activities. The superimposition of images on real time videos function as an instructional
technique to virtually incorporate jobsite experiences in the classroom. The assumption
is that enhancing the spatio-temporal constraints present will enable learners to
visualize context and hidden processes.
Therefore, through the combination of the ability of the learners to understand the
complexity of construction products and associated jobsite processes by using the real
environment augmented with computer-generated information layers, a significant
enhancement of their perception of reality is expected. In preparation, this research
presents an overview of AR, examines recent AR developments, explores the impact of
AR on the construction industry and construction education, and evaluates the impact of
AR on learning in construction management education.
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CHAPTER 1 INTRODUCTION
Purpose of Study
It is very important for new employees to go into the labor market after years of
educational training in institutions of higher learning around the globe with the abilities
required to resolve the intricate complications ingrained in the construction process. The
future of the construction industry is highly dependent on the competence of these new
employees. However, the prevalent situation in institutions of higher learning reveals an
inadequate exposure of students to many construction processes and procedures,
resulting in a minimal comprehension of the spatial and temporal constraints which exist
during the process of construction. Teaching techniques that incorporate site visits and
in-class media presentations are usually implemented with the aim of rectifying this lack
of exposure. Although these techniques may provide some understanding, sole
dependence on them would fail to deliver the contextual details required to fully grasp
the complex nature of construction projects. The resulting lack of experience and
understanding renders the students inadequately equipped for the workforce.
The evolution of Augmented Reality Technology (ART) enables the deployment
of advanced teaching techniques that can be used to provide greater insight to
students, such as integrating site visits in the classroom, thereby dispensing a more
effective learning experience. A literature review discussing the current use of ART in
education and the construction industry is found in Chapter 2. Ensuing the conclusion of
the literature review, it was established that a significant amount of work still has to be
done preparatory to enabling the prevalent use of ART in enhancing the educational
experiences of students. This study therefore focuses on discovering the possible
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advantages of incorporating ART with traditional teaching techniques and how its use
can be optimized in construction management education.
Objective of the Study
The aim of this research is to determine whether the use of ART can enhance
the educational experience of construction management students using the steel
structure assembly process. The specific objectives of the study are: 1) to investigate
the current use of ART in the construction industry; 2) to assess the current use of ART
in education; 3) to assess the current use of ART in construction management
education; and 4) to determine the effectiveness of ART in the comprehension of the
use and erection of steel components among construction management students.
In order to acquire fundamental information, an extensive review of literature was
carried out to assess the current use of ART in the construction industry and education.
Learning assessments were carried out through the implementation of an established
test case and the use of visual documentation to determine the students’ understanding
of spatial and temporal constraints, as related to the assembly of structural steel
components and the construction process. Through the use of descriptive statistics, the
data was analyzed to determine whether ART can enhance the comprehension of the
assembly of structural steel components for construction management students.
Research Hypothesis
The primary goal of the study is to determine whether the use of ART can
enhance the educational experience of construction management students and their
comprehension of the spatial and temporal constraints which exist in construction
projects. The specific assembly focused upon in this study is structural steel
components and their erection process. Therefore, the study seeks to answer whether
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the use of ART will enhance the comprehension of the use and erection of steel
structural components among construction management students, which is the
hypothesis to be tested.
Research Methodology
A study which involved students enrolled in an Estimating 1 course at the
University of Florida was conducted as a means of determining the effectiveness of
ART in enhancing the comprehension of the assembly of structural steel components
among construction management students. The study was conducted in two phases,
Phase 1 (pre-learning test) and Phase 2 (post-learning test), and the participants were
divided into three testing groups, classified as Groups A, B, and C, through random
selection. A complete structural steel construction video with augmentation was
developed and was used in different combinations with the standard lectures on the
subject of structural steel estimating in order to achieve an effective means of data
analysis upon the conclusion of the study. Phases 1 and 2 were developed to
accurately assess the participants’ base knowledge of the subject matter and then
assess the impact of the various instructional tools used. The collected data resulted in
the necessary quantitative data required to analyze the different abilities of the
participants in comprehending the assembly of structural steel components based on
the testing groups. Comparisons of the results to the literature review findings were
made, and conclusions and recommendations are presented.
Scope and Limitation
The study focused on assessing the use of ART in enhancing the educational
experience of construction management students. The study discusses the use of ART
and attempts to investigate its possible advantages, more specifically to education and
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the construction industry. It contains information on ART, its application, recent
development and trends, and the challenges it solves in construction management
education.
For the purpose of this study, only the understanding of structural steel assembly
process among construction management students enrolled in the Rinker School of
Construction Management at the University of Florida was evaluated before and after
the introduction of augmented reality visualizations. For future research studies,
students from other colleges can be sampled and other construction related subject
matters can be examined.
Research Organization
Chapter 2 provides a literature review on ART and its current use in the
construction industry and education. The literature review directly defines augmented
reality and reviews its variety of applications in design, construction and education.
Chapter 3 describes the methodology followed in conducting this research. Two
sets of questionnaires were used for this study, a demographic and background
questionnaire and a problem solving skills questionnaire. The demographic and
background questionnaire consisted of seven questions, while the problem solving skills
questionnaire consisted of five questions. The participants in the study were
undergraduate construction management students enrolled in an Estimating 1 course at
the University of Florida.
Chapter 4 provides the overall analysis of the results derived from the
experimental procedure and learning assessment from the survey of the population
sampled. Comparisons of the results to the findings in the literature review are made
and the hypothesis was tested and discussed.
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The conclusions and recommendations drawn from the analyses conducted are
found in Chapter 5. Also, recommendations for future research are presented in this
final chapter.
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CHAPTER 2 LITERATURE OVERVIEW
Overview
This literature review consists of five sections relating to the progression of
augmented reality technology (ART) and construction and education operations. Each
section addresses the current knowledge of both applications within the AEC industry,
by looking at: 1) ART and its evolution over the years; 2) application of ART in
education; 3) application of ART in the construction industry; 4) application of ART in
construction management education; and, 5) the future of ART in the construction
industry and education.
The first section of this literature review briefly follows definitions and presents a
historical background of ART. A summary of current available ART system technologies
is provided as well as descriptions of the applications and limitations of these systems.
The second section examines the concept of ART in education and all the existing
applications of AR in education are briefly described. Several tools that can be used to
facilitate AR in education are briefly discussed. The third gives an overview of the
applications and benefits of ART in the construction industry. The fourth section
explores the challenges faced in construction management education, as well as
possible solutions to these problems through the implementation of ART. The final
section of the literature review concludes with a brief explanation of future
developments and applications of ART in education and the construction industry.
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Augmented Reality
Definition
Augmented Reality (AR) describes a vast array of technologies that project
information generated by computers, such as text, images and videos, onto the user’s
recognition of the real-world. Simply put, AR is a discipline that merges the real-world,
computer generated (virtual) world and computer generated data (Izkara et al. 2007). In
the virtual world, the user is immersed in an entirely simulated reality without having any
connection with the immediate real-world. AR enables the user to see the real-world
augmented with computer generated information, essentially allowing the user to
perceive the real and virtual objects as coexisting in the same space (Krevelen and
Poelman 2010). AR is an unfolding technology in the field of virtual reality (VR) and it is
observed to have gained considerable relevance as an area of research and
development to an increasing extent (Yuen et al. 2011).
At the outset, AR was defined by researchers in terms that restrict the concept to
particular display technologies, such as a head-mounted displays (HMDs), or to the
sense of sight. However, research has refuted these conceptions as AR has and can
inherently be applied to all known senses (Krevelen and Poelman 2010). These
definitions are considered to be too simplistic for a field that is perpetually advancing
and expanding. According to Yuen et al. (2011), some of the widely accepted definitions
of AR are as follows:
AR Systems are those which combine real and computer generated information in a real environment, interactively and in real time, and [which align] virtual objects with physical ones. (Höllerer and Feiner 2004)
AR is the human-computer-interaction, which adds virtual objects to real senses that are provided by a video camera in real time. (Ludwig and Reimann 2005)
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AR is technology which allows computer generated imagery to exactly overlay physical objects in real time. (Zhou et al. 2008)
Azuma et al. (2001) have defined the engineering of AR based on three
characteristics:
combines real-world and virtual elements in a real environment;
aligns real and virtual objects with each other in real time; and
runs interactively, both in three dimensions and in real time. Finally, Krevelen and Poelman (2010) also defined AR as removing real objects
by overlaying virtual ones, which is also known as the as the technique called mediated
or diminished reality.
Augmented Reality and Virtual Reality
Augmented reality is only a part of the general area of mixed reality. There are
four types of environments typically factored into the range of technologies developed to
modify, augment, interact with, or replace our perceptions of reality (Milgram et al.
1994). The first environment is the real-world, which the users are well accustomed to.
The second environment is the virtual world, also known as VR, which is at the opposite
margin. In the virtual environment, all the information perceived by the user is generated
by the computer and is most oftentimes entirely independent of the locations, objects or
activities in the real environment. Two types of augmented environments exist in the
middle of these two extreme environments mentioned: augmented reality (AR) and
augmented virtuality (AV). In AR, computer generated contents and information are
superimposed on the real environment, while AV superimposes real-world data onto a
computer generated world. Figure 2-1 shows the reality-virtuality (MR) spectrum put
forward by Milgram et al. (1994).
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AR is strongly tied to VR in the sense that AR was developed as a variation of
VR; both environments are interactive, immersive, and include information sensitivity.
However, the dominant perception in AR is the real-world, which is improved by digital
intelligence, whereas in AV is a system of immersive virtual environment with added
real-world imagery. Nonetheless, both virtual environments are totally simulated by
rapidly advancing technologies, which may possibly result in a situation where elements
in the virtual and real-world may become more difficult to differentiate (Yuen et al.
2011). Linden Lab’s Second Life is the best known example of VR, gaming consoles
like Nintendo Wii, PlayStation 3 and Xbox 360 are examples of AV, while smartphone
apps that make use of global positioning system (GPS) data are examples of AR.
Figure 2-1. Reality-Virtuality (VR) Continuum (adapted from Milgram et al. 1994)
Augmented Reality Technology
The contents of AR can be observed through several available media, some of
which include quick response (QR) codes and head mounted displays (HMDs). Images
can be viewed as digital content on computers with webcams with the use of QR codes
and users wearing HMDs can see digital content on the HMD screen, as well as their
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real environment through the screen. However, many AR applications are currently
location based and demand the presence of various essential tools before they can be
utilized on smartphones and mobile devices. These required tools include GPS
technology, an accelerometer, and a digital compass, also known as a magnetometer.
By using AR applications, smartphone cameras enable users to observe the world by
facilitating the realization of digital content integrated with the real environment (Yuen et
al. 2011).
Historical Background
According to Krevelen and Poelman (2010), the prospect of a technology that
enables you see beyond what others see, hear beyond what is deemed expected, and
possibly touch, smell and taste what others cannot, sparked the interests of researchers
in AR. ARTs are progressively being adopted to enhance the perception of
environments in improved and better ways, with the hopes of a widespread adoption in
the nearest future. AR has exhibited great potentials in improving productivity in real-
world tasks as well as support in several fields such as education, maintenance, design
and inspection. Also, AR is a new field of research with many challenges, however a
great deal of progress has been recorded lately. Because of these, researchers
continue to investigate AR.
Brief history of AR and recent developments
Research and development in the field of AR have continued for the past four
decades (see Figure 2-2). According to researchers (Azuma et. al 2001; Billinghurst
and Henrysson 2009; Krevelen and Poelman 2010), the beginnings of AR dates back to
the appearance of Ivan Sutherland’s work in the 1990s. Sutherland and his students at
Harvard University and the University of Utah used a see-through HMD to present 3D
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graphics. Research on AR continued in the 1970s and 1980s by a small group of
researchers at U.S. Air Force’s Armstrong Laboratory, the NASA Ames Research
Center, the Massachusetts Institute of Technology, and the University of North Carolina
at Chapel Hill. In 1979, mobile devices like the Sony Walkman, digital watches and
personal digital organizers were launched. This introduction laid the foundation for
wearable technology in the 1990s when personal computers were designed to be small
enough to be worn around the clock (Höllerer 2004). Examples of earliest palmtop
computers include the Psion I (1984), the Apple Newton MessagePad (1993), and the
Palm Pilot (1996). Presently, innumerable mobile platforms that may support AR exist,
such as tablet PCs, and smartphones.
The term “augmented reality” was not conceived until the early 1990s when
scientists at Boeing Corporation, Caudell and Mizell (1992) were working on the
development of an experimental AR system aimed at helping workers assemble wiring
harnesses. However, genuine mobile AR was not accomplished until a couple of years
after a GPS-based outdoor system that presents navigational assistance to the visually
impaired with spatial audio overlays was developed. Shortly after, computing and
tracking devices turned out to be adequately effective and small enough to support
graphical overlay in mobile settings. Subsequently. Höllerer and Feiner (2004)
developed an early model of a mobile AR system (MARS) that registers 3D graphical
tour guide information with buildings and artifacts seen by the visitor. By the late 1990s,
as AR became an unmistakable field of exploration, with the emergence of several
conferences on AR, including the International Workshop and Symposium on
Augmented Reality, the International Symposium on Mixed Reality, and the Designing
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Augmented Reality Environments workshop. Also, organizations such as the Mixed
Reality Systems Laboratory2 (MRLab) in Nottingham and the Arvika consortium in
Germany were formed. Furthermore, easily accessible software toolkits like ARToolKit
made it possible for AR applications to be swiftly established (Krevelen and Poelman
2010).
Figure 2-2. History of Augmented Reality (adapted from Yuen et al. 2011 and augment.com 2016)
Over the years, AR research has fundamentally centered around five core areas
essential to the delivery of AR applications: 1) techniques for tracking, 2) techniques for
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interaction, 3) calibration and registration issues, 4) developing AR applications, and 5)
display techniques (Zhou et al. 2008). In addition, other researchers have further
investigated unfolding directions for AR research, including: (a) evaluation and testing,
(b) mobile and wearable AR platforms, (c) AR authoring, (d) visualization, (e)
multimodal AR, and (f) rendering (Yuen et al. 2011).
AR System Technologies
AR systems integrate the virtual and the real-world, they are interactive in real
time, and coordinate three-dimensional items in the mixed reality. AR broadens the
abilities of the users to perceive the interaction of the real-world with virtual objects,
giving data that the users cannot recognize forthrightly with their senses. Special
gadgets are required to obtain these outcomes, an example of such device are glasses
that permit computer generated information to be superimposed over real-world images.
According to Izkara et al. (2007), an AR system comprises a group of devices with
corresponding functionalities associated and incorporated through a software platform.
There are three fundamental components of the system, from the hardware perspective:
the processing device, the visualization device and the positioning device.
The processing device
From the starting point, the processing devices used have been general purpose
laptops; however, the weight and size of these laptops do not meet the required
stipulations for an undemanding AR system. Currently there are portable computers of
diminished weight and size, making them the ideal devices for this type of applications
given the combination of computational power and size. Also, smartphones, which are
the smallest and widely used devices, can be used. As a result of the new era of 3D
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graphic chipsets, the processing and graphic capabilities of these handheld devices
have substantially increased (Izkara et al. 2007).
The visualization device
The visualization devices are responsible for registering and aligning all the
reality elements and virtual elements. According to Izkara et al. (2007), the visualization
devices can be broadly classified into two groups: video-through, and see-through. The
video-through devices are opaque devices, which require the input of a video camera in
capturing the images of the real environment. The virtual information is then overlaid
over these images, creating a combined image of made up of both the reality and virtual
data. The video-through devices are predominantly used as HMD devices. On the other
hand, the see-through devices comprise of semi-transparent screens through which the
users can observe the bordering environment. Generated digital contents are projected
on these screens, which are then integrated in both the virtual and the real-world by the
human system of vision. The most established and accessible operating systems for
mobile devices used for this purpose are Symbian, Windows Mobile and Java. The
biggest challenge these devices face is in combining the visualization of the 3D models
with reality (Izkara et al. 2007).
The positioning device
One of the main problems encountered in the applications of augmented reality is
to locate the transformation between the system of reference of the real-world and that
of the camera (Izkara et al. 2007). The positioning system operates based on images
captured through the use of a camera and the identification of those images, defining a
virtual camera that could insert digital information in the real scene.
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AR Enabling Technologies
One of the categories for new development in AR research is enabling
technologies. Enabling technologies are advances in the basic technologies needed to
build compelling AR environments. As mentioned by Azuma et al. (2001), enabling
technologies for AR include displays, tracking, and calibration.
Displays
Displays are primarily used in observing the integrated virtual and real
environments can be generally classified into three categories; 1) head-mounted
displays (HMDs), which are mounted on the head and provide users with images in
front of their eyes, 2) handheld displays, which acts as a window that shows the real
objects with an AR overlay, with flat-panel LCD displays that use an attached camera to
provide video see-through-based augmentations, and 3) projected displays, which
project the desired virtual information directly on the physical objects to be augmented
(Azuma et al. 2001).
However, the display technologies are not without a few problems. Firstly, see-
through displays lack the sufficient brightness, resolution, field of view, and contrast
required to harmoniously integrate a wide range of real and virtual images. Also, HMDs
are still somewhat weighty and bulky and typically fastened by video cabling. Be that as
it may, there have been improvements on particular issues, such as conventional optical
see-through displays, where virtual objects cannot totally obstruct real objects. Also,
most displays have fixed eye accommodation that focus the eyes at a particular
distance (Azuma et al. 2001).
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Tracking and registration
An accurate track of the viewing orientation of the users is very critical for AR
registration. Several of the available tracking systems reveal exceptional tracking for
prepared indoor environments. These tracking systems typically exercise hybrid-
tracking techniques that capitalize on the strength of the individual tracking
technologies, thereby counteracting the shortcomings of the technologies. An example
of a resilient tracking system that demonstrates precise registration is one that
combines accelerometers and video tracking. Generally, visual tracking depends on
altering the environment with fiducial markers planted at known positions. Although
some of the latest AR systems show a productive and cogent registration in prepared,
indoor environments, a lot still has to be done in tracking and calibration. Ongoing
research on tracking and calibration border around some of the following topics: sensing
the entire environment, operating in unprepared environments, minimizing latency, and
reducing calibration requirements (Azuma et al. 2001).
Calibration
As mentioned earlier, a thorough calibration is required by AR systems in order
to yield a precise registration. Required estimations and measurements for an extensive
calibration includes camera parameters, field of view, sensor offsets, object locations,
distortions, etc. However, the need for calibration can be avoided through the
implementation of some particular techniques. Such techniques include the
development of calibration-free renderers, obtaining camera focal length without an
explicit metric calibration step and auto-calibration (Azuma et al. 2001).
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Applications of Augmented Reality
Over the years, researchers and developers discover more areas that could profit
from augmentation. The initial AR systems focused on military, industrial and medical
application, however AR systems for commercial use and entertainment became
available not long after. Other areas include personal information systems, design,
assembly and maintenance, military, medical applications, entertainment, office,
education and training (Krevelen and Poelman 2010). AR technologies can be applied
to many different fields and there is no hierarchy to which it can be applied. According to
Yuen et al. (2011), AR can be applied within the following fields:
Advertising and marketing: AR has been embraced with great fervor in the
field of advertising and marketing. A variety of AR applications have been implemented
by companies on the lookout for new approaches to attract and interest prospective
buyers. For example, innovative automotive campaigns are exhibiting full-size AR virtual
cars in public areas, such as shopping centers (Yuen et al. 2011).
Architecture and construction: AR systems can be implemented in the AEC
industry to permit professionals, workers and potential clients to visualize a virtual
structure during an actual walk through of the construction site in the real-world. AR
systems can be used broadly in design, construction and inspection. There are many
ways in which AR technology can be implemented in order to save time and money, as
well as minimize complications, in the field of architecture and construction (Yuen et al.
2011).
Entertainment: Both the electronic games industry and the social media industry
are expanding their purview to include AR technologies. AR systems are being
incorporated into smartphone apps and hand-held game consoles, an example includes
30
smartphone apps that allow users to fire AR Gatling guns, which appear to actually hit
objects in the real-world. A variety of AR entertainment projects have been enabled
through the iPad, such as a holographic helicopter which hovers over the surface of the
iPad screen. Through the use of a smartphone or other mobile devices, some other
apps allow users to fly an actual remote controlled helicopter or drone. AR technologies
have also been embraced in the movie and music industries for special effects,
especially holographic effects (Yuen et al. 2011).
Medical: Asides from being able to boost medical, surgical and clinical
procedures by improving cost effectiveness, safety, and efficiency, medical AR systems
can also facilitate the invention of new surgical procedures. AR systems have the
potentials to enhance surgical procedures by aiding navigation and orientation prior to,
during, and following surgery. Implementing AR technologies in the medical field will
also allow for more progressive pre-operative imaging studies and visual augmentation
of planned surgical procedures. Furthermore, haptic devices can be integrated with AR
systems to allow surgeons examine patients without having to resort to open surgery,
thereby making complicated surgeries eventually become minimally invasive (Yuen et
al. 2011).
Military: One of the notable military AR application involves the use of HMDs
worn by fighter and helicopter pilots. This technology permits easy access to relevant
information such as instructions, maps, and enemy locations. The required information
can also be projected on to vehicle screens, or the windshield of a cockpit. Several
technologies are being developed for soldiers on the ground and in the air, such as
military-grade AR helmets equipped with computers, 360-degree cameras, UV and
31
infrared sensors, stereoscopic cameras, and OLED translucent display goggles.
Assigning color spectrums to various objects and people will visually provide soldiers
with critical data and warnings about friendly forces, potential danger spots, imminent
air-raid locations, and assignation points. AR technology has the potential to completely
change the face of military combat (Yuen et al. 2011).
Travel: AR can be implemented with services such as GPS systems for driving
and online search apps, to enhance the experience of the users in navigating the real-
world. With AR technologies, these services manifest tangibly as virtual holographic
signs, markers, guiding lines, floating arrows, and other cues. AR can also prompt the
development of advanced comprehensive interfaces, which make social, historical and
business information relevant to a particular location easily accessible to tourists
through the GPS of smartphones or through a photo taken with a smartphone camera
(Yuen et al. 2011).
Limitations of AR Technology
AR technology is still an emerging technology and there are several challenges
regarding several issues such as, technological challenges and social challenges that
must be addressed before AR becomes accepted as part of our everyday life. The
limitations that must be conquered include (Krevelen and Poelman 2010):
Portability and outdoor use: Most of the available mobile AR systems have
been observed to be bulky as they require a heavy backpack to accommodate the PC,
sensors, display, batteries, and every other accessory required. Also, the connections
between all the devices must be capable of resisting the harsh conditions that come
with outdoor use, including weather and shock, but universal serial bus (USB)
connectors are considered to be unreliable. However, the development of smartphones
32
and tablets in mobile technology are overcoming these challenges in mobile AR. Some
of the challenges observed with the use of optical and video see-through displays
outdoors are low brightness, contrast, resolution, and field of view, making them
unsuitable for outdoor use. However, laser-powered displays recently developed by
MicroVision, provide an improved dimension in head-mounted and hand-held displays
that prevail over these challenges. The major limitation observed with most portable
computers is the CPU and consumer operating systems, which limit the amount of
visual and hybrid tracking, also making it not suited for real time computing. Also,
specialized operating systems for real time computing do not have the drivers to support
the sensors and graphics in modern hardware (Krevelen and Poelman 2010).
Tracking and auto-calibration: Tracking in unprepared environments continues
to be a challenging feat, however hybrid systems are becoming small enough to be
incorporated with smartphones or tablets. Due to the complex and extensive process
required for the calibration of these devices, calibration-free or auto-calibrating
approaches that decrease the requirements for setup are preferred. Also, some delay
issues can be resolved through techniques like pre-calculation, temporal stream
matching, and prediction of future viewpoints. Errors can also be reduced by scheduling
system latency through meticulous system design, and shifting pre-rendered images at
the last minute to make up for pan-tilt motions (Krevelen and Poelman 2010).
Depth perception: One difficult challenge that occurs with registration is
accurate depth perception. Although stereoscopic displays help, additional problems
including accommodation-vergence conflicts or low resolution and dim displays result in
objects appearing faraway than they should be. Correct occlusion ameliorates some
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depth problems, so does consistent registration for different eye point locations. In early
video see-through systems with parallax, users needed to adapt to vertical displaced
viewpoints (Krevelen and Poelman 2010).
Overload and over-reliance: Asides from technical challenges, the user
interface must also follow some guidelines in order to avoid overloading the user with
information while also preventing the user to overly rely on the AR system such that
important cues from the environment are missed (Krevelen and Poelman 2010).
Social acceptance: Achieving a widespread acceptance of the use of AR is
proving to be more taxing than excepted. Some of the many factors that play a role in
the social acceptance of AR range from inconspicuous fashionable appearance (gloves,
helmets, etc.) to privacy concerns. These underlying issues must first be confronted
before AR can be widely accepted (Krevelen and Poelman 2010).
ART in Education
Applications of AR in Education
Considering the exciting developments and the evident functionality of AR as an
improved user interface technology, researchers have established that AR has vast
potential implications and numerous benefits for the augmentation of teaching and
learning environments (Yuen et al. 2011). According to Kaufmann and Papp (2006),
spatial abilities present an important component of human intelligence. Also, studies
have shown that spatial abilities can be improved by well-designed trainings. According
to pedagogical theories, collaboration is a fundamental social process that promotes the
development of capabilities in learners. In a typical collaborative AR environment,
multiple users may access a shared space populated by virtual objects, while remaining
based in the real-world. This approach is particularly powerful for educational purposes
34
when users are located in the same space and can use natural means of
communication, but can also be mixed successfully with immersive VR or remote
collaboration (Kaufmann and Papp 2006).
Researchers have explored the use of AR applications within a variety of fields
and disciplines, many of which are already directly or indirectly related to education. The
applications of ART in Education according to Yuen et al. (2011) include:
AR Books: AR books are likely becoming the major key in helping the public
bridge the gap between the virtual and real-world. AR technology has great possibilities
that present students with 3D presentations and interactive experiences that are likely to
be attractive to digital native learners. AR books will open the art of fiction and
storytelling to an entirely new interface that demands greater attention from the authors
to a variety of issues, such as the books cohesion, quality on many fronts, and
immersive capabilities. However, AR books has great potentials that appeal to many
types of learners and proves to be exciting for educators (Yuen et al. 2011).
AR Gaming: Games are often used by instructors to help students understand
concepts being taught in the classroom. With the help of AR technology, instructors can
develop new teaching techniques that show relationships and connections through
games that are grounded in the real-world and augmented with networked data.
Another technique to AR gaming allows learners or educators to create virtual contents
and objects, and then to relate these creations to specific locations in the real-world. AR
games offers instructors the opportunity to implement a new highly visual and highly
interactive form of learning, for example, SimSnails (Yuen et al. 2011).
35
Discovery-based learning: Discovery-based learning is often prompted out of
AR applications that impart information about a real-world location. Many historic sites
currently display overlay maps and different points of historic information for their
visitors. However, AR is expected to stir up more excitement in historic sites though
various developing projects in the near future. There are different AR tools available
which allow visitors to pan across a location while observing a historic event play out or
similarly transform school field trips by replacing paper question sheets with just-in-time
information access. These tools include the EU-funded iTacitus AR project, TAT
Augmented ID application, SREngine, Wikitude and LeamAR (Yuen et al. 2011).
Objects modeling: AR can also be used to contrive objects, allowing learners to
visualize how a particular element would appear in different locations. The models can
be swiftly created, maneuvered, and pivoted. Immediate visual feedback about the
generated ideas and designs can be given to the students so they can spot the
irregularities, address them and learn from their mistakes (Yuen et al. 2011).
Skills training: AR can also be applied to education in the area of skills training.
AR strongly has the capacity to provide powerful contextual, positional learning
experiences and fortuitous research, at the same time stimulating the recognition of the
connected nature of information in the real-world. AR goggles have been used to train
individuals, especially in specific tasks, such as hardware mechanics in the military, or
airplane maintenance, at companies such as Boeing (Yuen et al. 2011).
Available Tools for AR Applications in Education
There are many tools easily available to educators willing to implement AR
applications in education. The choice of the AR tool primarily depends on the type of AR
36
being planned for and the devices available to the students to interact with the AR. The
several tools available include (Yuen et al. 2011):
Daqri, MixAR, and ZooBrust, which are simple and require no programming knowledge or skill.
Others tools include Software Development Kits (SDK) such as ARToolKit, Unifeye Mobile SDK, and Wikitude. These tools have been developed for serious AR developers and are very powerful. They allow developers design various AR applications for variety of devices. Unfortunately, the more advanced tools require extensive knowledge and experience in computer programming, Java, and 3D virtual reality.
Other AR SDK kits includes: AllJoyn SDK, Brew MP SDK, Adreno SDK, Qualcom QCAR SDK, and Qualcomm's Gobi 2000 SDK.
Potential Benefits of AR to Education
The following include the potential benefits of implementing AR in education. AR
technologies:
engage, encourage, and inspire students to study class materials from new perspectives (Kerawalla et al. 2006);
help teach subjects where students they could not realistically gain real-world first-hand experience, for instance astronomy and geography (Shelton and Hedley 2002);
promote collaboration among students, and between students and instructors. (Billinghurst 2002);
encourage creativity and imagination of students (Klopfer and Yoon 2004);
help students take control of their learning path at their own pace (Hamilton and Olenewa 2010); and
foster a genuine learning environment suitable to various learning styles and techniques (Yuen et al. 2011).
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ART in the Construction Industry
Applications of ART in the Construction Industry
The study of the applications for augmented reality has spanned across many
industries, including the construction industry, and has continued to evolve. The
architecture, construction and engineering (AEC) industry has begun to explore
applications for augmented reality in the areas of as-planned to as-built progress
monitoring, training, dynamic site visualization, construction defect detection and
integrating with various building information modelling (BIM) workflows (Rankohi and
Waugh 2013). However, there remains a lot of work to be done and the full potentials
for augmented reality applications has yet to be achieved. The primary research areas
for augmented reality in the AEC industries have focused on the use of the technology
in the field. One such example was the use of augmented reality for steel column
inspections completed by Shin and Dunston (2009) in their study to detect the accuracy
of anchor bolt positions and steel column plumbness. The following include applications
of ART in the industry:
Mobile computing: According to Izkara et al. (2007), the construction sector is
an example of the several settings in which technologies that require mobility of the
users, and access to the information at any time and any place, need to be
implemented, thereby warranting the use of mobile devices. Therefore, the development
of mobile computing solutions is essential in construction sites. The constant change
that occur on construction sites denotes that workers, employers and clients need to
always have access to updated information. The solutions that mobile computing proffer
make this information available without reducing or disrupting the mobility and agility of
the users. Recently building information models have significantly improved the
38
comprehensive semantic content present in design information and information models
are being employed to integrate the initial phases of construction project development.
However, lined-based paper drawings or projections on portable displays are still being
used to represent designs on some construction sites. AR is an all-encompassing
technology that can integrate this design information and situate it in time, place and
context (Meža et al. 2015).
Building and inspecting: One of the major potential application of ART in the
construction industry is that it provides a visual aid to supervise the construction
process and also the inspection of the finished product (Dunston and Shin 2009). Feiner
et al. (1995) was among the pioneers who illustrated the practical use of AR for
construction assembly and maintenance inspection. In the application of AR to
inspection, the technology is reckoned to be an upgrade over other visual aids used to
provide reference points, influencing the extending availability of digitally generated
design information. Notable examples of potential applications of ART for inspection
purposes are layout, excavation, installation, and inspection (Dunston and Shin 2009).
Coordination: As opposed to verbal descriptions, notes, or hand sketches for
the present condition of work areas typically used in in coordinating the construction
process, ART can be used to develop animations of construction activities
superimposed onto a construction site. Essentially, AR can be used for construction
simulation, thereby allowing the field staff to understand the present condition of the
work areas easily and free them from having to mentally imagine the work to be done
(Dunston and Shin 2009).
39
Interpretation and communication: According to Dunston and Shin (2009), AR
also provides visual aids for interpreting drawings and specifications and for
communication on construction projects. Such applications of AR can be regarded as
enabling augmented drawings and specifications.
Lifecycle analysis: In the AEC industry, the use of computer visualization can
occur throughout the entire lifecycle of the construction product; from the initial concept
stage to the final stages of construction and can also extend to the maintenance of the
facilities. Three-dimensional walkthroughs can be created from hand drawn sketches at
the very early stages of the design process to enable visualization. Also, three-
dimensional models can be used by design teams to convey the design objectives to
the client and users and to compare and assess the most suitable design options.
Furthermore, three-dimensional representations can be used to inspect the solidity of
services coordination, accessibility and maintainability during more advanced stages of
design. Visualization also makes it easier for site operatives to interpret the design
details during construction (Bouchlaghem et al. 2005).
Potential Benefits of AR to the Construction Industry
The primary benefit of Augmented Reality (AR) is that it permits delivery of
computer-mediated contextual information to the user that may not be readily made
accessible otherwise. With the trends inching towards a broad use of computers in the
development, capturing, and transmission of data, information, and knowledge, AR
portrays itself as a distinctly radical alternative for workers and supervisors to interact
with computers inherently. AR technology attempts to deliver information as smoothly
as possible to encourage improvements in decision making, and thus performance. As
such, the potentials of AR for impacting field performance during the construction phase
40
of AEC projects is worth exploring. Also, construction sites are very prone to accidents,
therefore safety at work is one of the major concerns of the construction sector
according to the number of accidents recorded and their consequences. The ambience
of the construction environment is very different from other industrial environments,
mostly because it is an uncontrolled environment that is constantly changing and very
fast moving. Some of the benefits provided by AR in the construction industry include
the following (Izkara et al. 2007):
Compensate for the mobility of the workers by making the technology useful in locations where a PC could not be conveniently used.
Increase productivity, by automatically making available the information necessary to perform tasks and make decisions on the construction site.
Show specific information relevant to the current project phase.
Improve surveillance of the status of all the elements required for safety on construction sites by allowing context detection in an uncontrolled environment.
Allow for low user-machine interaction, which enables users to keep the attention on the environment. It must not imply for the user the need to spend too much effort in learning.
AR in Construction Management Education
Challenges of Construction Management Education
The prevalent situation in institutions of higher learning reveals an inadequate
exposure of students to many construction processes and procedures, resulting in a
minimal comprehension of the spatial and temporal constraints which exist during the
process of construction (Mutis and Issa 2014). It is very important for new employees to
go into the labor market after years of educational training in institutions of higher
learning around the globe with the abilities required to resolve the intricate
complications ingrained in the construction process. The future of the construction
41
industry is highly dependent on the competence of these new employees. Techniques
that incorporate site visits and in-class media presentations are usually implemented
with the aim of rectifying this lack of exposure. Although these techniques may provide
some understanding, sole dependence on them would fail to deliver the contextual
details required to fully grasp the complex nature of construction projects. The resulting
lack of experience and understanding renders the students inadequately equipped for
the workforce.
According to Mutis and Issa (2014), sole reliance on the traditional teaching
strategies and media limitations have resulted in knowledge gaps and inadequacies in
grasping spatial and temporal skills. Therefore, Construction Management courses
need to prepare students to connect concepts for better reasoning and problem-solving
skills. There is a need for educators to situate students in a learning environments
fashioned to get students involved in real-world life situations, including unexpected
situations, to better develop logical thought process required to accomplish construction
activities within the project processes and procedures. Typically, many established CM
curricula proffer field trips and internships as a solution to the instructional media
limitations on instructing spatial-temporal and context conditions in the classrooms.
ART as an Educational Tool
ART can be used as an instructional tool to virtually incorporate jobsite visits in
the classroom. The purpose is to adequately instill the exposure of on-site experiences
during all phases of construction projects into construction courses by thoroughly
revealing the spatial temporal constraints in classroom environments. AR class
components assist students to better understand complex concepts such as the
management of space. Therefore, for educational purposes, ART enhances the
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perception of the learners, and functions as an auxiliary tool to perceive and identify
spatial-temporal constraints through the interaction of virtual elements and the
representations of the real-world. Also, the application of ART intensifies the cognizance
and understanding of construction products, processes, sequences and complications
found within the context of construction projects (Mutis and Issa 2014).
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CHAPTER 3 METHODOLOGY
Overview
This experimental study evaluates the possibilities of implementing ART in
construction management education. The primary focus of this study examines the
incorporation of ART in the steel assembly construction process to enhance the
educational experience of construction management students. In order to obtain the
necessary data required to conduct this research, a test case was developed and
learning assessments were conducted to determine the understanding of the students
on the test subject. The purpose of the experimental procedure was to assess the
participants’ base knowledge and then assess the impact of the various instructional
tools used. Accordingly, the main objective of the survey was established as to
determine the effectiveness of ART in the enhancement of the comprehension of the
use and erection of steel components among construction management students.
After collecting data through an extensive review of literature, the second phase
of this research was to conduct the experimental procedure. The study was conducted
in two phases, with the participants being split into three testing groups. The third phase
involved collecting the data derived from the experimental procedure in order to conduct
an analysis of the results using descriptive statistics. Upon analysis of the data, the
fourth and final phase of the research was to determine the effectiveness of the ART
used and provide a summary of the acquired results (see Figure 3-7).
Survey Questionnaire Design
The survey is comprised of two questionnaires, demographic and background
questionnaire and problem solving skills questionnaire. The demographic and
44
background questionnaire consisted of seven questions, while the problem solving skills
questionnaire consisted of five questions. The survey also contained a contained a
consent form with an inclusive confidentiality statement, which indicated that all
responses to the survey questionnaire would be held in complete confidentiality, in
compliance with the University of Florida Institutional Review Board (UFIRB-02). These
consent forms were signed by the participants after the purpose and requirements of
the study was explained. A detailed description of each question in the questionnaires
can be found in the next section.
Demographic and Background Questionnaire
The study participants vary in demographic and background characteristics,
therefore it is important to identify the demographic and background characteristics of
the participants in order to determine if any difference in demographic and background
characteristics can be used later in the report to draw comparisons among participants.
The demographic and background survey questionnaire was designed for identifying
several characteristics of the participants such as age, sex, academic degree program,
level classification, site visits experience and work experience.
Question 1 - What is your age? The purpose of this question was to determine
the average age of the participants in order to determine if age was a factor in the
participants’ knowledge of the subject matter.
Question 2 - Sex? The responses to this question provided information on the
percentage of participants who identified as male and the percentage of those who
identified as female.
Question 3 - Have you been a United States resident for the last 10 years?
The responses to this question provided information on the percentage of participants
45
who have been United States residents for the last 10 years, thereby providing
information about the familiarity of the participants with the United States construction
processes.
Question 4 - Are you concurrently enrolled in an academic degree program
other than Construction Management? The purpose of this question was to
determine the percentage of participants who were concurrently enrolled in an
academic program other than Construction Management and to determine if this had
any relevance to the participants’ performance on the study tests.
Question 5 - What is your current classification level in the BSCM
program? The responses to this question provided information on the current
classification level of the participants in the Construction Management program, which
suggests the level of exposure the participants have to various construction techniques.
Question 6 - Have you visited construction sites as part of your classes or
course work? The purpose of this question was to determine the percentage of
participants who had visited construction sites as part of their classes or course work.
Question 6.1 - If YES, what was the nature of your visit? This question was
posed in order to determine if the circumstances surrounding the participants’ visits to
construction sites.
Question 6.2 - If YES, approximately how many times? This question was
posed in order to determine if there was any relevance between the numbers of times
the participants had visited construction sites and their understanding of the subject
matter.
46
Question 7 - Have you worked in any capacity in the construction industry
prior to taking the survey? The purpose of this question was to determine the
percentage of the participants who had work history and to determine the level of work
experience of those who had.
Question 7.1 - If YES, how many months? This question was posed in order to
determine if there was any relevance between the length of the participants’ work
experience and their performance on the study tests.
Question 7.2 - If YES, please quantify your duties by assigning percentages
of time spent on the tasks on the tasks that you performed the total should add
up to 100%. The responses to this question provided information on the total amount of
time the participants spent on tasks performed at their job. The duties the participants
were required to quantify revolved around the following roles: staff, project engineering,
estimating, purchasing and administration, scheduling and project control.
Problem Solving Skills Questionnaire
The problem solving skills questionnaire was designed to accurately assess the
participants’ knowledge of the subject matter. This questionnaire was administered in
two phases, Phase 1 (pre-learning test) and Phase 2 (post-learning test). During Phase
1 of the study, the participants were required to answer the problem solving skills
questionnaire using only a simple parametric view of the test case. This purpose of the
pre-learning test was to accurately assess the participants’ base knowledge of the
subject matter. Based on the different informational combinations provided to the
different testing groups, the participants were required to complete the qualitative
questions during Phase 2 of the study. The purpose of the post-learning test was to
determine if there was a change in each participant’s knowledge and spatio-temporal
47
understanding of the structural steel assembly process, as well as assess the impact of
the various instructional tools used.
Question 1 - What are the main elements of the structural steel assembly
shown in Figure 1? The responses to this question provided information on the
knowledge of the participants on the identification of structural steel components and
their understanding of the structural steel construction process.
Question 2 - What are the possible tasks required to build the structural
steel assembly? The responses to this question provided information on the ability of
the participants to identify the necessary tasks required for the structural steel
construction process.
Question 3 - For the list generated in (2) of construction products, please
organize in order of the most suitable installation sequence within the
construction process. Similar to Question 2, the responses to this question provided
information on the ability of the participants to identify the necessary tasks required for
the structural steel construction process and also the sequencing of these tasks.
Question 4 - List all tasks, if any, generated in (2) that can be going on in
parallel. Similar to Question 2, the responses to this question provided information on
the participants’ knowledge on tasks identification and sequencing, and also the
understanding of the spatio-temporal constraints present in structural steel construction
process.
Question 5 - Do you have any recommendations to improve the efficiency
of the construction process used in constructing the structural steel assembly?
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The responses to this question present the opinions the participants have on
approaches to improve the structural steel construction process.
Sample Population
The second phase of this research consisted of selecting a list of potential
participants in order to conduct the experimental procedure. The selected target
audience was intended to be undergraduate students enrolled in a Construction
Management program. For the purpose of this research, the data analyzed is based on
a sampling of students enrolled in the Rinker School of Construction Management at the
University of Florida (UF). The experimental procedure was conducted with students in
their second semester of their junior year in the program. A total of 55 students, from
the spring 2015 and spring 2016 semesters, completed the experimental procedure.
The Estimating I class was the class selected for the implementation of this
study. In the Construction Management program at UF, the students are fundamentally
exposed to the construction estimating process in the Estimating I class. The student
learning objectives for this course included:
1. Understand the significance of estimating to the construction industry and identify
the duties, responsibilities, and risks associated with construction estimating.
2. Recognize different types of estimates and their uses.
3. Read and interpret the drawings and specifications.
4. Perform quantity takeoffs based on the drawings and specifications and
5. Generate detailed estimates.
6. Use computers to assist in quantity takeoffs.
Prior to the Estimating I course, students enroll in a course that introduces them
to the different construction techniques. The learning strategies implemented in this
49
class are predominantly reading and classwork accompanied by visits to jobsites and
material manufacturers and trade demonstrations by subcontractors. Expanding on the
knowledge the students gain about construction techniques, the Estimating I course
teaches the students how to quantify the cost elements vitally important to produce a
comprehensive cost estimate. The course focuses on the ability of the students to
identify all elements in a construction process that have cost implications, either
indicated or implied in the Construction Documents. Therefore, it is required that the
Estimating I instructor review construction techniques already presented to students in
prior courses and expand on that knowledge. However, one challenge in teaching cost
item quantification is the ability for the students to understand cost items that are not
evident on the drawings or in the specifications but are imperative to the performance of
the work items. The cognizance of these supplementary cost elements is typically
acquired over time with experience and has to be made known to these students as part
of a comprehensive estimating course. The focus of AR in this study is the use of ART
as a teaching supplement, as it provides a visual representation of these construction
processes in the classroom setting without having to visit a construction site.
Augmented Reality Test Case
Selected Sample Project
The project selected for this study was a multi-story academic classroom and
office building being constructed on the UF campus. The research team worked
together with a local contractor to document the entire construction project and the
processes involved, for the purposes of this study. Construction on the site of the
sample project commenced in the fall of 2013 and the researchers visited the site daily
throughout the construction phase to capture image and video data. The techniques
50
used to gather multiple data from several vantage points include standard imaging
techniques and an unmanned aircraft system (UAS). In addition to using the collected
visual data for purposes of this study, it was also made available to the contractor for
their use as part of their project documentation. A system was created whereby all of
the data captured was stored on a secure computer system and was organized by date
as the project progressed.
The construction site was located in the historic area of the UF campus, thereby
requiring special consideration for design and long lasting construction techniques. The
general construction style was a steel structural frame enclosed with masonry exterior
walls faced with a brick veneer and a clay tile roof system. In addition, the building had
advanced information technology and HVAC systems, which could be documented
throughout the installation phases. Collaboration with the contractor fostered a
relationship, which, coupled with the close proximity of the project to the researchers’
home building, allowed for exceptional site access. Additionally, the contractor worked
with the researchers to identify key installation dates and project milestones to
document to ensure that key construction processes were not missed. This project
proved to be ideal for this study due to the project complexity and breadth of information
that could be gathered through the documentation of the entire construction process.
The team worked to document site activity and construction processes on a daily
basis, spanning from foundation excavation through final building inspections. Still
images as well as video were taken in order to provide a variety of media platforms to
work from as this study progressed. Figure 3-1 is an example of the project
documentation and depicts the construction project as of August of 2014. Due to the
51
design and selected building systems, a wide range of construction techniques were
used including: steel erection, masonry work, metal stud framing, clay tile roofing
installation, stone parapet installation, chilled beam air conditioning system installation,
and fireproofing. The assembly of structural steel member was captured in Figure 3-
2.The range of systems captured afforded the research team the opportunity to
document a variety of installation techniques capturing the means and methods
involved, which were a crucial consideration for this study.
Figure 3-1. Overall construction progress image as of August 2014 captured via an unmanned aircraft (Courtesy of Nathan Blinn, 2014)
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Figure 3-2. Example of project documentation, structural steel assembly (Courtesy of Nathan Blinn, 2014)
Steel Construction
The structural steel assembly was singled out as the primary focus of this phase
of the study and was made use of for the remaining part of the study during the
classroom assessments. The structural steel assembly for the selected sample project
included foundation footings, structural columns, structural framing, angle bracing and
metal decking. Structural steel assemblies are less complicated when compared to
some other building systems, however the system poses some challenges to students
when it comes to understanding the sequencing and overlap involved in the erection of
structural steel elements.
Augmented Procedures
Augmentation Procedure
To achieve a satisfactory result in the process of augmenting a virtual model onto
a construction site, a variety of software packages were used. The entire collection of
53
site images and videos was reviewed and some were selected to receive the
augmentation. This primary factor considered in the selection was content, afterwards
the selected images and videos were then screened for adequate quality and smooth
camera positioning. A camera tracking software was utilized first to process the video,
defining the camera path and defining object locations within the construction site. The
generated camera path script was then taken into 3D modeling software to begin the
process of combining the visual media and virtual objects. The desired virtual model
was placed in the appropriate location using a series of object markers, defined by the
camera tracking software, which were assigned to various elements found within the
view. Thus, the desired 3D model was located within the defined object markers and
visualized from the appropriate camera path and angle. A video-editing software was
then used to combine multiple videos and images depicting the entire structural steel
assembly processes, with selected augmentation.
Figure 3-3 shows the outcome of the layering of the virtual and real-world
information layers using ART. The virtual model, only developed partially showing the
portion of the building that was used in the student evaluations, is shown in its final form
over the as-built structure on the jobsite. These augmentation examples were
developed using the previously described process and were reviewed by the project
team to determine the methods that might work most favorably for the remainder of the
study. Upon the completion of augmentation, the developed video was packaged as a
standard video file and hosted on a secure server, which the students were provided
access to as needed during the appropriate phases of the study. The completed steel
erection video with augmentation was 8 minutes and 37 seconds in length. During the
54
assessments, students were permitted to view the video and progress through it as they
saw fit, with no involvement from the proctors, other than providing them access to the
video. The selected augmentations and video were reviewed by the research team to
ensure the contextual accuracy of all components.
Figure 3-3. Structural steel column augmentation over on-site construction progress documentation (Courtesy of Nathan Blinn, 2014)
Steel Component Augmentation
All the major elements of the structural steel assembly were identified, as
students tend to have difficulty identifying individual steel components. Although all the
several structural steel elements were enhanced through the augmentation of BIM
components into the real-world visual documentation, the structural beam and angle
bracing were the assembly elements highlighted in the augmented reality development.
In order to eliminate any excessive influence on the students’ learning and
55
comprehension experience, text and sound were not incorporated with the
visualizations.
An augmentation related to the placing of the concrete foundation footings and
the order of structural columns and structural framing erection is shown in Figure 3-4
and Figure 3-5. The virtual model shows the sequence in which the elements were
assembled, to allow for understanding of the assembly process. This is an example of
the augmentations which were superimposed over real-world visuals capturing the
entirety of the masonry wall installation process. Additionally, Figure 3-6 shows a real-
world image of the installed metal decking.
Figure 3-4. Augmentation of concrete foundation footings over existing as-built site conditions (Source: Nathan Blinn, 2014)
56
Figure 3-5. Augmentation of steel structure over existing as-built site conditions (Source: Nathan Blinn, 2014)
Figure 3-6. Captured image of the installed metal decking (Source: Nathan Blinn, 2014)
57
Experimental Procedures
The study was conducted in two phases, Phase 1 and Phase 2, with the
participants being split into three testing groups, designated as Groups A, B and C.
Phases 1 was developed to accurately assess the participants’ base knowledge after
which Phase 2 was implemented to assess the impact of the various instructional tools
used. Through the use of a random number generator, the participants in each group
were randomly selected and provided with varied combinations of information regarding
structural steel assembly. Following the formation of the three test groups, a tracking
number was given to each participant and their names were not included in any
documents or results seen by the research team. The random number generated during
the grouping process became the participants’ identity number throughout the study. In
the event that a student requested that their data be excluded from the study or a name
was needed for any reason, the participant’s identity number could be compared to a
master list. Also, the research team members completing the data analysis were not
involved in the proctoring of the experiment and had no access to the participants.
Table 3-1 shows the information that was made available to each of the three
groups. Group A was not permitted to attend the standard classroom lecture and was
only given access to the ART enabled video for the assembly being studied. Group B
was the control group and attended the standard structural steel erection lectures but
was not permitted to view or access the masonry or roof assembly video. Group C was
permitted to attend the standard lecture and was then given access to the ART enabled
video developed for each of the assembly being studied. In addition to this information,
an identical document sets for each of the two phases of the experiment was given to
each participant.
58
Table 3-1. Group designations and associated information streams
Testing Groups Group A Group B Group C
Information Provided
AR Video Only Lecture Only (Control)
Lecture and AR Video
All of the lecture materials and information defined in the course curriculum were
made available to the students throughout the study. Also, make up lectures were
delivered when necessary to ensure that there was no adverse impact on their regular
learning experience. The participants attended class three times a week and the study
was completed over the span of two class periods. Phase 1 involved a pre-learning test
(pre-test) and was completed at the beginning of the semester and Phase 2, the post-
learning test (post-test), was completed during a Wednesday class towards the end of
the semester. For the pre-test phase, participants were asked to complete the required
tasks using a simple parametric 3D view of the sample project with no additional
information other than any knowledge they might have attained through their own
experiences. The students answered qualitative questions regarding material
components, task identification and task sequencing related to the structural steel
assembly process. The pre-test phase of the study allowed for the establishment of a
baseline knowledge in order to effectively determine the change in each participants’
knowledge and spatio-temporal understanding of the studied assembly construction
process.
During Phase 2 the participants were asked to complete the same qualitative
questions regarding material components, task identification and task sequencing of the
structural steel assembly process, as well as an estimating assignment. A set of
construction drawings for the portion of the test case building, which included plans,
59
sections and 3D parametric views of the building, were provided to all the participants to
be used in completing the assigned tasks. For the integrity of the study, the three
groups were separated and those in groups A and group C were brought to a computer
lab where they were provided access to the AR enhanced steel video. The participants
in groups A and C had access to the video on individual computer terminals while they
completed the assignment. In addition, the participants were not permitted to discuss
their work with one another or ask questions of the proctor. In order to ensure that the
participants in Group A did not miss the information provided in the classroom lecture,
they were administered Phase 2 of the study and access to the ART enabled video
directly following their completion of Phase 1. This way they were able to attend the
classroom lecture with no interruptions in the educational experience outlined in the
curriculum.
All documents and work associated with the participants’ answers to the
questions were collected and filed based on their assigned identification number. This
information was then entered into a database for analysis. Documentation, both
physical and digital, was kept in a secured location and on secured servers.
Method of Analysis
The third phase of this research involved receiving the participants’ answers to
the questions, upon which a detailed analysis followed in order to determine whether
the use of ART will enhance the comprehension of the structural steel assembly
process. In order to fully analyze the results of the survey, the responses to each
question in both questionnaires were studied through the use of descriptive statistics.
Afterwards, each question of the problem solving skills questionnaire was assessed
through an analysis based upon comparisons of the population portions of the pre-test
60
and post-test. All in the problem solving skills questionnaire were further analyzed
collectively to assess the impact of the various instructional tools used. The intent of the
analysis was to determine whether the use of the augmented video can enhance the
educational experience of construction management students and their comprehension
of the spatial and temporal constraints existent in the assembly of structural steel
components and the construction process.
Figure 3-7. Research methodology
Develop ART Enabled Media and
Survey Questionnaire
Element Identification
Task Identification
Task Sequencing
Conduct Experimental
Procedure
Determine the sample population to be studied.
Split participants into testing groups A,B and C.
Conduct Phase 1 of the study.
Conduct Phase 2 of the study.
Amalysis of Survey Responses
Assess the participants' base knowledge about the subject matter.
Determine if there was a change in each participant’s knowledge of the subject matter.
Assess the impact of the various instructional tools used.
Develop Conclusions and
Recommendations
Analyze data
Establish interpretations based on literature review and survey analysis.
61
CHAPTER 4 SURVEY RESULTS
The study was conducted over two semesters, with participants from the spring
2015 and 2016 semesters. Out of the 78 students who started the experimental
procedure, 55 completed the procedure. The results of the survey questionnaire are
provided in the following sections, with a brief analysis on each of the questions. The
results in the chapter are described through the use of descriptive and inferential
statistics using the population sample as a whole and the sample proportions.
Demographic and Background Survey Results
The study participants vary in demographic and background characteristics,
therefore it is important to identify the demographic and background characteristics of
the participants in order to determine whether any difference in demographic and
background characteristics can be used later in the report to draw comparisons among
participants. The demographic and background survey questionnaire was designed for
identifying several characteristics of the participants such as age, sex, academic degree
program, level classification, site visits experience and work experience.
Question DB-1: What is your age?
The purpose of this question was to determine the average age of the
participants in order to determine if age was a factor in the participants’ knowledge of
the subject matter. The results shown in Table 4-1 indicated that the highest percentage
in the sample were 22 years at 38% (21 participants), followed by 21 years at 29% (16
participants), followed by 20 years at 15% (8 participants), followed by 23 years at 5%
(3 participants), followed by 31 years at 4% (2 participants), then 19 years, 24 years, 26
years, 29 years and 30 years at 2% (1 participant) each.
62
Table 4-1. Age of study participants (DB-1)
Age Number of Participants % of Total
19 years 1 2% 20 years 8 15% 21 years 16 29% 22 years 21 38% 23 years 3 5% 24 years 1 2% 26 years 1 2% 29 years 1 2% 30 years 1 2% 31 years 2 4% Totals 55 100%
Question DB-2: Sex
The responses to Question 2 provided information regarding the sex of the
participants. According to the responses from the study shown in Table 4-2, 82% (45
participants) were males, with the remaining 18% (10 participants) being females.
Table 4-2. Sex of study participants (DB-2)
Sex Number of Participants % of Total
Male 45 82% Female 10 18% Totals 55 100%
Question DB-3: Have You Been a United States Resident for the Last 10 Years?
The responses to this question provided information on the residency status of
the participants over the preceding 10 years. According to the responses from the study
shown in Table 4-3, 95% (52 participants) have been a United States resident for the
last 10 years, while the remaining participants 5% (3 participants) have not. The
information gathered in this question will be useful in assessing the familiarity of
students with structural steel construction process.
63
Table 4-3. Residency status of study participants (DB-3)
United States Resident Number of Participants % of Total
Yes 53 95% No 3 5% Totals 55 100%
Question DB-4: Are You Concurrently Enrolled in an Academic Degree Program?
The responses to Question 4 provided information regarding the academic
enrollment of the participants. According to the responses from the study as shown in
Table 4-4, 96% (53 participants) were solely enrolled in the construction management
program, while the remaining participants 4% (2 participants) were concurrently enrolled
in other academic programs, quantity surveying and business.
Table 4-4. Academic program of study participants (DB-4)
Concurrent Enrollment Number of Participants % of Total
Yes 53 96% No 2 4% Totals 55 100%
Question DB-5: What is your Current Classification Level in the BSCM program?
The responses to this question provided information on the current classification
level of the participants in the Construction Management program. The results as shown
in Table 4-5 shows that the majority of the participants were juniors in their second
semester, at 98% (54 participants), while the remaining participants 2% (1 participant)
was a senior in their first semester.
Table 4-5. Classification level of study participants (DB-5)
Year Classification Number of Participants % of Total
Freshman 0 0% Sophomore 0 0% JR1 0 0% JR2 54 98% SR1 1 2% SR2 0 0% Totals 55 100%
64
Question DB-6: Have You Visited Construction Sites?
Question 6 was designed to provide information regarding the percentage of
participants who had visited construction sites as part of their classes or course work.
Based on the responses shown in Table 4-6, all of the participants (100%, or 55
participants) had visited construction sites as part of their classes or course work.
Table 4-6. Participants who have visited construction sites (DB-6)
Construction Site Visits Number of Participants % of Total
Yes 55 100% No 0 0% Totals 55 100%
Question 6 goes on further to ask the participants the nature of their visits to
construction sites and how many times they have paid visits to construction sites. From
the results shown in Table 4-7, a majority of the participants at 70% (38 participants)
have visited construction sites both for field trips and as a job requirements, the other
participants have either only visited for field trips purposes (15%, 8 participants), or
solely for work (15%, 8 participants).
Table 4-7. Nature visit to construction sites (DB-6)
Nature of Site Visit Number of Participants % of Total
Work 8 15% Field Trips 8 15% Work and Field Trips 39 70% Totals 55 100%
Table 4-8. Number of times study participants have visited construction sites (DB-6)
Number of Site Visits Number of Participants % of Total
1 – 10 times 25 45% 10 – 20 times 7 13% 20 – 30 times 3 5% 30 – 40 times 2 4% 40 – 50 times 2 4% More than 50 times 16 29% Totals 55 100%
65
As shown in Table 4-8, 45% of the participants (25 participants) have visited
construction sites roughly between 1 to 10 times, 13% (7 participants) have visited
construction sites roughly between 10 to 20 times, 5% (3 participants) have visited
construction sites roughly between 20 to 30 times, 4% (2 participants) have visited
construction sites roughly between 30 to 40 times and 40 – 50 times each, and 29% (16
participants) have visited construction sites approximately greater than 50 times. Figure
4-1 shows a graphical representation of the results.
Figure 4-1. Number of times study participants have visited construction sites
Question DB-7: Have You Worked in any Capacity in the Construction Industry?
The purpose of this question was to determine the percentage of the participants
who had work history and to determine the level of work experience of those who had.
As shown in Table 4-9, 84% of the participants (46 participants) had worked in any
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
0 - 10 10 - 20 20 - 30 30 - 40 40 - 50 > 50
Fre
qu
en
cy
Number of Times Visited
Site Visits
66
capacity in the construction industry prior to participating in the survey while the
remaining 16% (9 participants) had no prior construction related work experience.
Table 4-9. Work experience of study participants (DB-7)
Construction Work Experience
Number of Participants % of Total
Yes 46 84% No 9 16% Totals 55 100%
Additionally, Question 7 asks the participants how many months they have
worked for in the past and the kind of duties and tasks they have had to perform. From
the results shown in Table 4-10 and Figure 4-2, a majority of the participants at 52% (24
participants) had worked for approximately 1 to 6 months, 28% (13 participants) had
worked for approximately 6 to 12 months, 7% (3 participants) had worked for
approximately 12 to 24 months, 2% (1 participant) had worked for approximately 24 to
36 months, 4% (2 participants) had worked for approximately 36 to 48 months, and the
remaining 7% (3 participants) had worked for approximately more than 48 months.
Table 4-10. Length of work experience (DB-7)
Months Number of Participants % of Total
1 – 6 months 24 52% 6 – 12 months 13 28% 12 – 24 months 3 7% 24 – 36 months 1 2% 36 – 48 months 2 4% More than 48 months 3 7% Totals 55 100%
67
Figure 4-2. Number of times study participants have worked in the construction industry
The results shown in Table 4-11 give a percentage of the total time all the
participants spent on tasks performed based on the following roles: staff, project
engineering, estimating, purchasing and administration, scheduling and project control.
From the results it is seen that 41% of time of all the participants were spent on staff
related duties, 27% on project engineering duties, 13% on estimating related duties, 8%
on purchasing and administrative duties, and 11% on scheduling related duties.
Table 4-11. Percentage of time spent on tasks performed (DB-7)
Tasks Performed % of Time Spent
Staff 41% Project Engineering 27% Estimating 13% Purchasing and Administration 8% Scheduling and Project Control 11% Totals 100%
0
2
4
6
8
10
12
14
16
18
20
22
24
1 - 6 6 - 12 12 - 24 24 - 36 36 - 48 > 48
Fre
qu
ency
Number of Months Particpants have Worked
Construction Work Experience
68
Problem Solving Skills Survey Results
The problem solving skills questionnaire was designed to accurately assess the
participants’ knowledge of the subject matter. The purpose of this questionnaire was to
accurately assess the participants’ base knowledge of the subject matter and to
determine if there was a change in each participant’s knowledge and spatio-temporal
understanding of the structural steel assembly process, as well as assess the impact of
the various instructional tools used.
Question PS-1: Main Elements of Structural Steel Assembly
Figure 4-3 shows all the elements of the structural steel assembly that were
introduced in the estimating class, along with the number of students that listed each
item in their answers. All observations were grouped according to the three testing
groups. The number of observations for each of the elements was then converted to a
sample proportion according to the corresponding group. Comparing the different
sample proportion observed in the groups for each element would indicate whether
there any significant difference between them. The pre-test sample proportions are
compared to establish all groups that have a comparable base line, which would allow
for more accurate post-test comparisons. The elements that were highlighted as an
augmentation in the video were the concrete footings, structural columns and structural
framing. However, the metal deck and connections were visible in the video but not
highlighted.
69
Figure 4-3. Number of observations and sample proportions of structural steel elements
The null hypothesis (Ho) postulates that there is no significant difference between
the sample proportions, while the alternate hypothesis (Ha) postulates that there is a
significant difference between the sample proportions. Equations (4-1) and (4-2) show
the null and alternate hypotheses used in the 95% confidence level analyses.
Ho: p̂1 - p̂2 = 0 (4-1) Ha: p̂1 - p̂2 < 0 (4-2)
The sample proportions were compared using the MS Excel (2013) statistical
analysis add-in. Equations (4-3) and (4-4) were used to determine the test statistics of
both the pre-test and post-test sample proportions for each element. To test the
hypothesis, the p-value, which was then derived from the z-statistics, was used and the
null hypothesis is rejected if p ≤ 0.05.
z-statistics =𝑝𝑎−𝑝𝑏−(𝑝𝑎−𝑝𝑏)
√[𝑝 (1− 𝑝)(1
𝑛1+
1
𝑛2)] (4-3)
z-statistics =𝑝𝑏−𝑝𝑐−(𝑝𝑏−�̂�𝑐)
√[𝑝 (1− 𝑝)(1
𝑛2+
1
𝑛3)] (4-4)
Structural Steel Elements p̂ p̂ p̂ p̂ p̂ p̂
Structural Columns 0.74 0.63 0.75 0.75 0.75 0.88
Structural Framing 0.95 0.95 0.95 1.00 0.88 1.00
Metal Deck 0.00 0.37 0.00 0.10 0.00 0.13
Concrete Footings 0.63 0.74 0.85 0.55 0.50 0.44
Connections 0.37 0.26 0.25 0.25 0.25 0.44
Q1. What are the main elements
of the structural steel assembly
shown in Figure 1.
GROUP A OBSERVATIONS
(VIDEO ONLY)
GROUP B OBSERVATIONS
(LECTURE ONLY)
GROUP C OBSERVATIONS
(VIDEO AND LECTURE)
n = 19 n = 20 n = 16
0 2
17 11
5 5
Pre-Test
14
18
0
12
Post-Test
12
18
7
4 7
Post-Test
12 14
14 16
0 2
8 7
Pre-Test
14
7 5
Pre-Test Post-Test
15 15
19 20
70
Table 4-12 shows the results of the pretest and post-test null hypothesis testing
completed for the data sets of all the structural steel elements. The collected data
showed with 95% confidence that there were no significant differences between the
control group and the experimental group’s answers in regard to structural framing (p-
value > 0.05). Although the results show that there is a significant difference between
the control group and the experimental group’s answers in regard to concrete footings in
the post-test (p-value < 0.05), however the pre-test sample proportions do not provide a
comparable base line (p-value < 0.05). On the other hand, significant differences were
observed between the control group and the experimental group’s answers in regard to
structural columns, metal deck and connections. For the “Structural Columns” item, the
null hypothesis for the pre-test sample proportions between groups A and B could not
be rejected (p-value = 0.415 > 0.05), which means that the two groups had similar
proportions prior to the experiment. However, for the post-test sample proportions, the
null hypothesis was rejected (p-value = 0.023 < 0.05), indicating that groups A and B
have significantly different proportions after the experiment. The same was with the
case for the comparison between groups B and C where the sample proportions did not
show any significant differences in the pre–test (p-value = 0.500 > 0.05) but showed a
significant difference in the post-test (p-value = 0.036 < 0.05).
71
Table 4-12. Test results for difference in element identification (PS-1)*
Elements Difference Test Phase z-statistic p-value
Structural Columns
�̂�𝑎 − �̂�𝑏 Pre-test 0.214 0.415
�̂�𝑎 − �̂�𝑏 Post-test 2.000 0.023*
�̂�𝑏 − �̂�𝑐 Pre-test 0.000 0.500
�̂�𝑏 − �̂�𝑐 Post-test 1.795 0.036*
Structural Framing
�̂�𝑎 − �̂�𝑏 Pre-test 0.038 0.485
�̂�𝑎 − �̂�𝑏 Post-test 0.754 0.225
�̂�𝑏 − �̂�𝑐 Pre-test 1.019 0.154
�̂�𝑏 − �̂�𝑐 Post-test 0.000 0.500
Metal Deck
�̂�𝑎 − �̂�𝑏 Pre-test - 1.000
�̂�𝑎 − �̂�𝑏 Post-test 7.692 0.000*
�̂�𝑏 − �̂�𝑐 Pre-test - 1.000
�̂�𝑏 − �̂�𝑐 Post-test 0.946 0.172
Concrete Footings
�̂�𝑎 − �̂�𝑏 Pre-test 3.566 0.000
�̂�𝑎 − �̂�𝑏 Post-test 3.265 0.001
�̂�𝑏 − �̂�𝑐 Pre-test 5.493 0.000
�̂�𝑏 − �̂�𝑐 Post-test 2.054 0.020
Connections
�̂�𝑎 − �̂�𝑏 Pre-test 2.959 0.002
�̂�𝑎 − �̂�𝑏 Post-test 0.360 0.359
�̂�𝑏 − �̂�𝑐 Pre-test 0.000 0.500
�̂�𝑏 − �̂�𝑐 Post-test 4.084 0.000* * p < 0.05; Ho is rejected.
Furthermore, for the “Metal Deck” item, the post-test sample proportions showed
a significant difference between the groups A and B only (p-value < 0.001) as opposed
to the pre-test p-value of 1.000. For the “Connections” item, the post-test sample
proportions were found to be significantly different between groups B and C only (p-
value < 0.001) as opposed to the pre-test p-value of 0.500.
Question PS-2: Possible Tasks Required to Build the Structural Steel Assembly
Figure 4-4 shows all the possible tasks required to build the structural steel
assembly that were introduced in the estimating class, along with the number of
72
students that listed each item in the answers. All observations were grouped according
to the three testing groups. The number of observations for each of the tasks was then
converted to a sample proportion according to the corresponding group. Comparing the
different sample proportion observed in the groups for each task would indicate whether
there is any significant difference between them. The pre-test sample proportions will be
compared to establish all groups that have a comparable base line, which would allow
for more accurate post-test comparisons.
Figure 4-4. Number of observations and sample proportions of possible tasks
The null hypothesis (Ho) postulates that there is no significant difference between
the sample proportions, while the alternate hypothesis (Ha) postulates that there is a
significant difference between the sample proportions. Equations (4-1) and (4-2) show
the null and alternate hypotheses used in the 95% confidence level analyses.
The sample proportions were compared using the MS Excel (2013) statistical
analysis add-in. Equations (4-3) and (4-4) were used to determine the test statistics of
both the pre-test and post-test sample proportions for each task. To test the hypothesis,
Possible Tasks p̂ p̂ p̂ p̂ p̂ p̂
Order and Delivery 0.16 0.16 0.15 0.20 0.19 0.06
Fabrication 0.21 0.00 0.05 0.00 0.19 0.19
Excavation 0.11 0.47 0.15 0.15 0.06 0.13
Place Concrete Footings 0.53 0.74 0.70 0.65 0.44 0.44
Lifting and Placing of Steel Elements 0.68 0.79 0.80 0.90 0.94 1.00
Welded/Bolted Connections 0.79 0.68 0.50 0.85 0.69 0.69
7
15 13 10 17 11 11
10 14 14 13 7
13 15 16 18 15 16
3
2 9 3 3 1 2
4 0 1 0 3
Post-Test
3 3 3 4 3 1
Pre-Test Post-Test Pre-Test Post-Test Pre-Test
Q2. What are the possible tasks
required to build the structural steel
assembly?
GROUP A OBSERVATIONS
(VIDEO ONLY)
GROUP B OBSERVATIONS
(LECTURE ONLY)
GROUP C OBSERVATIONS
(VIDEO AND LECTURE)
n = 19 n = 20 n = 16
73
the p-value, which was then derived from the z-statistics, was used and the null
hypothesis is rejected if p ≤ 0.05.
Table 4-13 provides the results of the pretest and post-test null hypothesis
testing completed for the data sets of all the possible tasks required to build a structural
steel assembly. According to the results, the pre-test sample proportions of the following
tasks do not provide a comparable base line to allow for more accurate post-test
comparisons: fabrication, excavation, place concrete footings, lifting and placing of steel
elements and welded and bolted connections (p-values < 0.05).
On the other hand, significant differences were observed between the control group and
the experimental group’s answers in regard to order and delivery only. For the “Order
and Delivery” item, the null hypothesis for the pre-test sample proportions between
groups A and B could not be rejected (p-value = 0.391 > 0.05), which means that the
two groups had similar proportions prior to the experiment. Also, for the post-test
sample proportions, the null hypothesis could not be rejected (p-value = 0.084 > 0.05),
indicating that groups A and B had similar proportions after the experiment. Similarly,
the null hypothesis for the pre-test sample proportions between groups B and C could
not be rejected (p-value = 0.123 > 0.05). However, for the post-test sample proportions,
the null hypothesis was rejected (p-value < 0.001), indicating that groups B and C have
significantly different proportions after the experiment.
74
Table 4-13. Test results for difference in task identification (PS-2)*
Elements Difference Test Phase z-statistic p-value
Order and Delivery
�̂�𝑎 − �̂�𝑏 Pre-test 0.278 0.391
�̂�𝑎 − �̂�𝑏 Post-test 1.378 0.084
�̂�𝑏 − �̂�𝑐 Pre-test 1.160 0.123
�̂�𝑏 − �̂�𝑐 Post-test 4.818 0.000*
Fabrication
�̂�𝑎 − �̂�𝑏 Pre-test 6.151 0.000
�̂�𝑎 − �̂�𝑏 Post-test - 1.000
�̂�𝑏 − �̂�𝑐 Pre-test 5.064 0.000
�̂�𝑏 − �̂�𝑐 Post-test 7.766 0.000
Excavation
�̂�𝑎 − �̂�𝑏 Pre-test 1.732 0.042
�̂�𝑎 − �̂�𝑏 Post-test 8.054 0.000
�̂�𝑏 − �̂�𝑐 Pre-test 3.406 0.000
�̂�𝑏 − �̂�𝑐 Post-test 0.856 0.196
Place Concrete Footings
�̂�𝑎 − �̂�𝑏 Pre-test 3.107 0.001
�̂�𝑎 − �̂�𝑏 Post-test 1.464 0.072
�̂�𝑏 − �̂�𝑐 Pre-test 4.474 0.000
�̂�𝑏 − �̂�𝑐 Post-test 3.702 0.000
Lifting and Placing of Steel Elements
�̂�𝑎 − �̂�𝑏 Pre-test 1.889 0.029
�̂�𝑎 − �̂�𝑏 Post-test 1.695 0.045
�̂�𝑏 − �̂�𝑐 Pre-test 1.913 0.028
�̂�𝑏 − �̂�𝑐 Post-test 1.333 0.091
Welded and Bolted Connections
�̂�𝑎 − �̂�𝑏 Pre-test 5.054 0.000
�̂�𝑎 − �̂�𝑏 Post-test 2.662 0.004
�̂�𝑏 − �̂�𝑐 Pre-test 3.130 0.001
�̂�𝑏 − �̂�𝑐 Post-test 2.396 0.008 * p < 0.05; Ho is rejected.
Question PS-3: Installation Sequence of Tasks Required to Build the Structural Steel Assembly
Figure 4-5 shows the suitable installation sequence for structural steel assembly
that were introduced in the estimating class, along with the number of students that
listed each item in the answers. All observations were grouped according to the three
testing groups. The number of observations for each of the tasks was then converted to
75
a sample proportion according to the corresponding group. Comparing the different
sample proportion observed in the groups for each task would indicate whether any
significant difference existed between them. The pre-test sample proportions are
compared to establish all groups that have a comparable base line, which would allow
for more accurate post-test comparisons.
Figure 4-5. Number of observations and sample proportions of installation sequence
The null hypothesis (Ho) postulates that there is no significant difference between
the sample proportions, while the alternate hypothesis (Ha) postulates that there is a
significant difference between the sample proportions. Equations (4-1) and (4-2) show
the null and alternate hypotheses used in the 95% confidence level analyses. Similarly
as before, Equations (4-3) and (4-4) were used to determine the test statistics of both
the pre-test and post-test sample proportions for each task and the null hypothesis is
rejected if p-value ≤ 0.05.
Table 4-14 shows the results of the pretest and post-test null hypothesis testing
completed for the data sets of the suitable installation sequence within the structural
Installation Sequence p̂ p̂ p̂ p̂ p̂ p̂
Fabrication 0.11 0.05 0.00 0.00 0.13 0.13
Excavation 0.16 0.37 0.10 0.15 0.13 0.13
Delivery 0.05 0.11 0.10 0.15 0.19 0.00
Concrete Footings 0.74 0.84 0.70 0.70 0.56 0.50
Columns 0.53 0.74 0.65 0.70 0.56 0.50
Structural Framing 0.58 0.74 0.70 0.65 0.50 0.63
Metal Deck 0.11 0.26 0.05 0.05 0.00 0.13
Welded/Bolted Connections 0.63 0.68 0.45 0.70 0.38 0.50
2 5 1 1 0 2
12 13 9 14 6 8
10 14 13 14 9 8
11 14 14 13 8 10
Q3. For the list generated in (2)
of construction products, please
organize in order of the most
suitable installation sequence
within the construction process.
GROUP A OBSERVATIONS
(VIDEO ONLY)
GROUP B OBSERVATIONS
(LECTURE ONLY)
GROUP C OBSERVATIONS (VIDEO
AND LECTURE)
n = 19 n = 20 n = 16
Pre-Test Post-Test Pre-Test Post-Test Pre-Test Post-Test
3 7 2 3 2 2
2 1 0 0 2 2
1 2 2 3 3 0
14 16 14 14 9 8
76
steel construction process. The collected data showed with 95% confidence that there
were no significant differences between the control group and the experimental group’s
answers in regard to fabrication, excavation, delivery, structural framing and metal deck
(p-value > 0.05). The pre-test sample proportions of some of the aforementioned tasks
in the installation sequence do not provide a comparable base line to allow for more
accurate post-test comparisons (p-values < 0.05).
On the other hand, significant differences were observed between the control
group and the experimental group’s answers in regard to concrete footings, structural
columns and connections. For the “Concrete Footings” item, the post-test sample
proportions showed a significant difference between the groups A and B only (p-value =
0.011 < 0.05) as opposed to the pre-test p-value of 0.271. Therefore, the null hypothesis
for the post-test sample proportions between groups A and B can be rejected, indicating
that groups A and B have significantly different proportions after the experiment.
Furthermore, for the “Structural Columns” item, the post-test sample proportions
showed a significant difference between the groups B and C only (p-value < 0.001) as
opposed to the pre-test p-value of 0.074. For the “Connections” item, the post-test
sample proportions were found to be significantly different between groups B and C only
(p-value < 0.001) as opposed to the pre-test p-value of 0.068.
77
Table 4-14. Test results for difference in task sequencing (PS-3)*
Elements Difference Test Phase z-statistic p-value
Fabrication
�̂�𝑎 − �̂�𝑏 Pre-test 6.333 0.000
�̂�𝑎 − �̂�𝑏 Post-test 4.475 0.000
�̂�𝑏 − �̂�𝑐 Pre-test 6.336 0.000
�̂�𝑏 − �̂�𝑐 Post-test 6.336 0.000
Excavation
�̂�𝑎 − �̂�𝑏 Pre-test 2.230 0.013
�̂�𝑎 − �̂�𝑏 Post-test 5.953 0.000
�̂�𝑏 − �̂�𝑐 Pre-test 0.946 0.172
�̂�𝑏 − �̂�𝑐 Post-test 0.856 0.196
Delivery
�̂�𝑎 − �̂�𝑏 Pre-test 2.368 0.009
�̂�𝑎 − �̂�𝑏 Post-test 1.732 0.042
�̂�𝑏 − �̂�𝑐 Pre-test 2.931 0.002
�̂�𝑏 − �̂�𝑐 Post-test 6.943 0.000
Concrete Footings
�̂�𝑎 − �̂�𝑏 Pre-test 0.610 0.271
�̂�𝑎 − �̂�𝑏 Post-test 2.276 0.011*
�̂�𝑏 − �̂�𝑐 Pre-test 2.229 0.013
�̂�𝑏 − �̂�𝑐 Post-test 3.322 0.000
Structural Columns
�̂�𝑎 − �̂�𝑏 Pre-test 2.257 0.012
�̂�𝑎 − �̂�𝑏 Post-test 0.610 0.271
�̂�𝑏 − �̂�𝑐 Pre-test 1.446 0.074
�̂�𝑏 − �̂�𝑐 Post-test 3.322 0.000*
Structural Framing
�̂�𝑎 − �̂�𝑏 Pre-test 2.122 0.017
�̂�𝑎 − �̂�𝑏 Post-test 1.464 0.072
�̂�𝑏 − �̂�𝑐 Pre-test 3.332 0.000
�̂�𝑏 − �̂�𝑐 Post-test 0.403 0.343
Metal Deck
�̂�𝑎 − �̂�𝑏 Pre-test 2.739 0.003
�̂�𝑎 − �̂�𝑏 Post-test 7.455 0.000
�̂�𝑏 − �̂�𝑐 Pre-test 4.003 0.000
�̂�𝑏 − �̂�𝑐 Post-test 3.215 0.001
Connections
�̂�𝑎 − �̂�𝑏 Pre-test 3.452 0.000
�̂�𝑎 − �̂�𝑏 Post-test 0.266 0.395
�̂�𝑏 − �̂�𝑐 Pre-test 1.494 0.068
�̂�𝑏 − �̂�𝑐 Post-test 3.322 0.000* * p < 0.05; Ho is rejected.
78
Question PS-4: Tasks that can be Going On in Parallel
In order to assess the spatial and temporal understanding of the participants on
structural steel assembly process, the responses to Question 4 present the possible
tasks the participants suggested could occur in parallel. The following details the
analysis of the results (See Table 4-15 and Figure 4-6):
Of the 34 responses to this question, 9% indicated that the tasks excavation and fabrication can occur in parallel.
Of the 34 responses to this question, 26% indicated that the erection of the structural steel members and their connections can occur in parallel.
Of the 34 responses to this question, 35% indicated that the structural steel columns can be simultaneously erected and connected to the footings.
Table 4-15. Tasks that can occur in parallel (PS-4)
Tasks that can occur in parallel
Pre-Test Post-Test % of Total
Number of Participants
p̂ % Number of Participants
p̂ %
Excavation and Fabrication
1 0.05 5% 2 0.17 17% 9%
Steel Erection and Connections
6 0.27 27% 3 0.25 25% 26%
Column Erection and Connection to Footings
6 0.27 27% 6 0.50 50% 35%
Roof Members and Protruding Section
1 0.05 5% 0 0.00 0% 3%
Welded Connections and Bolted Connections
5 0.23 23% 0 0.00 0% 15%
Procurement of steel and crane
1 0.05 5% 0 0.00 0% 3%
Placing of Concrete Footings
2 0.09 9% 0 0.00 0% 6%
Foundations and Ordering
0 0.00 0% 1 0.08 8% 3%
Total 22 100% 12 100% 100%
79
Figure 4-6. Tasks that can occur in parallel
Of the 34 responses to this question, 3% indicated that the assembly of the structural steel roof members and the steel sections at the protruding end of the building can occur in parallel.
Of the 34 responses to this question, 15% indicated that both the welded connections and bolted connections of the structural steel members can occur in parallel.
Of the 34 responses to this question, 3% indicated that the procurement of the structural steel members and other materials and the procurement of the equipment needed for steel erection can occur in parallel.
Of the 34 responses to this question, 6% indicated that the concrete foundation footings can be poured simultaneously.
Of the 34 responses to this question, 3% indicated that the ordering and procurement of materials and equipment can occur in parallel with the pouring of the concrete foundation footings.
0%
5%
10%
15%
20%
25%
30%
35%
40%
Excavationand
Fabrication
Steel Erectionand
Connections
ColumnErection andConnectionto Footings
RoofMembers and
ProtrudingSection
WeldedConnectionsand Bolted
Connections
Procurementof steel and
crane
Placing ofConcreteFootings
Foundationsand Ordering
Pe
rce
nta
ge o
f P
arti
cip
ants
' Re
spo
nse
s
Tasks Required for Structural Steel Assembly
Tasks that Can Occur in Parallel
80
Question PS-5: Recommendations to Improve the Efficiency of the Construction Process
This question was posed in order to acquire recommendations from the survey
participants on how to improve the efficiency of the construction process used in
constructing the structural steel assembly. The analysis of the results as shown in Table
4-16 indicated (also see Figure 4-7):
Of the 81 responses to this question, 27% indicated having efficient equipment and crew members will efficiently improve the construction process used in constructing the structural steel assembly.
Of the 81 responses to this question, 27% indicated that prefabricating some of the structural steel assembly offsite, especially roof members, and placing them on-site, will efficiently improve the construction process used in constructing the structural steel assembly.
Table 4-16. Recommendations on how the structural steel assembly process can be efficiently improved (PS-5)
Recommendations Observations % of Total
Efficient Equipment and Crew 22 27%
Prefabrication 22 27%
Use of adequate safety equipment 6 7%
Good communication and Scheduling 6 7% Site Planning 5 6%
Early Delivery of Materials 5 6%
Avoid Field Welds; Use shop welds or bolted connections
7 9%
Concrete Columns on first level 1 1%
Erect heavy steel members first 1 1%
Install leveling bolts while pouring concrete footings
1 1%
Quality materials 1 1%
Clear sets of drawings 1 1%
Use 3D Printing for materials 1 1%
Use Automated Machines 1 1%
Use the same contractor for steel erection 1 1% Totals 81 100%
Of the 81 responses to this question, 7% indicated that enforcing safety measures and the use of adequate safety equipment on-site will efficiently
81
improve the construction process used in constructing the structural steel assembly.
Of the 81 responses to this question, 7% indicated that good communication between all parties involved in construction and organized scheduling of the construction activities will efficiently improve the construction process used in constructing the structural steel assembly.
Of the 81 responses to this question, 6% indicated that a well laid out site will efficiently improve the construction process used in constructing the structural steel assembly.
Of the 81 responses to this question, 6% indicated that early delivery of all the materials required will efficiently improve the construction process used in constructing the structural steel assembly.
Of the 81 responses to this question, 9% indicated that the use of shop welds and bolted connections, as opposed to the use of field welds, will efficiently improve the construction process used in constructing the structural steel assembly.
The remainder 8% of responses to this question indicated that the following will efficiently improve the construction process used in constructing the structural steel assembly:
o The use of concrete columns on the first floor of the building (1%).
o Erection of heavy steel members first, followed by smaller members that can be assembled by hand or with smaller equipment (1%).
o Installation of leveling bolts while pouring concrete footings to prevent drilling holes into the foundation later on (1%).
o The use of standard and quality materials (1%).
o Having a clear set of drawings (1%).
o The use of 3D printing for materials (1%).
o The use of automated machines (1%).
o Using the same contractor for all work related to structural steel assembly (1%).
The following lists the rankings of the major recommendations in descending
order from highest to lowest observed percentages:
82
1. Efficient Equipment and Crew (27%) 2. Prefabrication (27%) 3. Avoid Field Welds; Use shop welds or bolted connections (9%) 4. Use of adequate safety equipment (7%) 5. Good communication and Scheduling (7%) 6. Site Planning (6%) 7. Early Delivery of Materials (6%)
Figure 4-7. Recommendations on how the structural steel assembly process can be efficiently improved
27%
27%
8%
8% 6%
6%
9%
2%
1%
1%
1%
1%
1%
1%
1%
9%
Recommendations
Efficient Equipment and Crew Prefabrication
Use of adequate safety equipment Good communication and Scheduling
Site Planning Early Delivery of Materials
Avoid Field Welds; Use shop welds or bolted connections Concrete Columns on first level
Erect heavy steel members first Install leveling bolts while pouring concrete footings
Quality materials Clear sets of drawings
Use 3D Printing for materials Use Automated Machines
Use the same contractor for steel erection
83
CHAPTER 5 CONCLUSIONS AND RECOMMENDATIONS
The following sections outline the conclusions of this investigation obtained from
the literature review and survey analysis found in Chapters 2 through 4.
Conclusions
Augmented reality (AR) is an emerging technology that encompasses a vast
range of technologies that can be applied within diverse fields and industries. Presently,
the application of the technology is broadly classified into the following: personal
information systems, industrial and military application, medical applications, AR for
entertainment, AR for the office, and education and training. However, the applications
of AR technologies are not limited to these fields alone. Although, AR is being
embraced by a lot of industries, the technology is not without imperfections. A lot of
work still has to be done to improve on 1) the portability and outdoor use of ART
devices, 2) tracking and registration of users’ experiences, 3) accuracy of depth
perception, 4) user interfacing, and 5) social acceptance. Aside from these, the
opportunities for research on ART is never ending.
AR has vast potential implications and numerous benefits for the augmentation of
teaching and learning environments. AR enhances the collaboration and spatial abilities
needed to support the learning development capabilities of students. There are several
available AR tools and technologies that can be used to improve learning experiences
in the classroom, some of which include AR books, gaming and software development
kits (SDK). The constant changing nature of modern information technologies demands
a change in learning situations for both the learners and the educators, and ART
provides the tools to help facilitate these changes.
84
The implementation of ART in the construction industry is swiftly progressing and
developing in yet undiscovered ways. The industry is currently immersed in the
applications of AR in areas like as-built progress monitoring, training, dynamic site
visualization, construction defect detection, mobile computing, lifecycle analysis, and so
on. ART has thus far proven to have a positive impact on the industry as witnessed in
several areas of improvement such as mobility and functionality, increased productivity,
increased safety records on construction sites and so forth. Furthermore, mobile
devices and other necessary hardware are becoming increasingly affordable and
accessible, as a result the use of ART on construction sites is reasonably widespread.
ART has exhibited great potentials in enhancing construction management
education, although a significant amount of work still has to be done preparatory to the
prevalent use of ART in enhancing the educational experiences of students. The major
educational challenge many construction management students are confronted with is a
knowledge gap in grasping the spatial and temporal constraints which exist during
construction processes. This stems from an insufficient exposure of students to many
construction processes and procedures. ART provides a simple and convenient solution
to this by virtually incorporating jobsite visits into the classrooms.
The results of this study show that the augmentation video increased students’
understanding of the main elements of the structural steel assembly, especially the
“Metal Deck” element. Also, the augmentation video increased the understanding of the
students on the possible tasks required to complete the structural steel assembly. The
group that had access to the lecture and video had the highest benefits, as they were
able to accurately visualize how the structure was erected. It can be inferred that the
85
augmentation video helped buttress the elements and concepts introduced in class. The
video also helped those students visualize and remember the installation sequence for
structural steel the elements, most especially regarding the “Structural Framing”
element. In addition, students that had access to the video without the lecture were able
to correctly list out a suitable installation sequence more than the students that had the
lecture only. Therefore, the best approach to enhancing the educational experience of
students is by introducing the augmentation video as a supplement in the classroom, as
opposed to a complete replacement of the lectures and other instructional materials.
The world is undoubtedly changing and moving increasingly towards a very
immersive and virtual reality, not just in education and construction. However, a lot of
AR tools are very much at our disposal for our innovative utilization. We can only grow
and adapt along with the change while driving forward significant changes in all our
industries.
Results to Investigation Objectives
As indicated throughout the research, the targeted purpose for this research was
to discover the possible advantages of incorporating ART with traditional teaching
techniques and how its use can be optimized in construction management education.
The following objectives were used to evaluate any meaningful data derived through the
literature review and survey (Chapters 2 through 4): investigate the current use of ART
in the construction industry; assess the current use of ART in education; assess the
current use of ART in construction management education; and determine the
effectiveness of ART in the comprehension of the use and erection of steel components
among construction management students.
86
Objective 1: Investigate the current use of ART in the construction industry
In investigating the current use of ART in the construction industry, the systems
and technologies of AR had to be explored. Although the construction industry has
begun to embrace applications for augmented reality in several areas, there remains a
lot of work to be done before a widespread implementation and the full potentials of AR
applications are to be achieved.
Objective 2: Assess the current use of ART in education
Currently, AR has shown great potentials in bringing about sweeping
improvements in education, as the combinations of AR technologies and tools with
conventional classroom teaching techniques have indicated an improvement in the
performance of students. It is expected that as educators experiment with all the
available AR tools, and develop new methods of teaching and learning, continuing
progress will be made.
Objective 3: Assess the current use of ART in construction management education
AR is being used as an instructional tool to effectively bring in the exposure of
on-site experiences during any phase of the construction projects into construction
courses. ART provides a convenient solution to the major educational challenge many
construction management students are confronted with by virtually incorporating jobsite
visits into the classrooms. At the same time it provides instructors with the flexibility to
incorporate these field experiences at the opportune time in the classroom.
Objective 4: Determine the effectiveness of ART in the comprehension of the use and erection of steel components among construction management students
ART has displayed positive capabilities in enhancing construction management
education. During the structural steel assembly experimental procedure in this study, it
87
was observed that the ART enabled media was able to help students better understand
and identify the elements and tasks involved in the assembly process. It was also
perceived that combining traditional classroom lectures with ART enabled media
showed to be advantageous.
Improvements to the Survey
The following are improvements that need to be made to further enhance the
quality of the survey as well as provide more accurate analysis of the results:
The research was intended for undergraduate students enrolled in the Construction Management program at the University of Florida, which limits the targeted audience. However, since the targeted audience is limited, extra steps should be taken to ensure almost all, if not all, of the students who started the procedure completed it.
Recommendations for Future Research
Although this study only discussed the effects of understanding the elements and
tasks required to complete a structural steel assembly, the same concept can be
applied to any other system in the construction industry. In future work different
assemblies should be used to conduct similar tests and further discern the most
effective use for ART.
Future researchers interested in this topic of study should attempt to analyze the
participants’ performance in the estimating part of the survey. Also, another course
besides the Estimating I class should be sampled. As the focus of the research is
relatively new to construction management education, feedback from the students on
the use of the ART video as an instructional tool may also be of some help.
In addition, more statistical analysis can be performed on elements and tasks
whose pre-test sample proportions showed a significant difference when comparing the
control group and the experimental group’s answers, to determine the underlying factors
88
behind the observed differences. These differences may have been attributable to the
different demographic and background characteristics of the participants and can only
be confirmed by additional statistical tests.
89
LIST OF REFERENCES
Augment (2016). “Infographic: The History of Augmented Reality.” Augment, <http://www.augment.com/blog/infographic-lengthy-history-augmented-reality/> (June 7, 2016).
Azuma, R. (2004). “Overview of augmented reality.” In ACM SIGGRAPH 2004 Course Notes (p. 26). ACM.
Azuma, R. T. (1997). “A survey of augmented reality.” Presence: Teleoperators and Virtual Environments, 6(4), 355-385.
Azuma, R., Baillot, Y., Behringer, R., Feiner, S., Julier, S., and MacIntyre, B. (2001). “Recent advances in augmented reality.” Computer Graphics and Applications, IEEE, 21(6), 34-47.
Billinghurst, M. (2002). “Augmented reality in education.” New Horizons for Learning, 12.
Bouchlaghem, D., Shang, H., Whyte, J., and Ganah, A. (2005). “Visualisation in architecture, engineering and construction (AEC).” Automation in Construction, 14(3), 287-295.
Caudell, T. P., and Mizell, D. W. (1992). “Augmented reality: An application of heads-up display technology to manual manufacturing processes.” In System Sciences, 1992. Proceedings of the Twenty-Fifth Hawaii International Conference on (Vol. 2, pp. 659-669). IEEE.
Dunston, P. S. (2009). “Key areas and issues for augmented reality applications on construction sites.” In Mixed Reality in Architecture, Design and Construction (pp. 157-170). Springer Netherlands.
Feiner, S. K., Webster, A. C., Krueger III, T. E., MacIntyre, B., and Keller, E. J. (1995). “Architectural anatomy.” Presence: Teleoperators & Virtual Environments, 4(3), 318-325.
Hamilton, K., & Olenewa, J. (2010). “Augmented reality in education.” <http://www.authorstream.com/Presentation/k3hamilton-478823-augmented-reality-in-education> (May 16, 2016).
Höllerer, T. H. (2004). “User interfaces for mobile augmented reality systems”. Doctoral dissertation, Columbia University.
Höllerer, T., and Feiner, S. (2004). “Mobile augmented reality.” Telegeoinformatics: Location-Based Computing and Services, Taylor and Francis Books Ltd., London, UK.
90
Izkara, J. L., Pérez, J., Basogain, X., and Borro, D. (2007). “Mobile augmented reality, an advanced tool for the construction sector.” In Proceedings of the 24th W78 Conference, Maribor, Slovenia (pp. 190-202).
Kaufmann, H. (2003). “Collaborative augmented reality in education.” Institute of Software Technology and Interactive Systems, Vienna University of Technology.
Kaufmann, H., and Papp, M. (2006). “Learning objects for education with augmented reality.” Proceedings of EDEN, 160-165.
Kerawalla, L., Luckin, R., Seljeflot, S., and Woolard, A. (2006). “Making it real: Exploring the potential of augmented reality for teaching primary school science.” Virtual Reality, 10(3-4), 163-174.
Klopfer, E., and Yoon, S. (2004). “Developing games and simulations for today and tomorrow’s tech savvy youth.” TechTrends, 49(3), 33-41.
Meža, S., Turk, Ž., and Dolenc, M. (2015). “Measuring the potential of augmented reality in civil engineering.” Advances in Engineering Software, 90, 1-10.
Milgram, P., Takemura, H., Utsumi, A., and Kishino, F. (1995). “Augmented reality: A class of displays on the reality-virtuality continuum.” In Photonics for Industrial Applications (pp. 282-292). International Society for Optics and Photonics.
Mutis, I., and Issa, R.R. (2014). “Enhancing spatial and temporal cognitive ability in construction education through augmented reality and artificial visualizations.” Proceedings International Conference on Computing in Civil and Building Engineering, R. Issa and I. Flood, Eds., Orlando, FL, 2079-2086.
Shelton, B. E., and Hedley, N. R. (2002). “Using augmented reality for teaching earth-sun relationships to undergraduate geography students.” In Augmented Reality Toolkit, the First IEEE International Workshop (pp. 8-pp). IEEE.
Van Krevelen, D. W. F., and Poelman, R. (2010). “A survey of augmented reality technologies, applications and limitations.” International Journal of Virtual Reality, 9(2), 1.
Wang, X., Kim, M. J., Love, P. E., and Kang, S. C. (2013). “Augmented Reality in built environment: Classification and implications for future research.” Automation in Construction, 32, 1-13.
Yuen, S., Yaoyuneyong, G., and Johnson, E. (2011). “Augmented reality: An overview and five directions for AR in education.” Journal of Educational Technology Development and Exchange, 4(1), 119-140.
Zhou, F., Duh, H. B. L., and Billinghurst, M. (2008). “Trends in augmented reality tracking, interaction and display: A review of ten years of ISMAR.” In
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Proceedings of the 7th IEEE/ACM International Symposium on Mixed and Augmented Reality (pp. 193-202). IEEE Computer Society.
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BIOGRAPHICAL SKETCH
Fopefoluwa Bademosi was born in Ibadan, Nigeria and lived there all her life until
she moved to the United States in 2014 to further her studies. In 2013, she graduated
with honors with a Bachelor of Science in Building Technology from Covenant
University in Ota, Nigeria. She will be graduating with a Master of Science in
Construction Management from the Rinker School of Construction Management at the
University of Florida in August 2016. She plans to further pursue her education by
enrolling in the Rinker School Ph.D. program.
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