UNIVERSITY OF CINCINNATI Date:___________________

I, _________________________________________________________, hereby submit this work as part of the requirements for the degree of:

in:

It is entitled:

This work and its defense approved by:

Chair: _______________________________ _______________________________ _______________________________ _______________________________ _______________________________

MECHANISTIC-BASED PERFORMANCE PREDICTION

AND LIFE CYCLE COST ANALYSIS TOOLS:

AN APPLICATION TO THE OHIO ROUTE 50 TEST PAVEMENT

A thesis submitted to the

Division of Research and Advanced Studies

of the University of Cincinnati

in partial fulfillment of the

requirements for the degree of

MASTER OF SCIENCE

in the Department of Civil and Environmental Engineering

of the College of Engineering

2005

by

Pavan Kumar Tallapragada

B.Tech. (Civil), Indian Institute of Technology – Chennai (Madras), India, 2001

Committee Chair: Dr. Anastasios M. Ioannides, P.E.

iii

ABSTRACT

This study examines the development, utilization and application of performance

prediction and life cycle costing for rigid pavements. Emphasis is laid on selecting an

appropriate computer application that comprises mechanistic-based prediction equations and

life cycle costing, for application to the Ohio Route 50 Project.

A thorough literature review examining methods of collection and processing

pavement performance data, development of performance prediction equations for flexible

and rigid pavement systems, computer programs for rigid pavement performance prediction

and life cycle cost analysis, and usage of performance prediction and life cycle costing

methods by selected state highway agencies for planning and maintenance is presented.

Pavespec 3.0, developed for the Federal Highway Administration, is selected, and

over two hundred simulations of the program are completed, using the as-constructed

pavement system data from the Ohio Route 50 Project as inputs. Observed distress data

trends are used for calibration, and predictions for the service life of the Ohio Route 50

pavement system are generated. Life cycle cost analysis methods are utilized to determine

the relative cost effectiveness of various joint sealing options on the Project.

From the comparisons of predicted and observed distresses for the eastbound and

westbound sections of the Ohio Route 50 pavement, it is established that data points spaced

out over a longer period of time provide better regression curves, and subsequently, a more

reliable analysis. The slopes of observed distress curves for the international roughness

index, for transverse slab cracking and for spalling, are found to be many times higher than

the slopes of the corresponding predicted curves obtained from Pavespec 3.0. The

iv

differences are most pronounced in the case of transverse slab cracking. Previous

mechanistic analysis of this pavement system had attributed the very high cracking

percentage to longer slab length. In addition, it had been inferred that an unexpected flood

and various construction issues led to the premature deterioration of this pavement system.

Data calibration assumes a significant role in such cases, but Pavespec 3.0 uses a linear

regression method for this purpose, which is found to be inadequate.

Life cycle cost analysis methods are used to examine the cost effectiveness of the ten

joint sealant materials used on the Ohio Route 50 Project. Approximate rehabilitation life

cycle costs are calculated using Pavespec 3.0. Compression sealants, with the exception of

Techstar W050, are found to be the most cost effective sealing option, due to their lower

material and installation costs, consistently higher performance, and longer replacement

cycles.

v

ACKNOWLEDGEMENTS

I would like to express my heartfelt gratitude to my graduate advisor, Dr. Anastasios

M. Ioannides. His support and guidance during my graduate study at the University of

Cincinnati was valuable, and will definitely help shape up my professional career.

I would also like to thank Dr. Mark T. Bowers, Dr. Issam A. Minkarah, and Dr. Sam

Salem, for their consent to serve on my committee, and devoting time to help me at various

stages of my graduate study. My coursework and Master’s Thesis have been rewarding and I

am grateful to the faculty and staff at the University of Cincinnati.

I would like to thank my parents, my sister, and friends Sharat, Ravi Prasad and

Amar, for providing me with constant support and confidence that helped me successfully

complete graduate studies. Mahesh and Shelly at Totally Productive Group, Inc. deserve a

very special mention here, in appreciation of their efforts to let me work flexible hours and

take time off to complete my Master’s Thesis.

During my studies at the University of Cincinnati, I have received financial assistance

in the form of a Research Assistantship under Dr. Anastasios M. Ioannides (October 2001-

September 2002) and a University Graduate Scholarship.

vi

TABLE OF CONTENTS

Page

ABSTRACT iii

ACKNOWLEDGMENTS v

LIST OF TABLES x

LIST OF FIGURES xi

LIST OF ABBREVIATIONS xiii

SI* (MODERN METRIC) CONVERSION FACTORS xviii

1 INTRODUCTION 1

1.1 Introduction 1

1.2 Problem Statement 2

1.3 Research Objective 4

1.4 Research Significance 5

1.5 Thesis Organization 6

2 LITERATURE REVIEW 9

2.1 Introduction 9

vii

2.2 Pavement Performance Prediction 10

2.3 Performance Prediction Models for Flexible Pavements 13

2.3.1 AASHTO Model 13

2.3.2 Arizona DOT Model 14

2.3.3 Texas FPS Model 14

2.3.4 Highway Design and Maintenance Standards (HDM) Model 14

2.3.5 SUPERPAVE Research 14

2.4 Performance Prediction Models for Rigid Pavements 15

3 SURVEY OF CURRENT PRACTICE 22

3.1 Introduction 22

3.2 Computer Programs for Pavement Performance

Prediction 23

3.2.1 Programs for Rigid Pavements 25

3.3 Programs for Life Cycle Cost Analysis 30

3.4 Performance Prediction – Current

State-of-the-Practice 33

3.5 Pavement Management System’s Framework 34

3.5.1 Distress Data Collection 35

3.5.2 Data Categorization and Analysis 35

3.5.3 Pavement Performance Modeling 36

3.5.4 Application of Performance Prediction 38

viii

4 SENSITIVITY ANALYSIS USING Pavespec 3.0 46

4.1 Introduction 46

4.2 Discussion of Sensitivity Analysis Observations 48

4.2.1 Pavement Design, Dimensions and Lane Configuration 48

4.2.2 Traffic Factors 50

4.2.3 Climatic Factors 51

4.2.4 Sampling Methods 51

4.2.5 Acceptance Quality Characteristics 52

4.2.6 Maintenance and Rehabilitation Options 53

4.2.7 Cost and Interest Rates 53

4.3 Summary of Major Findings from Sensitivity Analysis 54

5 APPLICATION TO THE OHIO ROUTE 50 PROJECT 68

5.1 The Ohio Route 50 Project 68

5.1.1 Project Details 68

5.1.2 Experimental Plan 69

5.1.3 Performance Evaluation 70

5.1.4 Conclusions from the Ohio Route 50 Project 72

5.2 Distress Prediction for the Ohio Route 50 Project 74

5.2.1 Analysis Using the Ohio Route 50 Project Data 75

5.2.2 Sections with Compression Sealants – Eastbound 79

5.2.3 Sections with Silicone Sealants – Eastbound 81

ix

5.2.4 Sections with Hot Pour Sealants – Eastbound 82

5.2.5 Sections with No Sealants – Eastbound 82

5.2.6 Sections with Compression Sealants – Westbound 83

5.2.7 Sections with Silicone Sealants – Westbound 84

5.2.8 Sections with Hot Pour Sealants – Westbound 84

5.2.9 Sections with No Sealants – Westbound 85

5.2.10 Summary of Observations 85

5.3 Life Cycle Cost Analysis to Determine the Cost Effectiveness

of Sealants 86

5.3.1 Proposed M&R Steps and Costs 88

5.3.2 Cost Effectiveness of Sealant Material Using LCCA- Applied

Methodology 89

5.3.3 Results from the Analysis 90

6 CONCLUSIONS & RECOMMENDATIONS 133

6.1 Research Summary 133

6.1.1 Literature Review 133

6.1.2 Computer Programs Available 135

6.1.3 Current State-of-the-Practice 135

6.1.4 Sensitivity Analysis using Pavespec 3.0 136

6.1.5 Application to the Ohio Route 50 Project 137

x

6.2 Conclusions 138

6.3 Recommendations 140

7 REFERENCES 144

xi

LIST OF TABLES

Page

2.1 Prediction Models from the COPES study 20

2.2 Distress prediction models used in Pavespec 3.0 21

3.1 Distresses rated during inspections for the TxDOT PIMS pavement types 41

3.2 Significant pavement performance prediction programs-functions, features, modules and applicability 42 4.1 Range of input values provided for Input Output analysis of Pavespec 3.0 57

4.2 Sensitivity analysis results classified by importance 61

5.1 Inputs for Pavespec 3.0 used to simulate Ohio Route 50 pavement performance 91

5.2 Sealant unit costs, M&R costs and other inputs 93

5.3 Rehabilitation life cycle cost analysis for sealant materials used in the eastbound lanes of Ohio Route 50 Project 94

5.4 Rehabilitation life cycle cost analysis for sealant materials used in the westbound lanes of Ohio Route 50 Project 95

5.5 Data Filtering (IRI) for Eastbound Section with Delastic V 687 96 6.1 Correlation coefficients of observed distress curves for eastbound

and westbound lanes 138

xii

LIST OF FIGURES

Page

3.1 Computer based pavement management system, modules and outcomes 44

3.2 Data flow diagram for a Pavement Management System 45

5.1 Compression sealants Eastbound IRI comparison plots 97

5.2 Compression sealants Eastbound transverse slab cracking comparison plots 99

5.3 Compression sealants Eastbound spalling comparison plots 101

5.4 Silicone sealants Eastbound IRI comparison plots 103

5.5 Silicone sealants Eastbound transverse slab cracking comparison plots 105

5.6 Silicone sealants Eastbound spalling comparison plots 107

5.7 Hot pour sealants Eastbound IRI comparison plots 109

5.8 Hot pour sealants Eastbound transverse slab cracking comparison plots 110

5.9 Hot pour sealants Eastbound spalling comparison plots 111

5.10 No sealants Eastbound IRI comparison plots 112

5.11 No sealants Eastbound transverse slab cracking comparison plots 113

5.12 No sealants Eastbound spalling comparison plots 114

5.13 Compression sealants Westbound IRI comparison plots 115

5.14 Compression sealants Westbound transverse slab cracking comparison plots 117

5.15 Compression sealants Westbound spalling comparison plots 119

5.16 Silicone sealants Westbound IRI comparison plots 121

5.17 Silicone sealants Westbound transverse slab cracking comparison plots 123

5.18 Silicone sealants Westbound spalling comparison plots 125 5.19 Hot pour sealants Westbound IRI comparison plots 127

xiii

5.20 Hot pour sealants Westbound transverse slab cracking comparison plots 128

5.21 Hot pour sealants Westbound spalling comparison plots 129

5.22 No sealants Westbound IRI comparison plots 130

5.23 No sealants Westbound transverse slab cracking comparison plots 131

5.24 No sealants Westbound spalling comparison plots 132

xiv

LIST OF ABBREVIATIONS AND SYMBOLS

AADT Annual Average Daily Traffic

AASHO American Association of State Highway Officials

AASHTO American Association of State Highway and Transportation Officials

AC Asphalt Concrete

ACP Asphalt Concrete Pavements

ADOT Arizona Department of Transportation

ADT Average Daily Traffic

APA Asphalt Pavement Alliance

AQC Acceptance Quality Characteristics

CalTrans California Department of Transportation

CDOT Colorado Department of Transportation

CERL Construction Engineering and Research Laboratory

COMP Composite Pavement

COPES Concrete Pavement Evaluation Study

CPM Condition Prediction Model

CRCP Continuously Reinforced Concrete Pavement

CRS Condition Rating Survey

CRSI Concrete Reinforcing Steel Institute

CS Condition Score

DS Distress Scores

DVA Danish Void Analyzer

xv

ESALs Equivalent Single Axle Loads

FHWA Federal Highway Administration

FPS Flexible Pavement Design System

GIS Geographic Information System

HIPERPAV High Performance Concrete Paving Software

HDM Highway Design and Maintenance Standards

HPMA Highway Pavement Management Application

HPMS Highway Performance Monitoring System

IDOT Illinois Department of Transportation

ILLIPMS Illinois Pavement Information Management System

IPFS Illinois Pavement Feedback System

IRI International Roughness Index

JCP Jointed Concrete Pavements

JPCP Jointed Plain Concrete Pavement

JRCP Jointed Reinforced Concrete Pavement

L/l Ratio of Length of Slab (ft) to Radius of Relative Stiffness (ft)

LCCA Life Cycle Cost Analysis

LTPP Long Term Pavement Performance

M&R Maintenance and Rehabilitation

MAYS Mays Number

MnDOT Minnesota Department of Transportation

NCHRP National Cooperative Highway Research Program

OCI Overall Condition Index

xvi

ODOT Ohio Department of Transportation

PCC Portland Cement Concrete

PCI Pavement Condition Index

PCR Pavement Condition Rating

PDOT Pennsylvania Department of Transportation

PMS Pavement Management System

PRS Performance Related Specifications

PSI Present Serviceability Index

PSR Present Serviceability Rating

R2 Coefficient of Correlation

RPLCCA Rigid Pavement Life Cycle Cost Analysis

RPPR Rigid Pavement Performance and Rehabilitation

RSL Remaining Service Life

RUS Ride Utility Score

SD Structural Deduct

SHA State Highway Agencies

SHRP Strategic Highway Research Program

SLTE Stress Load Transfer Efficiency

SN Structural Number

SUPERPAVE Superior Performing Asphalt Pavements

TFHRC Turner-Fairbank Highway Research Center

TTI Texas Transportation Institute

TxDOT Texas Department of Transportation

xvii

TxPMIS Texas Pavement Management Information System

UPDI Unified Pavement Distress Index

xviii

1

1 INTRODUCTION

1.1 Introduction

The development and utilization of mechanistic principles and concepts for

pavement design, evaluation and maintenance have risen to prominence over the last

decade. Mechanistic-based methods combine theoretical analysis of responses induced in

a pavement system under the applied loads and calculated on the basis of mechanical

properties of materials, with statistical/empirical pavement performance considerations.

Consequently, mechanistic-based design and performance algorithms need to be

validated and calibrated using data representing in situ conditions (Paterson, 1987). A

mechanistic-based statistical regression equation calibrated with good experimental data

constitutes a popular basis for developing designs, predicting performance and

formulating maintenance plans for pavement systems.

Predicting the remaining life of or distress levels in a pavement section allows

engineers and highway agencies to plan ahead for maintenance and rehabilitation (M&R)

activities, to budget for future expenses, and thereby to minimize costs (Smith, et al.,

2001). Pavement performance monitoring can provide a quantitative trend of the

behavior of a pavement system, and can supply information for a life cycle cost analysis

(LCCA) necessary when comparing M&R options. Mechanistic-based performance

2

prediction has been gaining popularity due to its perceived universal applicability, and

the lack of exclusively mechanistic alternatives.

Pavement performance and cost are two important aspects of pavement

management, and they are often interrelated. Adding or changing a feature that increases

construction costs must be justified by a corresponding increase in pavement

performance (Wilde, et al., 2000). LCCA has been an important tool for assessing the

effect of various design features by comparing costs incurred to overall pavement

performance improvement. In the case of a newly constructed pavement system,

pavement performance can be modeled using distress prediction equations, and the

LCCA methodology can be applied for cost benefit analyses. Research presented in this

thesis focuses on mechanistic-based performance prediction and life cycle costing models

for rigid pavements.

1.2 Problem Statement

One of the most controversial issues in concrete pavement design is whether joint

sealants contribute in a cost effective manner to enhance long-term pavement

performance, as quantified by the extent of distresses, like roughness, spalling, cracking

and faulting, developing over the pavement’s service life. Whereas numerous previous

studies have examined the effectiveness of joint sealant materials and processes with

regard to the performance of the sealants themselves, relatively little effort has been

expended in assessing their impact on pavement structural performance. Consequently,

3

the question of cost effectiveness and desirability of joint sealing remains unanswered.

This thesis examines the application of long-term predictions of pavement distress levels

obtained from widely available mechanistic-based procedures to the assessment of the

relative marginal life cycle costs incurred by the use of a variety of joint sealant types.

Naturally, such an exercise involves calibration to local conditions, and this is

accomplished herein using monitoring data collected between 1998 and 2001 from the

Ohio Route 50 joint sealant experiment test site near Athens, OH (Ioannides, et al.,

2002). A third issue addressed in this work pertains to the sources of the discrepancies

between predicted and observed pavement structural performance. Moreover, through a

LCCA, this investigation seeks to assess the relative marginal cost effectiveness of a

variety of joint sealant types, installed at the test site, and reapplied according to different

M&R schedules.

To embark upon this problem, performance prediction in pavements has to be

studied and understood, so as to select an appropriate distress prediction methodology.

Distress predictions for the expected life of a pavement system provide an idea of its

future performance, allowing planners to formulate M&R measures. Emphasis is laid

upon mechanistic-based performance prediction, due to the increasing significance of this

approach in pavement design.

Furthermore, using LCCA methods, the age old dilemma of the cost effectiveness

of concrete pavement joint sealing can be examined. Prior research from the Ohio Route

50 Project has rated the sealants based on the functional effectiveness of each material

configuration but has not revealed any significant correlations between this and the

overall pavement structural performance. By comparing the rehabilitation costs that will

4

accrue for each sealant material over the life cycle of the pavement system, suggestions

can be made in relation to the cost effectiveness of the different kinds of sealant material

options that have been used in the Ohio Route 50 Portland cement concrete (PCC)

pavement.

1.3 Research Objective

Listed below are the significant objectives that this research attempts to address:

1. Examine the development of performance prediction concepts and equations for

rigid pavements. Included in the literature review of this study will be methods that have

been developed to predict fundamental pavement distresses, e.g., transverse slab

cracking, spalling, joint faulting, etc., or composite pavement condition indicators, e.g.,

international roughness index (IRI), present serviceability index (PSI), pavement

condition index (PCI), etc.

2. In addition to rigid pavements, a brief discussion will be included reporting

developments in performance prediction in the field of flexible pavements. This will

enable the reader to identify and understand the presence or lack of consistency of

developments in either field. A brief summary of practices of state highway agencies

(SHA) will also be presented in order to understand the current status of pavement

performance prediction and its applications.

3. Predicted and observed distresses for the Ohio Route 50 pavement system will be

compared. Distress trends will be predicted using a suitable computer program that will

5

be selected based on its applicability, mechanistic-empirical nature, availability, and

inclusion of life cycle costing methods. These trends will be plotted and compared to the

actual field observations. This analysis will assess the precision of distress prediction and

will highlight any similarities or dissimilarities that can lead to further discussion.

4. Utilizing the existing methods for LCCA, the cost effectiveness of different kinds

of sealant materials used in the Ohio Route 50 pavement will be determined. The cost

and performance data for joint sealants will be used to perform a rehabilitation cost

analysis of sealants over the life cycle of the pavement system.

1.4 Research Significance

Performance prediction and life cycle costing are two important aspects of the

process of pavement management. Pavements are expensive parts of the transportation

infrastructure. An investment of approximately $30 billion has been made in pavements

for the U.S. Interstate Highway System alone, and billions more are spent annually on

maintenance and upgrading. Thus, even a marginal improvement in the component

technologies of pavement management can result in large absolute savings (Haas, et al.,

1994).

The current state of practice in the field of performance prediction of pavements

will be examined to understand the advantages and shortcomings of various methods that

are being used by the SHA. A review of existing predictive tools, developed and used by

the Federal Highway Administration (FHWA), the American Association of State

6

Highway and Transportation Officials (AASHTO), the Asphalt Pavement Alliance

(APA), various SHA and other organizations, along with their characteristics, advantages

and limitations will enhance the decision making process of selecting an appropriate tool

for analyzing future projects.

The application of a selected predictive tool to the Ohio Route 50 test pavement

will assess the precision of predictions made on the basis of the initial conditions of the

pavement as constructed. It can also help gage the adequacy of monitored data that have

been collected from the project. Performance and life cycle cost simulation programs are

helpful, since decisions can be made for M&R in the abstract, by exploring various

available options and examining their cost effectiveness, without actually applying them.

A possible cost effective solution for a particular sealant configuration and type that may

be used in order to obtain better performance can be formulated from the results.

1.5 Thesis Organization

The thesis is organized into six chapters. The first chapter provides a brief

introduction to the research conducted. The problem statement, research objectives,

significance and important practical implications of the work are discussed.

Chapter 2 provides an overview of the historical development of performance

prediction concepts for pavement management. A literature review pertaining to the

collection and analysis of performance monitoring data, as well as the development of

distress prediction methods and of regression formulae using such data is conducted for

7

both concrete and asphalt pavements. The period covered spans from the emergence of

the pavement serviceability concept following the AASHO Road Test (Carey and Irick,

1960) to the development of the mechanistic-empirical equations prevalent today for

performance prediction.

Chapter 3 complements the preceding review with an analysis of the existing

methods and important programs developed and implemented for pavement management

by the FHWA, SHA, etc. Information concerning statistical regression prediction models

in use by various states across the nation is compiled. For some states, a more detailed

discussion of the pavement management programs used is presented, so as to elucidate

how performance prediction and life cycle costing methods are essential to a successful

pavement management program.

A suitable program, Pavespec 3.0, is selected from among those examined in

Chapter 3, for application to the Ohio Route 50 pavement system. To begin with, a

sensitivity analysis for this program is conducted in Chapter 4, so as to document the

accuracy and plausibility of its predictions. The selected program’s prediction and life

cycle costing module is tested with artificial inputs that are very low, moderate and very

high, and its outputs are assessed on the basis of information in documented literature and

engineering common sense.

The application of Pavespec 3.0 to data from the Ohio Route 50 pavement is

discussed in Chapter 5, where predicted and observed distress trends to date are

graphically compared for individual pavement sections, and long-term life cycle and

rehabilitation cost analyses of the various sealant materials used at the site are performed.

8

Finally, Chapter 6 presents a concise summary of the findings from this research,

of its conclusions and its recommendations for future work in suggested areas.

9

2 LITERATURE REVIEW

2.1 Introduction

In this chapter, the development of distress prediction methodologies in rigid and

flexible pavements is investigated. An attempt is made to take an in depth look at the

development of performance prediction concepts and the identification of related

performance indices as well as the formulation of mechanistic, mechanistic-empirical and

statistical/empirical models that can be used to calculate these indices over the life of the

pavement. In addition, the evolution of life cycle cost analysis (LCCA) as an important tool

in pavement management is examined. Reliable performance prediction models and cost

analysis concepts are essential elements of a pavement management system (PMS). Over the

years, there has been much research in these areas that resulted in many equations and

correlations for the purpose of predicting particular pavement distress indices. Deterioration

has been predicted in terms of these indices, and that in return has helped in the formulation

of appropriate maintenance and rehabilitation (M&R) plans for the pavement. There has

been considerable research on the application of life cycle costing procedures in evaluating

the cost effectiveness of new design features and M&R operations. Many state highway

agencies (SHA) across the nation use LCCA models that they developed in-house. The

American Association of State Highway and Transportation Officials (AASHTO) Guide for

Pavement Structures (AASHTO, 1994) suggested that agency costs and user costs be

included in the economic evaluation of alternative pavement strategies. A systematic

10

approach is followed in reporting the methods used to apply the LCCA concepts to pavement

features and systems as a whole. A simple classification of the conventional pavement types

is adopted viz., asphalt concrete (AC) (flexible) pavement; composite (AC over Portland

cement concrete) pavement (COMP); jointed plain concrete pavement (JPCP); jointed

reinforced concrete pavement (JRCP); and continuously reinforced concrete pavement

(CRCP) (Lee, et al., 1993). Prediction models have been developed to calculate distresses

related to these types of pavements, and composite pavement condition indices, such as

pavement condition index (PCI), pavement condition rating (PCR), condition rating survey

(CRS) and present serviceability index (PSI).

2.2 Pavement Performance Prediction

An important step to achieve good pavement performance prediction is determination

of pavement performance. Since the inception of pavement performance monitoring tests,

such as the Strategic Highway Research Program (SHRP), Long Term Pavement

Performance (LTPP) and the National Cooperative Highway Research Program (NCHRP),

there has been a large amount of pavement performance data that has been collected and

reported by various agencies. Means to improve the determination of pavement performance

by valid data analysis is necessary for appropriate prediction models to be developed. This

aspect becomes significant when the pavement performance prediction models being used

are statistical/empirical and mechanistic-empirical in nature. A brief summary of the

11

significant research studies in the late 1990s that help improve the determination of pavement

performance is presented below.

Juang and Amirkhanian (1992) developed a simple method for a pavement

management system based on priority ranking. Information from a pavement survey is

processed using fuzzy sets and the unified pavement distress index (UPDI) is defined and

used to evaluate the pavement distress condition. Fwa and Sinha (1992) demonstrated that

incorporation of quantified monetary values of pavement performance could have a

significant impact on the outcome of engineering economic analysis. Liu and Herman (1996)

proposed a new methodology to evaluate pavement performance data by applying Fechner’s

psychophysical law. It has been found that the American Association of State Highway

Officials (AASHO) road test data, as well as road test data obtained in Texas, Canada, and

the international road roughness experiment are organized well by the simple summation of

logarithmic terms or the roadway characteristic variables. This methodology can be further

applied to handle similar types of road test data. Buch (1997) provided a framework for the

development of Weibull reliability factors for joint faulting models using field data from

SHRP’s LTPP database. Pavement performance factors, such as climate conditions, soil

conditions, and load transfer conditions, are considered for calibration using this analysis

(Pierce, et al., 2002).

Numerous prediction models have been developed using various available

mathematical techniques. They can be classified as either deterministic or as probabilistic,

depending on the nature of the values predicted. Regression models are most commonly

developed, where mathematical applications and procedures such as statistical analysis and

comparisons are used to arrive at trend lines of distresses. These trend lines and equations in

12

turn are used to predict specific pavement distresses, and to evaluate composite condition

indices.

Pavement performance, deterioration, design methods and many other relevant

concepts were addressed after the AASHO road test. Another important milestone was the

precise definition of pavement performance through the serviceability-performance concept

(Carey and Irick, 1960). Pavement performance prediction models have been classified into

two basic classes, namely, deterministic and probabilistic. These are further broken down

into structural, functional and damage for the first type and survivor curve and transition for

the second type. For operational purposes, four basic types of prediction models are defined:

(1) Purely mechanistic; (2) mechanistic-empirical; (3) regression-based; and, (4) subjective.

The first type, purely mechanistic, has not been developed yet because pavement engineers

do not use primary or fundamental response parameters as ends in themselves. Rather,

responses are only useful if they can be related to pavement distresses or to pavement

properties. Consequently, in the mechanistic-empirical approach to predicting deterioration

the fundamental characteristics of a pavement system are related to the distresses, which are

used to compute deterioration indices. Regression analysis is the most common method for

deriving correlations among these characteristics. Pure regression-based models accomplish

this just by attempting to devise statistical correlations between the distress indices and

pavement performance (Haas, et al., 1994).

Use of mechanistic principles in the development of performance prediction models

was initiated by Rauhut, et al. (1984), whose research for the Federal Highway

Administration (FHWA) focused on using a mechanistic model calibrated empirically by

field data, to evaluate the traffic damaging effects for highway cost allocation. A

13

classification of prediction models for pavements was suggested in the work of Lytton

(1987), and was detailed by Mahoney (1990). This compilation, however, was primarily

focused on models for flexible pavements. One of the more important indices that have been

devised is the PCI, which is used in many mechanistic-empirical, regression algorithms and

prediction models. An important consideration of many SHA throughout the country is that

of a prediction model’s ability to predict performance adequately for their specific designs,

materials, subgrades, traffic and climatic conditions, as the data supporting each such model

are invariably inadequate to cover the limitless combinations of these factors (Haas, et al.,

1994).

2.3 Performance Prediction Models for Flexible Pavements

Performance prediction models are better developed in the area of flexible pavements,

as these types of pavements are more frequently used all over the world. Many performance

prediction models for flexible pavements have been developed from studies conducted in

various countries. The most significant ones are briefly mentioned in this section (Paterson,

1987).

2.3.1 AASHTO Model

The model predicting pavement performance developed from the AASHO Road Test

(1958-1960) in Illinois, that was incorporated into the subsequent AASHO Interim Design

Guide, consists of one damage function for serviceability only. This model predicts the loss

of serviceability that is closely related to the roughness (Paterson, 1987).

14

2.3.2 Arizona Department of Transportation Model

The model developed for a pavement management system by the Arizona Department

of Transportation (ADOT) was derived from two databases sampling the Arizona road

network, and includes functions for roughness progression, crack initiation and progression

(Paterson, 1987).

2.3.3 Texas FPS Model

Developed for the Flexible Pavement Design System (FPS) of the Texas A&M

University by Lytton, et al. (1982), this model was derived using data from the samples of

the Texas road network, and includes functions for serviceability (roughness) and cracking

progression (Paterson, 1987).

2.3.4 Highway Design and Maintenance Standards (HDM) Model

Developed from a comprehensive, factorially designed database of in-service

pavements, the HDM III Model includes modules for predicting cracking, rutting, roughness,

etc. The cracking models are developed for various types of surfaces of flexible pavements

(Paterson, 1987).

2.3.5 SUPERPAVE Research

The development of performance prediction models for Superior Performing Asphalt

Pavements (SUPERPAVE) has been an active research topic since the late 1980s.

SUPERPAVE performance models, a key part of the SUPERPAVE software, are used to

predict how well a specific asphalt mix will withstand rutting, fatigue cracking and low

15

temperature cracking. This task is being accomplished by various SUPERPAVE research

centers across the nation, in association with the FHWA (Witczack, 1996).

In addition, there are numerous models developed from comprehensive databases that

predict cracking and rutting of asphalt pavements. The HDM III, RTIM2 Models and the

Brazil models developed by Queiroz (1981) are the more significant ones (Paterson, 1987

2.4 Performance Prediction Models for Rigid Pavements

Distress prediction models have been developed since the 1990s, with much emphasis

on transverse joint faulting, transverse fatigue cracking, transverse joint spalling, and on the

computation of the international roughness index (IRI). These models take into

consideration some or all of the important data types, including: (1) traffic data; (2) design

feature characteristic data, viz., material, Portland cement concrete (PCC) slab support,

drainage, joints, etc.; (3) climatic data; and (4) time rate of accumulation of the above

mentioned significant distresses (Hoerner, et al., 2000).

To classify the significant rigid pavement distress prediction models, the most

common distresses in rigid pavements have to be identified. For concrete pavement types,

such as JPCP, JRCP and CRCP, the significant distresses common to all the types of

pavements are: transverse slab cracking, spalling of longitudinal and transverse joints, joint

faulting, joint sealant damage and pavement surface smoothness loss.

The NCHRP-Concrete Pavement Evaluation Study (COPES) was one of the first

research projects to develop prediction models for JPCP and JRCP. Through this research,

Darter, et al. (1985) developed models that predicted concrete pavement distresses, including

16

pumping, joint faulting, joint deterioration, slab cracking, as well as the present serviceability

rating (PSR). National models were developed using the COPES database that was compiled

from six states and other studies. The models proposed to predict slab cracking for JPCP and

JRCP are given in Table 2.1.

Dossey and Hudson (1994) developed distress prediction models for CRCP for

application in the Texas Pavement Management Information System (TxPMIS). These were

statistical /empirical models specific for the Texas pavements that were developed using

twenty years of data, and can predict punch outs, patches, crack spacing, loss of ride quality

and spalling.

The composite condition indices signify the overall condition of the pavement, and

have been an important indicator of pavement condition since the AASHO road test that

defined PSI for a pavement. Mechanistic-empirical concepts have been used in developing

models that predict such composite condition indices. The Pennsylvania Department of

Transportation (PDOT) model predicts the PSI of reinforced concrete pavements solely as a

function of the pavement age. The states of Washington and Mississippi have models

developed to predict the PCR for asphalt and rigid pavements (Haas, 2001).

Lee, et al. (1993) developed models to predict the PSR for the five recognized

pavement types, with the structural number (SN), age, and cumulative equivalent single axle

loads (ESALs) as the predictor variables. The Highway Performance Monitoring System

(HPMS) databases were used to develop these models. Different model forms, such as

linear, logarithmic, and other simplified forms, were examined to develop the predictive

model and the following functional form was chosen:

dcb CESALAGESTRaPSRPSR ×××−= 1

17

where PSR I = Initial value of PSR at construction

STR = existing pavement structure : structural number for flexible pavement, total asphalt

concrete overlay thickness for composite pavement (in.) and slab thickness for concrete

pavement (in.)

AGE = Age of the pavement since construction or major rehabilitation (yrs.)

CESAL = Cumulative ESALs applied to the pavement in the heaviest traffic lane (nos.)

Regression coefficients a, b, c, and d were proposed by the authors for each of the different

types of the pavements namely, flexible, COMP, JPCP, JRCP and CRCP.

For the Illinois interstate system, the CRS is used as an indicator, with prediction

models developed to calculate this index for all kinds of pavements (Hall, et al., 1994)

proposed prediction models for the CRS, applicable to the Illinois jointed reinforced and

continuously reinforced pavements.

Pavement age is an essential factor in all the models mentioned above with some

more complex ones including roughness, traffic, and structure of the pavement. It is

interesting to note that very few of these models incorporate actual distresses to predict the

composite indices. These models have been used in many performance prediction

applications that are essential elements of pavement management system.

Pavespec 3.0 is a computer program that consists of mechanistic-based prediction

models for rigid pavements. In this research study, this program has been used for

simulating pavement performance. Pavespec 3.0 uses models that provide the trends of

major distresses in rigid pavements. A more detailed analysis of these models is provided

below.

18

Important models employed in Pavespec 3.0 include those used to calculate

transverse joint faulting, transverse slab cracking and transverse joint spalling as well as the

IRI. Over the years much research was focused on developing statistical /empirical, and

mechanistic-empirical models to calculate these distresses for concrete pavements. A

compilation of the best available models is provided in the publication for Pavespec 3.0

(Hoerner, et al., 2000).

Databases from the LTPP experiment, FHWA Rigid Pavement Performance and

Rehabilitation (RPPR) experiment, and from many other regional and statewide pavement

performance monitoring experiments, have been used to develop prediction models for

transverse joint faulting. Mechanistic-empirical models have been developed by Yu, et al.

(1997, 1998) for doweled as well as un-doweled pavements, to predict all the important

concrete pavement distresses. Statistical empirical models to predict these distresses have

been developed by Simpson, et al. (1994) for un-doweled pavements. Further contribution

has been made by Titus-Glover, et al. (1999) and by Hoerner, et al. (1999) in the form of

mechanistic-empirical models for the FHWA LTPP program. The most suitable ones among

these models have been used in Pavespec 3.0. Pavespec 3.0’s mechanistic-empirical models

are mainly for the JPCP type of concrete pavements. Calibration of these models was

performed using databases from pavement performance monitoring experiments, such as

LTPP, FHWA RPPR, NCHRP 1-19, Extended AASHO Road Test and the Mn/Road

Database. A brief description of the models that have been used for distress prediction in

Pavespec 3.0 is presented in Table 2.2.

For transverse joint faulting, the model that was developed by Yu, et al. (1998) under

the NCHRP 1-34 project and modified by Hoerner, et al. (1999) was identified as the most

19

suitable for use in Pavespec 3.0. For transverse cracking prediction the largely mechanistic

model developed by Yu, et al. (1997) under the RPPR project was selected for use. The

transverse joint spalling model developed by Yu, et al. (1997) under the RPPR project that

was modified by Hoerner, et al. (1999) was identified as the most suitable model for

application in the deterioration prediction module of Pavespec 3.0. The IRI prediction model

developed by Hoerner, et al. (1999) was used because of its inclusion of the initial IRI and

key distresses, in addition to the extensive LTPP database that was used for its development.

Furthermore, Pavespec 3.0 organized the relevant data from the national databases for

calibrating these models. Engineering assessment, statistical analysis and sensitivity analysis

methods were used to achieve this (Hoerner, et al., 2000).

20

Table 2.1 Prediction Models from the COPES study (Darter, et al., 1985)

JPCP

CRACKS = ESAL2.755 [3092.4(1-

SOILCRS)RATIO10] + ESAL0.5 (1.233

TRANGE2.0 RATIO2.868) + ESAL2.416(0.2296 FI1.53

RATIO7.31)

CRACKS = Total length of medium and high severity deteriorated temperature and shrinkage cracks, (ft. /mile) SOILCRS =0 if subgrade is fine grained = 1 if subgrade is coarse grained RATIO = Westergaard’s edge stress/ modulus of rupture (stress computed under a 9-kip wheel load) FI = Freezing Index TRANGE = Difference between the average maximum temperature in July and the average minimum temperature in January

JRCP

CRACKS = ESAL 0.897 [7130.0 JTSPACE/

(ASTEEL * THICK0.5)] + ESAL 0.10 (2.281

PUMP5.0) + ESAL2.16 [1.81/ (BASETYP + 1)]

+ AGE1.3[0.0036 (FI +1) 0.36]

CRACKS = Total length of medium and high severity deteriorated temperature and shrinkage cracks, (ft. /mile) JTSPACE =Transverse joint spacing, (ft.) ASTEEL = Area of reinforcing steel, (in.2/ft.) THICK = Slab thickness, (in.) PUMP = 0 if no pumping exists, = 1 if low severity, = 2 if medium severity, and = 3 if high severity BASETYP= 0 if granular base, = 1 if stabilized base AGE = Time since construction, years (indicator of cycles of cold and warm temperatures stressing reinforcing steel) FI = Freezing Index

21

Table 2.2 Distress prediction models used in Pavespec 3.0 (Hoerner, et al., 2000)

Transverse Joint Faulting Model

FAULT=DAMAGE0.275*[01741-

.0009911*DAYS90+0.001082*PRECIP]

FAULT=Average Transverse Joint Faulting per joint, in. DAMAGE=n/N n=Actual number of cumulative applied ESALs N=Allowable number of cumulative applied ESALs DAYS90=Number of days per year with the maximum temperature greater than 90 F PRECIP=Average Annual Precipitation, in.

Transverse Slab Cracking

% CRACKED = 100/(1+1.16 FD-1.3)

%CRACKED = Percentage of Slabs Cracked FD = Fatigue Damage

Transverse Joint Spalling

%SPALL = [AGE/(AGE+0.01)]*[100/(1+

1.005-12*AGE+SF)]

%SPALL=Percentage joints spalled (medium and high severities) AGE=Time since construction, years SF=Scaling Factor based on site, design and climate related variables

IRI Model IRI =

IRI0+0.013*%CRACKED+0.007*%SPALL+

0.001*TFAULT+0.03*SITE

IRI0=Initial Smoothness Measured as IRI, m./km. %CRACKED=Percentage of slabs with transverse cracking and corner cracks (expressed as a number between 0 and 100) %SPALL= Percentage of joints with spalling (medium to severe) TFAULT=Total joint faulting cumulated per km,mm. SITE = Site factor=AGE*(1+FI)1.5*(1+P0.075)*10-6 AGE=Pavement age since construction, years FI=Freezing Index, C-days P0.075=Percentage of subgrade material passing the #200 Sieve

22

3 SURVEY OF CURRENT PRACTICE

3.1 Introduction

In the previous chapter, the reader was introduced to the concept of mechanistic-

based performance prediction and to the development of distress prediction models since

the American Association of State Highway Officials (AASHO) Road Test (1958-1960).

It provided an introduction from a research point of view to underlying concepts and

relations employed in the prediction of distresses in pavements. In this chapter, computer

programs and applications that help forecast pavement performance through distress

prediction models are briefly examined. Some of the programs examined, e.g., Pavespec

3.0 and Micro PAVER, have been developed from concepts and relations presented in the

previous chapter. The purpose here is to investigate the usage of mechanistic-based

performance prediction tools, and examine their suitability for a variety of possible

applications. Experiences in performance prediction by various state highway agencies

(SHA) across the nation will be assembled, and brief descriptions of methods used

around the country will be presented. This will enable the reader to understand the

current state of affairs with respect to mechanistic-based performance prediction, and also

to assess possible needs to adopt such practices in the future. A subsequent chapter will

apply a selected computer program to the Ohio Route 50 test pavement, and will present

an analysis of the results obtained.

23

3.2 Computer Programs for Pavement Performance Prediction

Statistical regression formulae for the prediction of pavement performance and for

life cycle cost analysis (LCCA) have been an integral part of pavement management

systems (PMS) used by SHA since the emergence of computerized analysis methods in

the transportation sector. The first such pavement management tools were developed in

the mid-1970s, and at present the most advanced are those applied in North America.

They have been helpful for the planning, and maintenance and rehabilitation (M&R)

processes that require assessment of different scenarios of pavement deterioration. In

order to perform effective planning, prediction models should have the ability to assess

the performance of any chosen set of M&R actions performed over the life cycle period.

Prevention of pavement deterioration is partly dependent on the type of treatment used

for M&R, and, therefore, a realistic prediction algorithm will help monitor the different

performance paths that the pavement system might traverse when a certain option is used.

With the increased use of computers, which permit the maintenance of large

databases and the development of relationships from data collected through sophisticated

statistical analyses, performance prediction has undergone a rapid evolution. Many SHA,

as well as federal organizations, army research centers and universities across the nation

have developed and implemented computer programs for pavement management,

consisting of modules for performance prediction and life cycle costing. Many programs

are currently being used by SHA for pavement infrastructure management and future

planning. Moreover, the data from the Long Term Pavement Performance (LTPP) of the

24

Strategic Highway Research Program (SHRP) and from WesTrack experiments have

been of much use for calibration of pertinent algorithms in order to increase the reliability

of predictions.

Existing programs for personal computers can be broadly grouped on the basis of

their applicability to specific types of pavement systems and of the functions they

perform. Many of these programs have stand-alone application modules for deterioration

prediction, selection of maintenance options and cost analysis, and these are linked with

each other on the basis of fundamental concepts. A brief overview of computer-based

pavement management application packages and their modules is presented in Fig. 3.1.

Important modules of a good pavement management system include: (1) performance

prediction module; (2) planning, maintenance and rehabilitation module; (3) LCCA

module; and (4) pavement performance database. Some significant outcomes are: (1)

distress predictions and remaining life estimates; (2) pavement management and

maintenance strategies; (3) evaluation of such options and strategies through LCCA; and

(4) improved planning methods for future pavement systems. The structure shown in

Fig. 3.1 is generic, but it may involve further levels and classifications for each module.

The type of database, methodologies used in the performance prediction modules, LCCA

methods employed and the kinds of pavements to which these models apply, are some of

the additional distinctions.

25

3.2.1 Programs for Rigid Pavements

There are many software packages that address pavement performance prediction

and life cycle costing in one way or another. For example, various programs were

developed during the 1990s that address issues such as early-age concrete pavement

behavior and performance prediction (HIPERPAV), M&R options and costing for

concrete pavements (SAPER), life cycle costing for rigid and flexible pavements

(LCCOST- Asphalt Institute, LCCP/LCCR- Maryland), etc. The objective of this

research, however, is to investigate the use of mechanistic-based performance prediction

for rigid pavements. For this reason, only the most significant of applications developed

for such pavements to predict performance and thus establish life cycle costs for

assessing design or maintenance alternatives, are summarized below. The programs were

selected based primarily on the criteria that: (1) the application should address distress

prediction of rigid pavements based on mechanistic-based models; (2) an LCCA module

must be present to evaluate the effectiveness of M&R measures; and, (3) the program

should include various input options that encompass a wide variety of climate, traffic,

design, M&R variables present across the nation. Table 3.2 provides information related

to the applicability, prediction methodology, and LCCA techniques applied in programs

that are considered appropriate for this research.

Pavespec 3.0 (FHWA)

Developed by Turner-Fairbank Highway Research Center (TFHRC), a division of

the research, the development and technology section of Federal Highway Administration

(FHWA), Pavespec 3.0 is a program that can be used in developing performance related

26

specifications (PRS) by predicting the performance of an as-constructed or to be

constructed pavement. Mechanistic-empirical distress prediction equations are used in

this program to model the performance of rigid pavements based on a specific set of

indicators. Furthermore, calculated distress results are used to compute life cycle costs,

pay factor charts, etc., which are helpful in developing PRS for the pavement. A

complete description of the abilities of Pavespec 3.0, and the prediction models

incorporated into it, has been provided in detail by Hoerner, et al. (2000). Pavespec 3.0

has the following capabilities: (a) Simulation of pavement performance in terms of (1)

transverse cracking, (2) transverse joint faulting (3) transverse joint spalling, and (4)

pavement smoothness over time; (b) Applications of a user-defined M&R plan to

compute life cycle costs; (c) Development of pay factor charts for the following

acceptance quality characteristics (AQC): (1) strength; (2) thickness; (3) air content; (4)

smoothness; and, (5) consolidation around dowels; (d) Computation of contractor pay

factors from actual construction test results for the five AQC mentioned; (e) Assistance in

executing sensitivity analyses for a given PRS to be developed.

RPLCCA (Center for Transportation Research, TxDOT)

Rigid Pavement Life Cycle Cost Analysis (RPLCCA) was developed by the

Center for Transportation Research of the University of Texas at Austin, for training and

implementation by the Texas Department of Transportation (TxDOT). This program is a

life cycle costing methodology for Portland cement concrete (PCC) pavements that

considers all the aspects of pavement design, construction and maintenance, as well as

user impacts during the entire analysis period. It predicts the pavement performance

27

using state-of-the-art performance algorithms and reliability concepts, from which M&R

needs are established. The modular nature of its methodology allows it to incorporate

accurate performance prediction models, as well as user costs and external costs

associated with highway construction. The development history of and user guidelines

for this program have been provided by Wilde, et al. (2000) and by Waalkes, et al.

(1999), who have also presented a sensitivity analysis. Important features of this

program are: (a) Performance models that predict the level of selected distresses to be

expected based on the construction, environmental conditions plus traffic loading over

time; (b) Incorporation of reliability concepts in the prediction models due to the

variability of inputs and outputs; (c) Usage of mechanistic-empirical and

statistical/empirical models; (d) Complete LCCA of the pavement system considering the

available M&R options.

The analysis includes agency, user and external cost components. RPLCCA

offers a very logical and organized approach to pavement performance modeling and life

cycle costing. A shortcoming of this program is that most of the factors considered,

including costs and rehabilitation options, are pertinent to TxDOT; consequently its

applicability as a general program to be used elsewhere is limited.

POWERPAVE (CRSI)

POWERPAVE is a computer program developed by the Concrete Reinforcing

Steel Institute (CRSI) that allows users to predict the early-age performance of

continuously reinforced concrete pavement (CRCP) systems. This is an easy-to-use tool

that optimizes CRCP options to allow owners, designers, and contractors to reduce risk,

28

control costs, and maximize investment returns and performance. Performance

predictions in POWERPAVE are based on design assumptions, such as material quantities

and types, as well as construction assumptions, including environmental factors and

construction methods. The software features a Microsoft Windows-based interface that

accepts inputs related to climate, construction methods, materials, traffic and other

significant factors. The program predicts early-age performance based on mechanistic

methods, but it is only applicable to CRCP systems.

Micro PAVER (US Army Corps of Engineers)

Micro PAVER is a PMS that aids pavement managers in deciding when and

where to appropriate funds for pavement M&R. Micro PAVER provides pavement

management capabilities to: (1) develop and organize the pavement inventory; (2) assess

the current condition of pavements; (3) develop models to predict future conditions; (4)

report on past and future pavement performance; and (5) develop scenarios for pavement

maintenance based on budget or condition requirements. Micro PAVER inventory

management is based on a hierarchical structure composed of networks, branches, and

sections, with the section being the smallest managed unit. This structure allows users to

organize their inventory easily, while providing numerous fields and levels for storing

pavement characteristics. Micro PAVER uses the “family method” developed by Nunez

and Shahin (1986) and by Shahin and Walther (1990), through a research program at the

U. S. Army Construction Engineering and Research Laboratory (CERL).

Micro PAVER has the following modules and applications to help monitor

pavement deterioration and formulate M&R options:

29

1. Inventory: The Micro PAVER program allows users to store pictures of individual

sections, sample units and distresses that can act as a repository of information.

2. Field Inspection: Micro PAVER enables users to fill out a standard form that can

be entered and edited in the database.

3. Condition Analysis: Data from prior inspections, predictions and analyses help

visualize the condition of pavement over the specified period of time.

4. Prediction Modeling: The program identifies groups of pavements that are

expected to be subjected to similar traffic, weather and loading conditions.

Historical data about such “pavement families” are used to develop the condition

prediction model (CPM) for the future maintenance. The CPM is designed to

permit users to blend knowledge about their pavements and local condition

measurements with powerful modeling tools to produce accurate estimates of

future pavement life.

5. Work Planning: The M&R plan utilizes basic inventory data combined with

inspection information, maintenance policies, maintenance costs and predictions

about future pavement condition to develop a work-plan specific to a site.

These features make Micro PAVER an important tool in pavement management.

This system is used by the U. S. Army, several SHA and many other organizations.

There are over five hundred users of MicroPAVER across the U.S, Canada and in Asia.

Pavement performance is predicted on the basis of grouping and networking data

gathered previously.

30

Highway Design and Maintenance Standards Model (HDM) (World Bank)

The Highway Design and Maintenance Standards Model (HDM) computer

program was developed by the World Bank for evaluating highway projects, standards,

and programs in developing countries. HDM has been undergoing continuous

improvement and has been released in several versions, the current one being HDM-4. It

is used to make comparative cost estimates and ergonomic evaluations of different

construction and maintenance options for a given road section or a road network. HDM-

4 assumes that construction, maintenance and vehicle operating costs are functions of

vertical alignment, horizontal alignment and road surface conditions. One of the major

disadvantages of this program is that it does not specifically model PCC pavements.

3.3 Programs for Life Cycle Cost Analysis

LCCA is an important methodology through which state agencies can evaluate the

effectiveness of new design features and maintenance as well as rehabilitation plans for

pavement systems. A list of the most notable LCCA programs that have been developed

for rigid and flexible pavements was compiled by Wilde, et al. (2000) as a part of the

research to develop RPLCCA for the TXDOT. What follows is a summary of

information provided by Wilde, et al., (2000).

31

LCCOST (Asphalt Institute)

The program LCCOST was developed by the Asphalt Institute. It incorporates the

initial construction costs, multiple rehabilitation action costs and user costs at the work

zones during the construction and rehabilitation activities. Yet it does not have a

performance modeling module nor does it have a structural pavement model. One

important aspect is that this program considers the routine maintenance costs that are

often ignored by transportation agencies.

DARWin (AASHTO)

The DARWin pavement design system is a program that automates the American

Association of State Highway and Transportation Officials (AASHTO) design equations

and simplifies the management of materials, layers and construction activities. The life

cycle cost module of DARWin accounts for project dimensions, initial construction, up to

five preprogrammed rehabilitation strategies, and the salvage value of the pavement.

This program focuses primarily on calculating agency costs associated with specific

projects rather than the complete LCCA.

LCCP/LCCPR (University of Maryland)

This set of programs was developed by the University of Maryland for rigid as

well as flexible pavements. These programs incorporate user operating costs associated

with pavement roughness and other measures of user costs. They were developed for

project-level analysis.

32

EXPEAR (FHWA)

The EXPEAR application was developed by the University of Illinois for the

FHWA. It performs project-level evaluation and requires data from visual evaluation

surveys. Based on these surveys, the program recommends rehabilitation techniques,

including reconstruction, resurfacing, and minor rehabilitation techniques. It does not,

however, does not consider user costs or other indirect impacts of the recommended

rehabilitation techniques.

MicroBENCOST (Texas Transportation Institute)

This program was developed in 1993 under the NCHRP project 7-12 by the Texas

Transportation Institute (TTI). It analyzes many types of projects including pavement

rehabilitation, added lane capacity, bridge, and bypass construction projects. It takes a

large number of inputs and compares a benefit/cost analysis that considers costs with and

without the specific alternatives. Its main function is to compare different alternatives

and evaluate the benefits and costs of selecting a particular alternative.

Real Cost 2.1 (FHWA)

Real Cost 2.1 is a Microsoft Excel based application developed by the FHWA for

performing deterministic as well as probabilistic life cycle costing of pavement

rehabilitation alternatives. The procedures followed for performing this analysis follow

the recommendations of the ‘Pavement Division Interim Technical Bulletin’ prepared by

Walls and Smith (1998) for FHWA. This bulletin outlines procedures for application of

LCCA for pavement design and rehabilitation. Real Cost 2.1 can consider the relevant

33

economic variables, project data, and available design alternatives to output simulated

life cycle costs, which are either deterministic or probabilistic.

In addition to the ones mentioned above, there are many other LCCA modules

developed internally by various SHA to perform cost evaluations of the feasible M&R

options.

3.4 Performance Prediction – Current State-of-the-Practice

The concepts of pavement performance prediction and LCCA have been

incorporated in the PMS of several SHA in the United States. A PMS provides

information necessary to find cost effective strategies for evaluating and maintaining

pavements in a serviceable condition. The basic components of a PMS are: (1) a

comprehensive database that contains current and historic pavement condition, traffic and

structural data; (2) a set of tools that assist in determining the current and future

pavement conditions, which are then used to predict financial needs for M&R operations

(Haas, et al., 1994). Performance prediction involves statistical/empirical methods,

whereas financial assessment relies on LCCA techniques.

To comprehend the state-of-the-practice of pavement performance prediction

among various SHA, a typical data flow chart in a PMS is useful. Figure 3.2 is a generic

data flow diagram for a PMS. It shows that data collected can be distinguished into: (a)

construction data obtained during pavement construction; and (b) condition data from

existing in-service roads. The data are allocated between three main destinations: (i) a set

34

of individual case studies investigated; (ii) a research database concerned with the

influence of a number of variables; and (iii) planning and reporting activity database,

serving the entire network (Smith, et al., 2001). The PMS incorporates equations

developed using these data for various purposes, as shown in the Figure 3.2. Using

statistical/empirical correlations, such algorithms can predict pavement structural

distresses or calculate composite indices, such as pavement condition rating (PCR),

present serviceability index (PSI), remaining service life (RSL), etc.

The following section provides a detailed description of the various aspects of a

PMS, to give the reader a better idea of the need for pavement performance prediction.

Along with brief explanations, methods and techniques used by various SHA to

accomplish the requirements for each task of a typical PMS are presented.

3.5 Pavement Management System Framework

An efficient PMS hinges on reliable data, appropriate methods of analysis to

identify sections needed for repair, and accurate prediction models that help in decisions

related to M&R of pavements in a timely fashion and within the allocated budget.

Application of a PMS involves several stages, such as data collection, data categorization

and analysis, performance modeling and formulation of recommendations for M&R. The

fundamental objective of a PMS, the performance prediction, and the cost analysis are

common to most SHA. The difference, however, lies in the procedures implemented by

each SHA, for each of the stages mentioned.

35

3.5.1 Distress Data Collection

The primary sources of pavement performance data are visual or automated

inspections conducted during construction and regular condition surveys of pavement

sections. High quality, reliable data form the substructure of a good PMS.

The Illinois Pavement Feedback System (IPFS) has international roughness index

(IRI), rutting, faulting and surface type data collected every two years. Information about

D-cracking, pavement construction, rehabilitation, reconstruction and repair, as well as

average daily traffic (ADT), equivalent single axle loads (ESALs), truck traffic, traffic

growth rates, etc., has also been procured (Bham, et al., 2001b). In Texas, pavement

condition data are collected annually from the highway network by visual inspection.

These include distresses such as patching, rutting and pavement roughness, deflection

data reflecting pavement strength, and skid data (Smith, et al., 2001). In Colorado,

primary distress data related to rutting, faulting, spalling, cracking, corner breaks,

bleeding, raveling, etc., and peripheral data pertaining to the shoulder type, shoulder

condition and width are collected every two years. Colorado Department of

Transportation (CDOT) uses an automated data collection vehicle for rut and ride

condition data, whereas all other data (from pavement type to distress severity) are

deduced from video tapes (Farrokhyar, et al., 2001).

3.5.2 Data Categorization and Analysis

Data collected for a PMS can be simply categorized as follows: (1) pavement type

information; (2) pavement distress data; (3) traffic data; and (4) maintenance information.

36

A condition indicator is assigned for each distress. Such indicators provide the SHA a

better idea of the current condition of a pavement section or network, with respect to

significant distresses, enabling personnel to take preventive maintenance actions. After

an initial condition assessment, prediction modeling concepts can be used to determine

the appropriate M&R procedures needed, and the associated costs.

Texas Department of Transportation (TxDOT) categorizes pavements in the

following types: (1) CRCP; (2) jointed concrete pavements (JCP); (3) asphalt concrete

pavements (ACP); each is then classified further based on other criteria, such as thickness

of asphalt layer, presence of reinforcement, etc. Table 3.1 lists the distresses reported for

each pavement type during visual inspections. For every pavement section monitored,

each one of the distress types is attributed a rating. Initial analysis is performed to

compute distress scores (DS) from such distress ratings, ride utility scores (RUS) using

measured ride values, and a final condition score (CS) calculated from DS and RUS

values. CDOT uses a similar methodology by initially attributing a rating (0 to 100) to

each pavement distress. Index values are computed from the distress ratings for ride

roughness, rutting, alligator cracking, block cracking, longitudinal cracking, transverse

cracking, load associated longitudinal cracking and corner breaking.

3.5.3 Pavement Performance Modeling

The fundamental use of pavement performance modeling is in predicting the

behavior and related costs of various M&R options for in-service pavement sections.

During the planning and design stages, reliable performance modeling can help predict

37

the pavement system behavior and life cycle costs, thereby helping SHA make effective

decisions.

Performance Curves and Equations

Performance curves and equations are most commonly implemented by SHA in

predicting pavement performance. Historical data are grouped by pavement type, traffic,

geographic region, climatic conditions etc., and appropriate performance prediction

curves or equations are generated. Statistical regression analysis and probabilistic

techniques are commonly used in the development of such curves or equations.

The Illinois Department of Transportation (IDOT) uses the historical CRS, IRI,

Annual Average Daily Traffic (AADT) and rutting information from various Interstate

sections as input parameters for the prediction models. These data are used to customize

the prediction equations programmed in the PMS software for each pavement type to

generate trends for CRS, IRI, rutting and traffic forecasting for each section of the

highway system. Examples of the prediction equations used are presented below:

YearsValueDeductCRSCurrentCRSFuture ×−= (3.1)

YearsaRutCurrentRutFuture ×+= (3.2)

YearsaIRICurrentIRIFuture ×+= (3.3)

where, Deduct Value and a are parameters obtained from historical data.

TxDOT Pavement Information Management System (PIMS) uses sigmoidal (S-

shaped) distress curves. Most of the PIMS distress types are assigned performance curve

parameters based on empirical analysis of past performance of similar pavement sections

(Smith, et al., 2001). In the state of Colorado, CDOT uses performance curves that are

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site-specific developed using historical data collected from the site; pavement family

curves developed by grouping pavements based on type, traffic, climate, thickness; or

default curves. If historic data for more than five years are available for a section, site-

specific curves are used. In case data are not sufficient, pavement family or default

curves are utilized to project the trend of each distress type.

3.5.4 Application of Performance Prediction

Applications of performance modeling are numerous, spanning the areas of

pavement design, planning, construction and maintenance. Among various SHA,

performance modeling is used mainly to help in the decision making process related to

M&R operations for the pavement network. Alternative M&R treatments are examined

for their effectiveness by predicting the performance, and life extension of the pavement

system with the specific treatment options. Additionally, due to budget limitations, the

best pavement M&R treatment has to be applied to the pavement system that is projected

to deteriorate the fastest. Thus, performance modeling helps optimize the limited

monetary resources of SHA.

Maintenance and Rehabilitation

IDOT PIMS has a decision tree built into its performance modeling that conducts

a cost benefit analysis of each available M&R treatment option using weighted benefit

ranking methods. The input parameters for this analysis are the effects of rehabilitation

on CRS, IRI, and rutting for various types of pavements, unit costs for each potential

39

rehabilitation alternatives, and allocated budget. The analysis generates an optimized

multi-year pavement improvement program.

In the case of Texas, four treatment levels are specified: (1) preventive

maintenance; (2) light rehabilitation; (3) medium rehabilitation; and (4) heavy

rehabilitation or reconstruction, with detailed actions and options for each pavement type

built into the state PIMS. Based on the field data of distress scores, ride roughness,

rutting, etc., prediction equations are used to generate the performance trends for a

pavement section when each one of the four maintenance options is chosen. Based on a

cost benefit analysis performed subsequently, an M&R program is selected.

CDOT uses performance curves to predict the pavement distresses over the

pavement’s life cycle. A threshold value is assigned for each distress that will help

calculate the RSL of the pavement for each distress viz., ride, rut, alligator cracking,

block cracking, longitudinal cracks, transverse cracks for asphalt pavements, and ride,

rut, longitudinal cracks, transverse cracks and corner breaks for concrete pavements.

Various strategies are evaluated with the help of expert opinion to determine the M&R

treatments necessary based on RSL, costs associated and benefits provided by each

option.

Planning and Design

At the statewide pavement network level, performance modeling can help SHA in

planning and allocating resources better. Distress predictions for each section of a

pavement network can provide an idea of the projected repairs, and costs associated, thus

assisting the SHA in budgeting funds appropriately.

40

The IDOT PIMS uses Geographic Information System (GIS) based spatial

mapping of the entire pavement network. The system provides a graphical outlook of the

predicted pavement distress (CRS, IRI, rutting, etc.) for a selected pavement section.

Rehabilitation projects and associated costs based on each pavement distress can be

extracted from the system. Such analysis helps the IDOT allocate funds efficiently or

optimize spending on M&R projects within budget.

Other Applications

In addition to assistance in maintenance, performance modeling helps during the

construction phase of pavements. Prediction modeling can determine pavement

distresses over a pavement’s entire life cycle using initial inputs such as pavement type,

material and structural data, traffic projections, climate, etc. Such analysis will help state

agencies make the best decisions, and also assess pay factors for contractors. Pavespec

3.0, the program selected for this research, is one such application and uses mechanistic-

based performance modeling for jointed concrete pavements (JCP) to project distresses

and generate pay factor curves.

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Table 3.1 Distresses rated during inspections for the TxDOT PIMS pavement types

CRCP DISTRESS TYPE JCP DISTRESS TYPE ACP DISTRESS TYPE

Spalled Cracks

Punch outs

Asphalt Patches

Concrete Patches

Average Cracking

Spacing

Failed Joints and

Transverse Cracks

Failures

Shattered Slabs

Slabs with Longitudinal

cracks

Concrete Patches

Apparent Joint Spacing

Shallow (6-12 mm) rutting

Deep (13-25 mm) rutting

Patching

Failures

Block Cracking

Alligator Cracking

Longitudinal Cracking

Transverse Cracking

Raveling (Optional)

Flushing (Optional)

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43

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Fig. 3.1 Computer based pavement management system, modules and outcomes

Computer based Pavement

Management Program

Pavement performance

Database

Performance or deterioration

prediction module

Maintenance and

Rehabilitation Module

Life Cycle Cost Analysis Module

Pavement Management

Plans and Strategies

Performance predictions in

terms of deterioration/ remaining life

etc.

Evaluation of the available M&R options via life cycle cost analysis

Improved planning for construction

and maintenance of

future pavements

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Fig. 3.2 Data flow diagram for a Pavement Management System (Smith, et al. 2001)

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4 SENSITIVITY ANALYSIS USING Pavespec 3.0

4.1 Introduction

In the previous chapter, various available computer programs for performance

prediction and life cycle costing were examined. For the current research undertaking,

Pavespec 3.0 was selected as an appropriate application. Pavespec 3.0 incorporates

mechanistic-empirical prediction models for rigid pavements, forecasts the performance of a

pavement system in terms of the significant distresses, and includes a module that provides

an approximate estimate of the life cycle and present worth costs of a specified pavement

system. Before Pavespec 3.0 is applied to the Ohio Route 50 Project data to generate distress

predictions to be compared with actual performance, a sensitivity analysis will be conducted.

Computer models that predict the performance and costs of design features of

pavements usually rely on data and empirical methods. In some cases, this reliance could act

as a drawback. A program that is developed and tested using a finite set of data may not

work properly for a completely different data set. Consequently, the need arises to conduct a

detailed sensitivity analysis, which involves examining the outputs produced by Pavespec 3.0

for different sets of input values. The traditional method utilized in conducting such an

analysis is to change one variable at a time, run the program and record the output. Another

approach available is to account for the interdependencies of variables and select

combinations of input variables to create outputs. The latter becomes complex as the number

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of input variables increases. For the current research, which has identified seventy-nine

inputs, the former method is employed.

The primary objectives of this analysis are to identify the most significant input

variables in the program and to evaluate the reliability of outputs generated. Such analysis

will help the user assess whether the program responds as expected to the more important or

critical input variables. Expert opinion and existing performance trends can help verify the

reliability of outputs. The following steps have been performed to accomplish this objective:

1. The input variables for program Pavespec 3.0 are identified and classified by the

modules present in the program. These modules are: dimension and lane configuration,

pavement design, design traffic, climate data, acceptance quality characteristics (AQC)

sampling and testing, AQC target values, maintenance and rehabilitation (M&R), cost data

and analysis, and simulation control. A total of seventy-nine input variables for Pavespec 3.0

are identified and tabulated in Table 4.1, in which details of the inputs and the values

assigned for further analysis are also presented.

2. The input variables identified are assigned three levels of values to generate outputs

for subsequent comparisons. The first set of “medium” values consists of input variables that

are generally close to the Ohio Route 50 Project data, and form the baseline for comparisons.

The second set of “low” values would most likely result in low distresses. Finally, the last

set of “high” values would most likely result in high values of pavement distresses. This

information is presented in detail in Table 4.1.

3. Over a hundred and fifty simulation runs of Pavespec 3.0 are performed using these

sets of input values. The service life of pavement system in all the cases has been assumed to

be twenty-five years. Pavement distresses that are predicted by Pavespec 3.0 include:

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transverse slab cracking percentage, transverse joint spalling failure percentage, transverse

joint faulting and pavement smoothness. For the life cycle costing module, inputs such as

unit costs, inflation percentage, depreciation, user cost percentage, etc., provide the total life

cycle costs incurred and present worth of these costs for the pavement system.

At the end of this chapter, for each distress predicted by Pavespec 3.0, corresponding

input variables that are highly significant and moderately significant are determined. In

Table 4.2, the bold and italic font distress values represent the outputs that changed

considerably with varying input values. The inputs that correspond to bold font distress cells

in Table 4.2 can be termed as of high significance. Discussion presented in the following

section explains this further by examining the input variables in each input module.

4.2 Discussion of Sensitivity Analysis Observations

The sensitivity analysis results for all the identified inputs of Pavespec 3.0 are

summarized in Table 4.2. Subsequent paragraphs describe the effect of varying inputs on the

outputs generated. The results are categorized by module.

4.2.1 Pavement Design, Dimensions and Lane Configuration

For a given average daily traffic (ADT), the two- and four-lane undivided

configurations incurred much more user costs (vehicle operating, delay and accident costs,

etc.) as expected. The divided configurations showed only slight increase in costs as the

number of lanes increased. It has to be noted that Pavespec 3.0 considers the M&R and user

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costs, but not the initial construction costs. As the lane width increases, the cost indices

change as expected, while the distresses remained the same all through. The pavement

shoulder type used influences both distresses and costs. A widened lane is the most

inexpensive and best performing of the three shoulder types considered in terms of distress

indices; asphalt and tied Portland cement concrete (PCC) shoulders showed somewhat higher

distresses. A lower value of stress load transfer efficiency (SLTE) showed a considerable

increase in the spalling percentage and in the overall costs. The inner lane cracking

percentage had no marked effect on any of the output values. The road location, viz., rural or

urban, changes the total cost values but not the distresses. The design life of the pavement,

as expected, does not alter the distresses or costs at a given point of time. An undoweled

jointed plain cement pavement (JPCP) results in more costs and increased distresses

especially spalling and international roughness index (IRI), when compared to a doweled

system. It appears that the optimum dowel diameter is 1.25 in., since smaller and larger

diameters are associated with increased distresses and costs. As expected, slabs of smaller

length result in lower distresses and higher costs, but joint spacing of 30 ft. only results in a

slight increase in the distresses compared to 20 ft. Mild variation in the PCC modulus of

elasticity does not affect the distresses or the costs to a great extent. The effect of water-

cement ratio was pronounced in terms of the resultant joint spalling. The preformed

compression sealant provided best performance with negligible spalling when compared to

asphalt or silicone sealant materials. Spalling for asphalt and silicone sealant materials was

observed to be the same.

When the subgrade modulus, k, was small, it resulted in more distresses viz., faulting

and transverse cracking. Negligible transverse slab cracking and joint faulting values were

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observed when k approaches the elastic solid limit (k = 500 pci). Increased finer material in

the subgrade increased the IRI of the pavement. The base layer permeability did not

influence the distresses or life cycle costs. A thin base layer resulted in an increased

transverse slab cracking percentage. All the other distresses did not change much with the

thickness of the base layer. The modulus of elasticity of the base layer did not affect the

distresses or life cycle costs considerably. A bonded PCC base interface eliminated the

transverse slab cracking percentage and reduced the IRI, when compared to the unbonded

case. Smaller values of the base erodibility factor resulted in lower joint faulting, IRI values

and costs.

4.2.2 Traffic Factors

Variations in the ADT produced outputs as expected: an increase in the ADT values

resulted in higher distresses and related costs. An increase in the traffic growth rate or a

variation from simple to compound interest computation of traffic growth resulted in

increased distresses and life cycle costs. Traffic directional factor had a considerable effect

on the distresses and the costs of the pavement. An increase in the percentage directional

factor resulted in increased distresses and costs incurred. Also, percentage trucks in the outer

lane showed a marked effect on the transverse slab cracking percentage. Other distress

indices, such as joint faulting and IRI, and life cycle cost values showed only a slight

increase due to higher truck percentage in the traffic mix.

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4.2.3 Climatic Factors

The average annual freezing index is found to influence the IRI values of the

pavement. A higher index resulted in higher IRI values. High annual precipitation caused

more joint faulting and increased the IRI values, thus resulting in more costs. The number of

freeze-thaw cycles considerably influences the spalling failure percentages of the PCC slabs,

consequently increasing the life cycle costs of the pavement system. Warmer climate

resulted in better performing pavements since an increase in the number of days over 90°F

resulted in decreased faulting and IRI values. Influence of climatic zone designation was

observed to be pronounced in terms of joint spalling percentages. Wet non-freeze climate

zone produced maximum spalling and IRI values, followed by the dry non-freeze, wet freeze

and dry freeze climate zones, respectively. Cost differences were also noticed between the

climatic zones due to the distresses that were observed.

4.2.4 Sampling Methods

The AQC used in Pavespec 3.0 for distress prediction, cost analysis and pay factor

calculations are: strength, PCC slab thickness, air content and IRI. Variations in sampling

methods and techniques for each of these AQC may change the predicted outputs. Strength

sampling options provided are beam, core and cylinder tests. Beam tests give the flexural

strength of PCC, whereas cores or cylinders give the compressive strength. There were

miniscule differences observed in the results predicted for these sampling methods.

Thickness sampling methods of cores, probes or ground penetration made no difference to

the obtained distress values. Air content sampling methods of the Danish Void Analyzer

(DVA), air pressure meter and cores had very little impact on the results. The profilograph

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reduction using manual or computerized methods made negligible difference to the final IRI

value. Life cycle costs and distresses that were predicted by simulation from the initial seed

values provided were observed to be slightly higher than the ones predicted just by

considering the initially specified mean values for input parameters.

4.2.5 Acceptance Quality Characteristics

AQC are the parameters of the pavement system, which can be measured during and

after construction and can be used to develop pay factors for the contractors. These are the

concrete strength, slab thickness, air content, initial smoothness and consolidation around

dowels. The distresses and life cycle costs predicted need to be appropriately sensitive to

these values. Concrete strength influences the transverse slab cracking, spalling and IRI to a

considerable extent, with a decrease in strength resulting in increased distresses. Joint

faulting is not affected much by the concrete strength. The distress indices of transverse slab

cracking percentage, spalling percentage, and IRI are dependent on the slab thickness. A

thinner PCC slab vastly increases these distress measures, as expected. Joint faulting is

influenced by the slab thickness only to a small extent. Air content in concrete affects the

spalling percentage to a greater extent than it does transverse slab cracking, smoothness and

joint faulting. Spalling percentage increased when the air content in the sample drops to 3%.

Varying the initial pavement smoothness values did not affect the distress indices

considerably. Changing the percentage consolidation around dowels influenced only the

joint faulting values. Increase in the percentage consolidation around dowels resulted in

decreased joint faulting and hence decreased costs.

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4.2.6 Maintenance and Rehabilitation Options

In this section, the M&R options for the pavement system are altered to examine the

influence of each on the distresses and the life cycle costs. Replacing the sealants of a higher

percentage of joints at shorter time intervals slightly decreased the spalling and increased the

costs as expected. Repair of cracked and spalled joints at regular intervals did not show

much of an effect on the costs, probably due to the fact that in the general case, spalling and

cracking failure predicted by the program was minimal. Joint faulting was limited to a value

after which failure was said to have occurred and repair was to be performed. This did not

affect the distresses produced and costs incurred. On a similar note, IRI limiting values

defining failure conditions were regulated. This measure did not result in changes in the

pavement costs.

The global rehabilitation options available were asphalt concrete overlays, PCC

overlays and diamond grinding process. There were small variations in the costs incurred for

each rehabilitation option. Also, as the expected life of rehabilitation treatment extended, an

expected decrease in the total life cycle costs of the pavement system was observed.

4.2.7 Costs and Interest Rates

Results show that increasing the unit cost of sealing per joint greatly affects the total

life cycle cost of the pavement system. As predicted, greater unit costs for slab replacement

and joint repair resulted in elevated life cycle costs. As the annual inflation rate increased,

decreases in the life cycle and the present worth costs of the pavement were observed. This

is appropriate as an increase in inflation leads to the multiplication of costs annually. As the

time of construction of the pavement system considered was assigned to be 1998 (the

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construction date of the Ohio Route 50 pavement system), one should observe a decrease in

costs as inflation increases. The pavement system distresses, as expected, remain unchanged.

Annual interest rates influenced the pavement system life cycle costs noticeably as a small

increase triggered a jump in the calculated costs. The user cost percentages that need to be

included for life cycle cost calculations are found to be crucial. As these calculations are

based on M&R costs and the percentage user costs, increasing the user cost percentage

sharply increased the calculated life cycle costs. Pavespec 3.0 recommends performing 500

trial simulations (referred to as “lots” in Pavespec 3.0) so as to assess the impact of each

factor reliably, yet it is found that the number of lots influenced the output values only

slightly. Smaller number of lots resulted only in a small increase in the life cycle costs. It

has to be noted that increasing the number of simulations enhances the quality of pay factor

curves that Pavespec 3.0 develops for performance related specifications (PRS). The

analysis life of the pavement system necessarily influences the distresses that occur at the end

of service life and the costs that are incurred. Extending the analysis life of the pavement

resulted in increased distresses and higher costs at the end of the time period.

4.3 Summary of Major Findings from Sensitivity Analysis

By observation, the factors that are of high importance in relation to pavement

distress occurrence can be noted. Pavement type (doweled, undoweled), dowel bar diameter,

percentage consolidation around dowels and PCC slab thickness inputs seemed to influence

the pavement joint faulting to the greatest extent. To a moderate extent, the shoulder type,

55

modulus of subgrade reaction (k-value), base erodibility factor, ADT, percentage trucks,

directional factor, and the average annual precipitation influenced the joint faulting measure.

For the PCC slab transverse slab cracking, a similar analysis was conducted.

Variations in concrete strength, PCC slab thickness, climatic zone, traffic factors and truck

percentages, base thickness and PCC base interface, k-value of subgrade, transverse joint

spacing, and SLTE produced changes in the percentage slabs cracked to a considerable

extent. Shoulder type, elastic modulus of the base layer and number of samples per sublot,

impact the slab cracking values to a moderate extent.

In order to identify the factors that increase spalling percentage, a similar analysis is

performed by examining the extent of distresses produced from changes in the input

characteristics. The factors that prominently influence the spalling percentages are joint

sealant type, water cement ratio, number of freeze thaw cycles, number of samples obtained,

concrete strength, slab thickness and air content. A moderate effect was produced by

variations in the strength sampling methods and number of simulation trials. The impact of

other factors on spalling percentage is found to be insignificant.

For studying pavement smoothness, the criterion considered was IRI. Factors such as

pavement type, dowel bar diameter, average annual freezing index, number of freeze thaw

cycles, number of days above 90°F, concrete strength, slab thickness and air content were

found to be significant in influencing the IRI values. Moderately influential were the factors

shoulder type, SLTE, transverse joint spacing, joint sealant type, k value, and water cement

ratio, base erodibility factor, ADT, traffic growth rate used, directional factor, percentage

trucks, average annual precipitation, climatic zone, and number of samples per sublot.

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In conclusion, it can be inferred from the above results that slab thickness, concrete

strength, air content, pavement type and dowel bar diameter, and climatic factors such as

freeze thaw cycles and days above 90°F are the most important factors controlling pavement

distress occurrence. Additionally, k value of the subgrade, traffic factors such as ADT,

percentage trucks, directional factor, and number of samples seemed to influence the distress

criteria considerably.

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5 APPLICATION TO THE OHIO ROUTE 50 PROJECT

5.1 The Ohio Route 50 Project

The Ohio Route 50 Joint Sealant Project was conducted between 1997 and 2001 by a

team of researchers from the University of Cincinnati (Ioannides, et al., 2002). This project

involved construction of an experimental stretch of pavement in which different kinds of joint

sealants and sealant configurations were used and performance monitored. Unsealed control

sections were included for comparison purposes. The objectives of this project were to assess

the effectiveness of a variety of joint sealing practices and to examine their repercussions in

terms of construction time and life cycle costs. Additionally, the project assessed the effect of

joint sealing on pavement performance for the purpose of identifying the materials and

procedures that are cost effective. The experimental design for this project was developed by the

Federal Highway Administration (FHWA) and the Ohio Department of Transportation (ODOT).

5.1.1 Project Details

The test site is a 3.3 km (2.0 mi) section of a 10.5 km (6.5 mi) four lane divided highway

along a stretch of U.S. Route 50 in Athens County, Ohio. The highway has a twenty-year design

period with average daily traffic (ADT) of 7820 and 10950 vehicles in the years 1993 and 2013,

respectively. The two eastbound lanes were built in the first phase of construction, during 1997-

1998, and the westbound lanes during the second phase of construction in 1998-1999. The

69

pavement section consisted of a 250 mm (10 in.) plain, jointed, wire reinforced Portland cement

concrete (PCC) slab, placed over a 100 mm (4 in) crushed aggregate, free draining base layer.

The subbase layer is 150 mm (6 in) of crushed aggregate, resting over a predominantly silty clay

local subgrade. The highway consists of two 3.7 m (12 ft) wide lanes in each direction,

incorporating tied PCC shoulders of variable width. Transverse joints sealed with various kinds

of sealants were provided at 6.4 m (21 ft) spacing. Epoxy coated steel dowels of 1.5 in diameter

were installed. The site is located in the wet-freeze climatic zone, with mean annual

precipitation of 40 in., mean monthly average temperature of 53°F, high average monthly

temperature of 75°F and a low average monthly temperature of 32°F.

5.1.2 Experimental Plan

Ten different joint sealants were used in the test sections, in addition to the control

sections that were left unsealed. Of the ten sealed sections, four received silicone sealants, two

single component hot-applied materials, and three preformed compression seals. In this

experiment, joints were cut in six geometric configurations that were numbered 1 to 6.

Configurations 1, 3 and 5 received a secondary cut and a backer rod was placed in

Configurations 1, 3 and 4. Configurations 2 and 6 were used in unsealed test sections, and

Configurations 1, 3 and 4 were used for liquid sealants. Two of the four silicone sealants,

designated as Crafco 903-SL and Crafco 902, were manufactured by Crafco Inc. The other two,

designated as Dow 888 and Dow 890-SL, were manufactured by Dow Corning Corporation. The

two hot applied sealants were named Crafco Roadsaver 221 and Crafco Superseal 444/777. The

compression seals used were Delastic V-687, Techstar W-050, and Watson Bowman WB-687.

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All sealants were placed in accordance to the manufacturers’ specific installation procedures.

The construction procedures were described in detail by Ioannides, et al., (1999). A performance

evaluation plan was formulated by the research team at the University of Cincinnati (Ioannides,

et al., 2000).

5.1.3 Performance Evaluation

The research team of the University of Cincinnati, conducted various investigations

during the project from 1997-2001, monitoring the joint sealant effectiveness and pavement

performance of the Ohio Route 50 pavement test sections. The pavement was divided into thirty

different test sections, typically 180 m (600 ft) in length, consisting of about twenty-nine

transverse joints per section.

The performance evaluation during the initial construction and sealant installation phases

involved two profilometer analyses and two visual inspections of the test sections. The ODOT

profilometer equipment produced results in terms of Mays number (MAYS), present

serviceability index (PSI), and average international roughness index (IRI). The first and second

profilometer runs on the newly constructed eastbound lanes were performed in June, 1998 and

May, 1999 respectively. Also, an initial profilometer run on the newly constructed westbound

lanes was performed in May, 1999. The first and second visual inspections for the eastbound

lanes were conducted in October, 1998 and May, 1999 respectively, and in May, 1999 for the

westbound lanes. Because of construction constraints imposed on the team during these early

inspections, the pavement and the joint sealant condition were examined from the shoulder,

71

which did not permit a quantitative evaluation. The initial analysis was mostly descriptive.

Visual estimates of sealant damage were recorded for three transverse joints in each test section.

Beginning from Fall, 1999, the team developed a methodology to acquire performance

data in a more consistent and organized fashion. A joint sealant evaluation form was developed

that included the joint sealant and configuration combination (or treatment) type, the number and

location of sampled joints, the beginning and ending stations, as well as measured distress and

failure lengths. Six transverse joints were selected randomly from each test section for continual

monitoring. Sealant failures and joint distresses were observed and recorded over 6ft of the joint

length. These included joint distress spalling, sealant failures such as adhesion loss, cohesion

loss, stone intrusion, surface extrusion, gaps and sunken seals. The length and position of each

distress was recorded. The lengths of each observed failure is summed to give the total distress

of that particular joint seal. Percentage overall effectiveness is determined by dividing this sum

by the total joint length. From these individual percentages, average effectiveness for each

section is determined. In addition to sealant defects, distresses pertaining to the PCC pavement

system as a whole such as corner breaks, and longitudinal or transverse cracking in the pavement

slab were recorded.

Subsequent evaluations were conducted for the eastbound and westbound lanes during

the Spring and Fall seasons of 2000 and 2001, which included joint sealant, profilometer and

pavement performance surveys. Examinations conducted during this period included inspections

of the under drain outlets, which may significantly affect the overall pavement performance.

Comprehensive analyses and comparisons of the observed joint sealant and pavement

system distresses were presented by Ioannides, et al. (1999) and Ioannides, et al (2000).

Included in these analyses, were sealant performance rankings, comparisons of sealant

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effectiveness to the profile survey data, and summaries of pavement distress in each section.

Furthermore, a mechanistic-empirical analysis was conducted to observe the effects of design

features on pavement performance. Features employed at the Ohio Route 50 project site and the

distresses observed were interpreted using mechanistic-empirical analysis.

5.1.4 Conclusions from the Ohio Route 50 Project

Observations of the sealant condition, pavement condition, smoothness and distresses

resulted in important conclusions and recommendations, especially in the areas of sealant

performance, its dependence on the type of material, placement technique and specifications of

the joint and the effect of sealing on overall pavement distresses.

The report for the initial phase of the project that involved construction, application of

sealant material to joints and performance inspections documented considerable deterioration of

silicone and hot pour sealants in the eastbound lanes, and recommended that the joint cleaning

and sealant placement methodologies needed to be revised. It was noted in the report that

manufacturer’s recommendations of sand blasting the joint were never adopted. It was also

recorded that the construction procedures employed for the pavement system were tightly

controlled, following pertinent specifications. By this time, a majority of the hot pour and

silicone sealants had experienced significant failure.

During subsequent performance evaluations, it was observed that compression seals, with

the exception of Techstar, outperformed the liquid sealants. The condition of sealants in the

westbound lanes was better than that of the sealants in the eastbound lanes due to the younger

age. Hot pour sealants were found to show the worst performance of the three types of sealants.

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On the basis of visible distresses, unsealed sections were still performing better than most of the

sealed sections during this phase. At that point of investigation, transverse slab cracking

occurred in ten of the fifteen test sections and corner breaks in seven sections in the westbound

lanes. It was found that no correlation existed between sealant effectiveness and pavement

distresses such as transverse cracking, corner breaks, and surface roughness. It was noted from

the trends that pavement surface profile was dependent on the climatic factors that initiate

curling and warping of the slabs. Similar trends in spalling were not observed for the eastbound

and westbound lanes.

A significant feature of this project was the mechanistic analysis conducted to examine a

number of pavement features associated with sound pavement design. The primary focus was on

the effects of subgrade support, load transfer, transverse joint spacing and tied PCC shoulders.

The influence of such features on the Ohio Route 50 pavement site was determined by a series of

mechanistic computations using pavement engineering software. Weakening of the soil

subgrade support led to increased bending stresses and deflections in the slab. In a separate

investigation, effects of strength losses in the base and subbase layers on the bending stresses

were found to be insignificant. Load transfer elements such as shoulder ties and dowels helped

reduce the bending stress and deflections, thus increasing the pavement life and capacity. The

factor with the most pronounced effect on pavement performance was found to be transverse

joint spacing. Based on mechanistic considerations, it was proved that the appropriate spacing

for the Ohio Route 50 pavement system was 4.6m (15 ft), significantly shorter than the actual

joint spacing of 6.1m (21 ft). “This unfortunate discrepancy may lead to premature pavement

distress in the form of transverse cracking throughout the concrete slab,” stated Ioannides, et al

(2000).

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The Ohio Route 50 joint sealant experiment is important inasmuch as it addresses the

significance of site specific factors, and the substantial role they play influencing the sealant and

pavement system performance. Sealant placement, configuration, joint cleaning procedures, and

climate conditions seemed to affect the sealant performance. A correlation between pavement

performance and sealant effectiveness was not established during this research.

5.2 Distress Prediction for the Ohio Route 50 Project

In the previous chapter, a sensitivity analysis was performed for Pavespec 3.0. From the

results obtained, it can be safely concluded that performance of the program conforms to

expectations. In this section, initial data from the Ohio Route 50 Project are input into the

program to simulate the distresses observed, and conduct a comparative analysis to investigate

the applicability of performance prediction.

Using the existing data, i.e., field reports and observations related to the Ohio Route 50

Project, values for all the necessary inputs for each module of Pavespec 3.0 were recognized.

These are presented in Table 5.1. Simulation runs using the software were performed to obtain

the distresses in each one of the sections which used a particular joint sealant material. This

provided the pavement system behavior over its expected life span in terms of spalling,

transverse slab cracking, joint faulting, and pavement smoothness (IRI) values. By giving

approximate unit costs of sealants, pavement repair, etc., to the program, the life cycle costs,

repair and rehabilitation costs for the pavement system over the specified period can be

calculated.

75

5.2.1 Analysis Using the Ohio Route 50 Project Data

A comparative analysis of the predicted and observed distresses was conducted for each

section of the pavement system that used a particular type of sealant material configuration.

Before the analysis results are discussed, the reader must become familiar with the basic

definitions, assumptions and study methods that were used.

Predicted Distresses

The simulation of pavement performance to calculate predicted distresses was

accomplished in the following steps. First, inputs for each module were extracted from the Ohio

Route 50 Project data, assumptions, and values used for mechanistic analysis. Second, a time

period of twenty years, the expected service life of this pavement system, was assigned for the

analysis. In all, fifteen sublots were simulated for each section, with the only input characteristic

varied for each section being the type of sealant used. The Ohio Route 50 joint sealants

experiment consisted of fifteen test sections in the eastbound and westbound lanes with each test

section using a specific sealant material configuration.

During the simulation, no maintenance and rehabilitation (M&R) options were input to

the software so as to accurately observe the distress trends. Annual distress predictions obtained

for the fifteen sections over the entire service life of the pavement system were averaged.

Observed Distresses

Observations from the Ohio Route 50 project provide data related to transverse slab

cracking, spalling, corner breaks, and IRI for each section of eastbound and westbound lanes.

These values were obtained from the periodic inspections conducted by the research team.

76

Calibrated Distresses

The Ohio Route 50 data was collected over a period of four years for a pavement system

with a service life of twenty years. In order to compare distresses for each section, using field

observations and predictions from Pavespec 3.0, data calibration becomes necessary. The

program has a built-in feature to use calibration coefficients that can reflect the future trend of

pavement distresses based on the obtained short term data. Using the observed distress and

predicted distress regression trends, calibration coefficients were obtained and calibrated

distresses were calculated for the entire service life of the pavement system. The following

section explains the assumptions and analysis methodology with detailed examples.

Analysis Methodology

For each section of the Ohio Route 50 pavement system with a different sealant material

configuration, observed, predicted and calibrated distresses were plotted on a time-distress

measure chart. The predicted and calibrated values were obtained from the averaged results of

Pavespec 3.0. The following are some important aspects of the prediction methodology.

(i) Curves for observed, predicted and calibrated data are obtained utilizing linear regression

capabilities of Microsoft Excel. The progression of distresses over time is assumed to be

linear in the form:

cmxy += (4.1)

where, y represents distress measure and x represents elapsed time.

Pavespec 3.0 allows the user to calibrate the predictions by defining the regression

coefficients A and B as follows:

BDistressredictedPADistressObserved += )( (4.2)

A and B can be most easily obtained by assuming that the distress curves are linear.

77

(ii) Boundary conditions for distresses are not imposed on any of the regression curves. This

resulted in distress curves with positive and negative intercepts, which can be explained

as follows. A positive intercept, in the case of transverse slab cracking or spalling curves

mean that distresses appeared immediately after the road section was opened to traffic. A

negative intercept means that for a period of time the pavement section distress values

were zero.

(iii) As predicted, observed and calibrated curves were obtained from linear regression

analysis, the coefficient of correlation (R2) becomes significant. In order to be

considered for analysis, the curves obtained should have R2 values greater than 0.5. In

several instances, the R2 values of observed trends are less than 0.5 and data filtering

becomes necessary to eliminate inconsistencies. For such cases, the best-fit linear

regression equation obtained is used to calculate the percentage deviation of each distress

from the distress curve values. This is computed using the following relation:

EquationfromDistressCalculatedDistressActualEquationfromDistressCalculatedDeviationPercentage /100)( ×−=

(4.3)

If, ,10≥DeviationPercentage the data point is eliminated from the next iteration

(iv) The data filtering was performed to a maximum number of two iterations to obtain

curves with R2 values greater than 0.5. In a few instances, if the iterations do not result in

this, further filtration was not performed and the curves were presented as existed.

Additionally, there were some cases in which

78

,10<DeviationPercentageMaximum and the data point with the highest

DeviationPercentage , was eliminated to produce the next iteration of the trend. A

maximum of two iterations were performed for data filtering for a distress measure.

(v) The transverse slab cracking model in Pavespec 3.0 does not provide accurate results

when calibration coefficients were assigned. As the results produced were inaccurate, it

was decided to eliminate the process of calibration for transverse slab cracking

altogether. The results presented only include the predicted and observed slab cracking

data and trends.

Analysis Example

The above concepts can be better illustrated with an example. The IRI comparisons of

Eastbound section Delastic V687 are examined below. If the observed IRI data is used, the

regression equation obtained is

758.631046.0 += xy , R2 = 0.1338 (4.4)

where, )(, MonthsTimeElapsedxIRIObservedy == .

This equation is used as the base to calculate the IRI values at the specific Elapsed Time

points, where the observations were made by the research team. The following formula is

used to compute the percentage deviation of actual data from the regression trend:

EquationfromDistressCalculatedDistressActualEquationfromDistressCalculatedDeviationPercentage /100)( ×−=

If, ,10≥DeviationPercentage the data point is eliminated from the next regression. The

details of this iteration are presented in Table 5.5. In the current example, this analysis

results in an IRI trend equation:

79

892.611323.0 += xy , R2 = 0.7133. (4.5)

As the R2 value is greater than 0.5, this equation can be used for further analysis.

For the same example, Delastic V-687, the calibration methodology may be explained as

follows: The linear regression equation obtained for the predicted distress trend is

747.610153.0 += xy (4.6)

and the observed trend is

892.611323.0 += xy (4.7)

Using the equation available from Pavespec 3.0,

BDistressedictedADistressObserved += )Pr(

The predicted and observed equations may be equated as:

)892.611323.0( +x =A ( 747.610153.0 +x ) + B (4.8)

Equating the coefficients of x and constants, one obtains A and B as 8.65 and -472.21

respectively. These values are then used to obtain the calibrated distress values from

Pavespec 3.0.

5.2.2 Sections with Compression Sealants – Eastbound

Compression sealants Techstar W-050, Delastic V-687, and Watson Bowman WB-687

were used in the eastbound lanes of the Ohio Route 50 project. According to the results from the

Ohio Route 50 experiment, the sealants Delastic V-687 and Watson Bowman 687 are performing

very well with an average effectiveness value of 94%, whereas the Techstar W-050 sealant was

19% at the last evaluation in Fall, 2001.

80

Pavespec 3.0 simulated the pavement and sealant related distresses of the sections. A

compression sealing option was used and the initial IRI values that were observed for each

section in the field were provided to the program. Distress curves were computed for a period of

20 years from the program. Calibrated curves were generated from the results of Pavespec 3.0

and using the methodology explained in the previous section. The plots are shown in Fig. 5.1,

5.2 and 5.3. It is important to note that the curves obtained using linear regression for the

observed data are not accurate. This is due to various reasons, some of them being data

inconsistency, insufficient field data to incorporate statistically sound regression curves, and the

assumption that the curves are linear. The predicted curves were observed to be conforming well

to linear regression methods, by producing high R2 values. The predicted, observed and

calibrated curves are compared for conclusions about pavement performance and sealant

effectiveness.

Pavement and Sealant Distresses

The distresses compared included pavement roughness in terms of IRI, spalling

percentage and transverse slab cracking percentage. The spalling percentage is the ratio of

spalled length to the total joint length; transverse slab cracking percentage is quantified using the

ratio of cracked slabs to the total number of slabs.

For sections with compression sealants, Pavespec 3.0 predicted negligible spalling during

the twenty year service life. Significant spalling was actually observed in the sections with

Delastic and Watson Bowman seals, and this is reflected in the calibrated trends. Techstar in the

eastbound lanes did not show any spalling, even though its average effectiveness was rated very

low. The predicted variation of IRI values with time exhibited a lower slope than the

corresponding calibrated trend. The slopes of calibrated curves were two to four times that of

81

predicted, indicating that deterioration was more rapid in the field. Data and related trend lines

indicate that the rate of transverse slab cracking in the field is much higher than predicted,

possibly due to the higher Slab Length/Radius of Relative Stiffness (L/l) ratio. The slopes of

observed transverse cracking curves in all three sections were similar.

5.2.3 Sections with Silicone Sealants – Eastbound

Silicone sealants that were used in the eastbound lanes of the Ohio Route 50 test section

are Crafco 903-SL, Crafco 902, Dow 890-SL and Dow 888. The results from Ohio Route 50

tests show that these sealants are performing well, and the slabs show little or no spalling.

Pavespec 3.0 simulated the values of IRI, spalling, transverse slab cracking and joint

faulting. The curves are shown in Figures 5.4, 5.5 and 5.6. Pavement performance related

distresses, IRI and transverse slab cracking observed in the field are several times higher than

predicted. Calibrated curves for IRI values were similar to the curves in the compression sealant

sections. Slopes of observed transverse slab cracking curves are similar to the compression

sealant sections.

Pavement sealant related distress spalling was also compared for the silicone sealant

sections. For spalling, an option of silicone sealants was used in the simulation program. The

curves show that sections with silicone sealants are performing comparably, and in a few cases,

better than the predicted.

82

5.2.4 Sections with Hot Pour Sealants – Eastbound

Hot Pour sealants that were applied to the eastbound sections of the Ohio Route 50

project are Crafco 221 and Crafco 444. Field data from the Ohio Route 50 pavement system

showed that the sealants and pavement distresses are similar to those observed in the other

sections. The sealant performance has been observed to be unsatisfactory for Crafco 444.

Crafco 221 in contrast has performed well and limited distresses were observed. Curves for the

hot pour sealant section distresses are shown in Figures 5.7, 5.8 and 5.9.

For the hot pour sealed sections in the eastbound lanes, the IRI values observed were

higher than the predictions from Pavespec 3.0, as was the case in all the other sections. Spalling

failure observed in the sections was higher than that predicted, indicating the role of faster

deteriorating sealant material. Transverse cracking occurred at a very similar rate as in the other

sections.

5.2.5 Sections with No Sealants – Eastbound

Two configurations of no sealants were applied in the eastbound lanes of the project.

Field data showed that the sections with no sealants were performing well and in some cases

better than the sealed sections in terms of joint related distress (spalling).

Figures 5.10, 5.11 and 5.12 show plots that illustrate the distress trends. The pavement

related distress measures such as IRI and transverse slab cracking were found to be in the same

range as those of other sealant types, viz., compression, silicone and hot pour. The IRI trend line

for the calibrated curves is about ten times steeper than that of predicted reiterating that the

deterioration of pavements is rapid in the field. The calibrated spalling trend was close to

83

predicted for the two configurations indicating a more accurate prediction from Pavespec 3.0 in

these cases.

5.2.6 Sections with Compression Sealants – Westbound

In the westbound lanes, Delastic V-687 and Techstar W-050, Watson Bowman WB-812

sealant configurations were used. Comparative trends of the distresses are shown in Figures

5.13, 5.14 and 5.15. Results from the field data indicate that compression sealants performed

very well, as did their eastbound counterparts. With the exception of Techstar W-050, which

performed poorly in both eastbound and westbound lanes, compression sealants were ranked as

the best of all sealants used in the Ohio Route 50 project.

As the input parameters are the same for westbound and eastbound lanes for the

performance prediction, distresses predicted were similar. Conclusions from the Ohio Route 50

project indicate that westbound lanes performed better in all aspects when compared to their

eastbound counterparts, mainly due to their younger age and improved construction and sealant

installation techniques.

IRI predictions for the westbound sections and comparisons to the calibrated curves

indicate a similar increased deterioration as was noted in the eastbound lanes. Transverse slab

cracking curves for the compression sealants were observed to be much greater in slope than the

eastbound counterparts. For the sealant related distresses, spalling predictions were almost

negligible when the compression sealants option was specified in Pavespec 3.0. The spalling

trend lines for the observed data also indicated a negligible slope signifying a good performance

of the sealants by far.

84

5.2.7 Sections with Silicone Sealants – Westbound

In westbound lanes, silicone sealants Crafco 903-SL, Dow 888, and Dow 890-SL were

used. These are performing much better than their eastbound counterparts. Average

effectiveness of sealants in the eastbound lanes was reported as 46%, whereas in the westbound

lanes it was 85%. Performance curves of predicted, observed and calibrated data are shown in

Figures 5.16, 5.17, 5.18.

Pavement surface roughness showed a downward trend for Crafco 903 SL configurations,

indicating that the pavement sections became smoother with age. Climatic factors such as

curling and warping were considered to be responsible for such behavior in the field. This was

not reflected in the results from the distress prediction model. Pavespec 3.0 predictions followed

similar trends for all the sealant configurations, and each of them showed an increase in IRI with

age. Transverse slab cracking slopes observed over time were much higher than predicted, as

was the case with all other sections of the Ohio Route 50 pavement system.

5.2.8 Sections with Hot Pour Sealants – Westbound

The sealant material configurations Crafco 221 and Crafco 444 that were applied in

eastbound lanes were used in the westbound lanes also. Crafco 444 performed much better than

Crafco 221 in the westbound lanes, maintaining an effectiveness of about 90%. This was in

contrast to the performance in the eastbound direction, where Crafco 444 was ineffective. The

distress trend comparison for these sections is shown in Figures 5.19, 5.20, 5.21.

The calibrated IRI curves for both sealant configurations were close to the predicted.

Transverse slab cracking observed in hot pour sealant sections was found to be much higher than

85

in their eastbound counterparts. In general, all sections in the westbound lanes had transverse

slab cracking levels higher than the corresponding eastbound sections. Spalling observed in

these sections was minimal when compared to the predictions from Pavespec 3.0.

5.2.9 Sections with No Sealant – Westbound

Two configurations of unsealed sections were applied in the westbound lanes. These

were control sections that were not sealed to monitor the differences between sealed and

unsealed joints. They performed well in both the east and westbound lanes. Predicted and

observed trend comparisons for the westbound sections are shown in Figures 5.22, 5.23, and

5.24.

The slope of the pavement smoothness indicator IRI for the no sealant sections was many

times higher than the predicted one, reaffirming the trend that was observed in almost all the

sections of eastbound and westbound lanes. Transverse slab cracking for the no sealant sections

was higher than the predicted values from Pavespec 3.0. Spalling percentages in the unsealed

sections of westbound lanes did not show a consistent trend.

5.2.10 Summary of Observations

From the results of the comparative analysis of predicted and calibrated distress trends,

the following observations can be reported:

1. Comparisons for the eastbound sections were statistically more significant than their

westbound counterparts. In general, the R2 values for observed trend lines were highest for the

eastbound IRI. The better results from linear regression analysis can be attributed to the higher

86

number of data points collected over a longer period of time. A total of 7 data points were

available, collected over a period of 40 months. Additionally, the eastbound lanes presented

distress trends with better R2 values, probably due to the same reason. Calibrating the

predictions using the observed values was more effective in the eastbound lanes than in the

westbound lanes due to better coefficients of correlation.

2. In a majority of cases, the calibrated IRI and spalling and transverse slab cracking values

were many times higher than the predicted. This could imply that (a) the pavement is

deteriorating faster, provided, one assumes that the predictions are accurate and reflect the way

such a pavement system should behave in the field, or (b) the prediction model provides

conservative estimates of distresses. It should be noted that the capabilities and accuracy of this

performance prediction program cannot be evaluated by one case study alone.

3. The observed transverse slab cracking curves presented similar slopes for all the sections

in the eastbound lanes. The westbound lanes exhibited this phenomenon, but the slopes of

curves were slightly higher.

5.3 Life Cycle Cost Analysis to Determine the Cost Effectiveness of Sealants

Research showed that life cycle costing is the most efficient way to examine the cost

effectiveness and benefits obtained from applying various features to the pavement system.

Sealant type, effectiveness levels, cost information and time intervals for replacement and repair

can all be incorporated in Pavespec 3.0 using the various input modules. Pavespec 3.0

simulations determine the sealant related distress of joint spalling and the corresponding

87

maintenance costs. By introducing in Pavesec 3.0 the above mentioned aspects of sealants used

in the Ohio Route 50 project, the approximate costs incurred in using a specific type of sealant

configuration were calculated. As the program provides rehabilitation options, an appropriate

plan was chosen for all sections. A final comparison of costs and frequency of repair necessary

will yield the most effective sealant configuration for the pavement system. These numbers are

just estimates, and the costs obtained may not be close to the real values. The significance of the

exercise lies in the fact that performance predictive algorithms are used and various available

rehabilitation options can be tested. Sealant replacement intervals have been chosen to be 3

years, or 6 years based on the observed sealant material performance. This will give us the

changes in the life cycle costs for each alternative methodology employed, provided the general

M&R plan remains unchanged.

Application of life cycle cost analysis (LCCA) to evaluate the cost effectiveness of

concrete pavement design features was discussed in detail by Cole (1998), using some available

models and the most common design features for concrete pavements. On similar lines, in this

study, joint sealing is considered as a pavement design feature, to compare the performance and

costs incurred. Ten different sealant materials were used in the Ohio Route 50 project. The

sealant material unit cost and cost per joint were reported by Ioannides, et al (1999). Using the

data from the Ohio Route 50 project, information from the literature pertinent to the Ohio

Department of Transportation (ODOT) pavement management system (PMS), and the available

options in Pavespec 3.0 for M&R, a rehabilitation plan for the Ohio Route 50 pavement to serve

as an input to Pavespec 3.0 is proposed.

88

5.3.1 Proposed M&R Steps and Costs

The steps involved in the proposed M&R plan are based on the available options in the

program and have been formulated to take into consideration, as much as possible, the

procedures followed by ODOT for M&R of PCC pavements. Rehabilitation steps are assumed

and applied in Pavespec 3.0 simulations for the Ohio Route 50 pavement section using the

methods and recommendations for life cycle cost analysis presented in ‘Pavement Preventive

Maintenance Program Guidelines’ (ODOT, 1999).

- 100% sealing of transverse joint sealants every 3 or 6 years (depending on sealant

material performance)

- 50% sealing of longitudinal joints every 5 years.

- 100% sealing of transverse cracks every 5 years.

- For the local rehabilitation plan, it is proposed that if the lot average percentage of

cracked slabs exceeds 50 %, then partial slab replacements be applied to 50 % of the

cracked slabs.

- If lot average percentage spalled joints exceeds 10 %, partial depth repairs are proposed

to 50% of spalled joints.

- If lot average IRI exceeds 99 in. /mi, then the global rehabilitation procedure is initiated.

- If the average transverse joint faulting exceeds 0.5 in., global rehabilitation procedure is

initiated.

- Global rehabilitation procedure prescribed as per the pavement type and ODOT

specifications is that of a PCC overlay that has an assumed service life of ten years, per

ODOT Rehabilitation Specifications (ODOT, 1999).

89

The proposed M&R plan is input in Pavespec 3.0’s maintenance module. The associated

cost of each step is provided. It has to be noted that only sealant material unit cost quotes are

accurate. The remaining quotes are obtained from M&R manual (ODOT, 1999), or the default

values available in Pavespec 3.0. The unit costs used as inputs for the proposed M&R plan are

provided in Table 5.1. It also shows the annual inflation rate and annual interest rate percentages

necessary to calculate the present worth costs, and the user cost percentage that needs to be

included for the LCCA.

5.3.2 Cost Effectiveness of Sealant Material Using LCCA – Applied Methodology

When examining the benefits of a design feature, increased life cycle costs should result

in increased overall pavement performance. The method suggested by Cole, (1998) is applied

herein to evaluate the cost effectiveness of the various joint sealant materials that were used in

the Ohio Route 50 project.

Based on the performance of each type of sealant material during the period of

observation of the project, a replacement time interval of 3 or 6 years is specified. The

recommendations formulated in the Ohio Route 50 project report (Ioannides, et al., 2002)

indicate that sealants whose performance is rated below 50% should be replaced.

The M&R plan presented in the previous section is applied for all the sealant materials

used in the eastbound and westbound lanes. Using the inputs specified in Table 5.1, Pavespec

3.0 calculates a life cycle cost summary for the lot, which includes a total rehabilitation life cycle

cost and the total rehabilitation present-worth life cycle cost. Comparing these values for all the

sealant materials that were used in the Ohio Route 50 project, the most effective joint sealing

90

material is selected. The costs resulting from such analyses can provide an approximate idea

only for comparison purposes.

5.3.3 Results from the Analysis

Results from the cost analyses of Pavespec 3.0 for all the sealant materials used in the

westbound and eastbound lanes of the Ohio Route 50 project are presented in Table 5.3 and

Table 5.4. These Tables show information related to the sealant material, frequency and

percentage of replacement that are calculated, as well as the total rehabilitation life cycle cost

and the total present-worth life cycle cost.

It can be inferred from the rehabilitation life cycle costs that the compression sealants,

with the exception of Techstar W-050, were the most effective in both eastbound and westbound

lanes. Techstar, with its high costs and frequent replacement needs, cannot be recommended for

further use from a cost and performance point of view. Silicone sealant Crafco 902 that was

used only in the eastbound lanes follows Techstar W-050 in terms of high costs and poor

performance. All the other sealant materials result in approximately equal total rehabilitation life

cycle costs. All the hot pour sealant materials used resulted in similar costs, and needed frequent

replacements. The hot pour sealants are found to be inconsistent in performance and cost

effectiveness, as Crafco 221 and Crafco 444 are cost effective in the eastbound and westbound

lanes, respectively.

91

Table 5.1 Inputs for Pavespec 3.0 used to simulate Ohio Route 50 pavement performance

INPUT VARIABLE VALUE FROM OHIO ROUTE 50 DATA

Lane Configuration Four Divided

Lane Width 12 ft

Shoulder Type Tied PCC

Stress Load Transfer Efficiency (39.1%-60.9%) – 25.5% typical value

Project Length 2.0 miles, 10560 ft.

Design Life 20 years

Pavement Type Jointed, Doweled

Dowel Bar Diameter 1.5 in.

Transverse Joint Spacing 21 ft.

PCC Modulus of Elasticity 27.6 GPa or 4000,000 psi

Transverse Joint Sealant Type None, Silicone, Hot Pour, Compression

Modulus of Subgrade Reaction (k) 150 pci

Water Cement Ratio 0.438

Base Layer Permeability Permeable Base

Base Thickness 4 in. (6 in. subbase)

Base Modulus of Elasticity 345 MPa or 50,000 psi

Base Erodibility Factor 0 or 1

ADT for the First Year ADT Design Year- 7820, Year of Usage- 8244

Directional Distribution Factor 50%

Truck Percentage in the Outer Lane 90%

92

Table 5.1 (contd.) Inputs for Pavespec 3.0 used to simulate Ohio Route 50 pavement performance

INPUT VARIABLE VALUE FROM OHIO ROUTE 50 DATA

Growth Rate 4%, Simple

Average Annual Freezing Index (0-100) 100 Considered

Average Annual Precipitation 40 in.

Average Annual Number of Days Over 90 F 30

Climate Zone Wet Freeze Zone

Average Modulus of Rupture of Beams Cast Mean 789 psi, SD 87 psi

Slab Thickness 10 in.

Air Content 6 +2 % as per ODOT Specifications

93

Table 5.2 Sealant unit costs, M&R costs and other inputs

SEALANT MATERIAL COST

Dow 890-SL $12.27 per joint

Crafco 903-SL $9.20 per joint

Dow 888 $10.74 per joint

Crafco 902 9.97 per joint

Crafco 444 2.68 per joint

Crafco 221 0.64 per joint

Watson Bowman WB-812 $43.26 per joint

Watson Bowman WB-687 $30.24 per joint

Delastic V- 687 27.72 per joint

Techstar W-050 3636.30 per joint

M&R COST

Longitudinal Joint Sealing $1.00 per foot

Transverse Joint Sealing $1.00 per foot

Partial Slab Replacements $80.00 per sq. yard

Partial Depth Repairs of Transverse Joints $ 50.00 per joint foot

PCC Overlay $ 15.00 per sq. yard

Annual Inflation Rate Used 3%

Annual Interest Rate Used 6%

User Cost Percentage Included 5%

94

95

96

Table 5.5 Data Filtering (IRI) for Eastbound Section with Delastic V 687

MONTHS IRI(in./mi) IRI FROM TREND

LINE(in./mi)

PERCENTAGE DIFFERENCE

FINAL IRI (in./mi)

0 60.37 63.758 5.31 60.37

4

11 64.48 64.9086 0.66 64.48

17

18 73.21 65.6408 -11.53

21 65.55 65.9546 0.61 65.55

28 66.59 66.6868 0.14 66.59

31 65.92 67.0006 1.61 65.92

40 65.77 67.942 3.19 65.77

97

IRI Comparisons,Techstar W 050 EB

Observedy = 0.4033x + 58.644

R2 = 0.4719

Predictedy = 0.0211x + 56.034

R2 = 0.9069

Calibratedy = 0.4136x + 60.207

R2 = 0.9044

0

10

20

30

40

50

60

70

80

90

100

110

120

130

140

150

160

170

180

190

200

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

IRI (

D) (

in/m

i)

Observed

Predicted

Calibrated

IRI Observed (Linear)

IRI Predicted (Linear)

IRI Calibrated (Linear)

IRI Comparisons, Delastic V 687 EB

Calibratedy = 0.1326x + 61.861

R2 = 0.913

Observedy = 0.1323x + 61.892

R2 = 0.7133

Predictedy = 0.0153x + 61.747

R2 = 0.9131

0

10

20

30

40

50

60

70

80

90

100

110

120

130

140

150

160

170

180

190

200

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

IRI (

in/m

i)

Observed

Predicted

Calibrated

IRI Calibrated(Linear)

IRI Predicted(Linear)

Fig. 5.1 Compression sealants Eastbound IRI comparison plots

98

IRI Comparisons, Watson Bowman EB

Predictedy = 0.0153x + 61.62

R2 = 0.9167

Observedy = 0.252x + 63.278

R2 = 0.6437

Calibratedy = 0.253x + 63.254

R2 = 0.9167

0

10

20

30

40

50

60

70

80

90

100

110

120

130

140

150

160

170

180

190

200

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

IRI (

D) (

in/m

i)

Predicted

Observed

Calibrated

IRI Predicted (Linear)

IRI Observed (Linear)

IRI Calibrated (Linear)

Fig. 5.1 (Contd.) Compression sealants Eastbound IRI comparison plots

99

Transverse Slab Cracking Comparisons, Techstar W 050 EB

Observedy = 1.8333x - 20.033

R2 = 0.4808

Predictedy = 0.0017x - 0.0445

R2 = 0.9689

0

10

20

30

40

50

60

70

80

90

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

Tran

sver

se S

lab

Cra

ckin

g (%

)

Observed

Predicted

Transverse CrackingObserved (Linear)

Transverse CrackingPredicted(Linear)

Transverse Slab Cracking Comparisons, Delastic V 687 EB

Observedy = 2.2487x - 44.441

R2 = 0.6905

Predictedy = 0.0003x - 0.004

R2 = 0.9761

0

10

20

30

40

50

60

70

80

90

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

Tran

sver

se S

labs

Cra

cked

(%)

Observed

Predicted

Transverse CrackingObserved (Linear)

Transverse CrackingPredicted (Linear)

Fig. 5.2 Compression sealants Eastbound transverse slab cracking comparison plots

100

Transverse Slab Cracking Comparisons, Watson Bowman EB

Observedy = 1.3821x - 5.8744

R2 = 0.9423

Predictedy = 0.0005x - 0.0131

R2 = 0.9694

0

10

20

30

40

50

60

70

80

90

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

Tran

sver

se S

lab

Cra

ckin

g (%

)

Observed

Predicted

Trasverse CrackingObserved (Linear)

Transverse CrackingPredicted (Linear)

Fig. 5.2 (Contd.) Compression sealants Eastbound transverse slab cracking comparison plots

101

Spalling Comparisons, Techstar W 050 EB

Observedy = 0

R2 = #N/A

Predictedy = 6E-06x - 0.0003

R2 = 0.6682

0

1

2

3

4

5

6

7

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed time (Months)

Spal

ling

(%)

Observed

Predicted

Spalling Observed(Linear)Spalling Predicted(Linear)

Spalling Comparisons, Delastic V 687 EB

Predictedy = 5E-05x - 0.003

R2 = 0.6682

Observedy = 0.0141x + 0.4206

R2 = 0.6482

Calibratedy = 0.0198x + 0.7505

R2 = 0.8786

0

1

2

3

4

5

6

7

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

Spa

lling

(%)

Observed

Predicted

Calibrated

Spalling Predicted(Linear)Spalling Observed(Linear)Spalling Calibrated(Linear)

Fig. 5.3 Compression sealants Eastbound spalling comparison plots

102

Spalling Comparisons, Watson Bowman EB

Observedy = 0.0096x - 0.1793

R2 = 0.6259

Predictedy = 4E-05x - 0.0012

R2 = 0.9137

Calibratedy = 0.0109x - 0.179

R2 = 0.8988

0

1

2

3

4

5

6

7

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

Spal

ling

(%)

Observed

Predicted

Calibrated

Spalling Observed (Linear)

Spalling Predicted (Linear)

Spalling Calibrated (Linear)

Fig. 5.3(Contd.) Compression sealants Eastbound spalling comparison plots

103

IRI Comparisons, Crafco 903 SL EB

Predictedy = 0.0498x + 61.228

R2 = 0.9746

Observedy = 0.2878x + 59.427

R2 = 0.8188

Calibratedy = 0.2868x + 59.503

R2 = 0.9755

0

10

20

30

40

50

60

70

80

90

100

110

120

130

140

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

IRI (

in/m

i)

Predicted

Observed

Calibrated

IRI Predicted (Linear)

IRI Observed (Linear)

IRI Calibrated (Linear)

IRI Comparisons ,Crafco 902 SL EB

Predictedy = 0.0313x + 65.575

R2 = 0.9763

Observedy = 0.0746x + 68.773

R2 = 0.0721

0

10

20

30

40

50

60

70

80

90

100

110

120

130

140

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

IRI (

in/m

i)

Observed

Predicted

IRI Predicted (Linear)

IRI Observed (Linear)

Fig. 5.4 Silicone sealants Eastbound IRI comparison plots

104

IRI Comparisions, Dow 890 SL EB

Predictedy = 0.0234x + 62.145

R2 = 0.9184

Observedy = 0.3184x + 51.46

R2 = 0.6936

Calibratedy = 0.2988x + 49.972

R2 = 0.9131

0

10

20

30

40

50

60

70

80

90

100

110

120

130

140

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Time Elapsed (Months)

IRI (

in/m

i)

Predicted

Observed

Calibrated

IRI Predicted (Linear)

IRI Observed (Linear)

IRI Calibrated (Linear)

IRI Comparisons, Dow 888 SL EB

Predictedy = 0.0308x + 60.025

R2 = 0.9771

Observedy = 0.0796x + 60.118

R2 = 0.5384

0

10

20

30

40

50

60

70

80

90

100

110

120

130

140

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

IRI

(in/m

i)

Predicted

Observed

IRI Predicted (Linear)

IRI Observed (Linear)

Fig. 5.4(Contd.) Silicone sealants Eastbound IRI comparison plots

105

Transverse Slab Cracking Comparisons, Crafco 903 SL EB

Observedy = 0.4615x - 3.3308

R2 = 0.4808

Predictedy = 0.0004xR2 = 0.9328

0

10

20

30

40

50

60

70

80

90

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

Tran

sver

se S

lab

Cra

ckin

g (%

)

Observed

Predicted

Transverse CrackingObserved (Linear)

Transverse CrackingPredicted (Linear)

Transverse Slab Cracking Comparisons, Crafco 902 SL EB

Observedy = 0.1872x + 34.29

R2 = 0.0168

Predictedy = 0.0005x - 0.0138

R2 = 0.9696

0

10

20

30

40

50

60

70

80

90

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

Tran

sver

se S

lab

Cra

ckin

g (%

)

Observed

Predicted

Tranverse CrackingObserved (Linear)

Transverse CrackingPredicted (Linear)

Fig. 5.5 Silicone sealants Eastbound transverse slab cracking comparison plots

106

Transverse Slab Cracking Comparisions, Dow 890 SL EB

Observedy = 0.4346x + 0.2577

R2 = 0.1196

Predictedy = 0.0002x - 0.0051

R2 = 0.9258

0

10

20

30

40

50

60

70

80

90

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

Tran

sver

se S

lab

Cra

ckin

g (%

)

Observed

Predicted

Transverse CrackingObserved (Linear)

Transverse CrackingPredicted (Linear)

Transverse Slab Cracking Comparisons, Dow 888 SL EB

Observedy = 0.9628x + 25.36

R2 = 0.596

Predictedy = 0.0006x - 0.0169

R2 = 0.9705

0

10

20

30

40

50

60

70

80

90

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

Tran

sver

se S

lab

Cra

ckin

g (%

)

Observed

Predicted

Transverse CrackingObserved (Linear)

Transverse CrackingPredicted (Linear)

Fig. 5.5(Contd.) Silicone sealants Eastbound transverse slab cracking comparison plots

107

Spalling Comparisons, Crafco 903 SL EB

Predictedy = 0.0767x - 1.164

R2 = 0.9371

Observedy = 0.0447x - 0.6646

R2 = 0.6534

Calibratedy = 0.0447x - 0.0674

R2 = 0.9372

0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

Spal

ling

(%)

Observed

Predicted

Calibrated

Spalling Predicted(Linear)

Spalling Observed(Linear)Spalling Calibrated(Linear)

Spalling Comparisons, Crafco 902 SL EB

Observedy = 0

R2 = #N/A

Predictedy = 0.0348x - 0.5844

R2 = 0.9306

0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

Spal

ling

(%)

Observed

Predicted

Spalling Observed(Linear)

Spalling Predicted(Linear)

Fig. 5.6 Silicone sealants Eastbound spalling comparison plots

108

Spalling Comparisons, Dow 890 SL EB

Observedy = 0.0275x - 0.5418

R2 = 0.4949

Predictedy = 0.0096x + 0.3738

R2 = 0.9632

Calibratedy = 0.0274x - 0.5411

R2 = 0.9631

0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

Spal

ling

(%)

Observed

Predicted

Calibrated

Spalling Observed(Linear)

Spalling Predicted(Linear)

Spalling Calibrated(Linear)

Spalling Comparisons, Dow 888 SL EB

Predictedy = 0.0022x + 0.0106

R2 = 0.0198

Observedy = 0.0327x - 0.4548

R2 = 0.9385

0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

Spal

ling

(%)

Observed

Predicted

Spalling Observed(Linear)

Spalling Predicted(Linear)

Fig. 5.6(Contd.) Silicone sealants Eastbound spalling comparison plots

109

IRI Comparisons, Crafco 221 EB

Predictedy = 0.0443x + 71.4

R2 = 0.9379

Observedy = 0.6407x + 71.4

R2 = 0.2923

Calibratedy = 0.6408x + 71.35

R2 = 0.9372

0102030405060708090

100110120130140150160170180190200210220230

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

IRI

(in/m

i)

Observed

Predicted

Calibrated

IRI Predicted (Linear)

IRI Observed (Linear)

IRI Calibrated (Linear)

IRI Comparisons, Crafco 444 EB

Observedy = 0.2033x + 64.257

R2 = 0.1553

Predictedy = 0.0317x + 61.229

R2 = 0.9782

Calibratedy = 0.2032x + 64.262

R2 = 0.9781

0102030405060708090

100110120130140150160170180190200210220230

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

IRI

(in/m

i)

Observed

Predicted

Calibrated

IRI Observed (Linear)

IRI Predicted (Linear)

IRI Calibrated (Linear)

Fig. 5.7 Hot pour sealants Eastbound IRI comparison plots

110

Transverse Cracking Comparisons, Crafco 221 EB

Predictedy = 0.0008xR2 = 0.9246

Observedy = 0.5538x + 17.423

R2 = 0.9231

0

10

20

30

40

50

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

Tran

sver

se S

lab

Cra

ckin

g (%

)

Observed

Predicted

Transverse CrackingPredicted (Linear)

Transverse CrackingObserved (Linear)

Transverse Slab Cracks Comparisons, Crafco 444 EB

Predictedy = 0

R2 = #N/A

Observedy = 0.3821x - 0.1744

R2 = 0.7249

0

10

20

30

40

50

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time(Months)

Tran

sver

se S

lab

Cra

ckin

g (%

)

Observed

Predicted

Transverse CrackingPredicted (Linear)

Transverse CrackingObserved (Linear)

Fig. 5.8 Hot pour sealants Eastbound transverse slab cracking comparison plots

111

Spalling Comparisons, Crafco 221 EB

Predictedy = 0.0465x - 0.858

R2 = 0.9248

Observedy = 0.1233x + 5.3437

R2 = 1

Calibratedy = 0.1232x + 5.3424

R2 = 0.9248

0

5

10

15

20

25

30

35

40

45

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

Spal

ling

(%)

Observed

Predicted

Calibrated

Spalling Predicted(Linear)

Spalling Obseved (Linear)

Spalling Calibrated(Linear)

Spalling Comparison, Crafco 444 EB

Observedy = 0.0923x - 2.0293

R2 = 1

Calibratedy = 0.0922x - 2.0286

R2 = 0.9497

Predictedy = 0.0369x - 0.3482

R2 = 0.9497

0

5

10

15

20

25

30

35

40

45

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

Spal

ling

(%)

ObservedPredicted CalibratedSpalling Observed (Linear)Spalling Calibrated (Linear)Spalling Predicted (Linear)

Fig. 5.9 Hot pour sealants Eastbound spalling comparison plots

112

IRI Comparisons, No Sealant A EB

Predictedy = 0.0424x + 58.681

R2 = 0.9817

Observedy = 0.2905x + 57.79

R2 = 0.8602

Calibratedy = 0.2909x + 57.751

R2 = 0.9818

0

10

20

30

40

50

60

70

80

90

100

110

120

130

140

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

IRI (

in/m

i)

IRI Predicted

IRI Observed

IRI Calibrated

IRI Predicted (Linear)

IRI Observed (Linear)

IRI Calibrated (Linear)

IRI Comparisons, No Sealant B EB

Predictedy = 0.0324x + 48.47

R2 = 0.9769

Observedy = 0.2162x + 54.719

R2 = 0.8623

Calibratedy = 0.2164x + 54.704

R2 = 0.9768

0

10

20

30

40

50

60

70

80

90

100

110

120

130

140

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

IRI

(in/m

i)

PredictedObservedCalibratedIRI Predicted (Linear)IRI Observed (Linear)IRI Calibrated (Linear)

Fig. 5.10 No sealants Eastbound IRI comparison plots

113

Transverse Slab Cracking Comparisons, No Sealant A, EB

Observedy = 1.1923x + 21.154

R2 = 0.4808

Predictedy = 0.0005x - 0.0126

R2 = 0.9703

0

10

20

30

40

50

60

70

80

90

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

Tran

sver

se S

lab

Cra

ckin

g (%

)

Observed

Predicted

Transverse CrackingObserved (Linear)

Transverse CrackingPredicted (Linear)

Transverse Slab Cracking Comparisons, No Sealant B EB

Observedy = 1.6026x - 7.6179

R2 = 0.4808

Predictedy = 0.0003x - 0.0095

R2 = 0.9721

0

10

20

30

40

50

60

70

80

90

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

Tran

sver

se S

lab

Cra

ckin

g (%

)

Observed

Predicted

Transverse CrackingObserved (Linear)

Transverse CrackingPredicted (Linear)

Fig. 5.11 No sealants Eastbound transverse slab cracking comparison plots

114

Spalling Comparisons, No Sealant A EB

Observedy = 0.0287x - 0.2165

R2 = 0.5836

Predictedy = 0.0588x - 0.19

R2 = 0.9648

Calibratedy = 0.0287x - 0.2155

R2 = 0.9648

0

5

10

15

20

25

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

Spal

ling

(%)

Observed

Predicted

Calibrated

Spalling Observed (Linear)

Spalling Predicted (Linear)

Spalling Calibrated (Linear)

Spalling Comparisons No Sealant B EB

Predictedy = 0.0364x - 0.5235

R2 = 0.9336

Calibratedy = 0.0775x - 1.275

R2 = 0.9392

Observedy = 0.0783x - 1.3366

R2 = 0.9986

0

5

10

15

20

25

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

Spal

ling

(%)

Predicted

Observed

Calibrated

Spalling Predicted(Linear)Spalling Calibrated(Linear)Linear (Observed)

Fig. 5.12 No sealants Eastbound spalling comparison plots

115

IRI Comparisons, Delastic V 687 WB

Observedy = 0.3919x + 59.305

R2 = 0.6257

Predictedy = 0.0162x + 59.215

R2 = 0.9137

Calibratedy = 0.3903x + 59.29

R2 = 0.9134

0

10

20

30

40

50

60

70

80

90

100

110

120

130

140

150

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

IRI

(in/m

i)

Observed

Predicted

Calibrated

IRI Observed (Linear)

IRI Predicted (Linear)

IRI Calibrated (Linear)

IRI Comparisons, Techstar W 050 WB

Observedy = 0.2746x + 63.633

R2 = 0.9815

Predictedy = 0.0162x + 64.615

R2 = 0.9137

Calibratedy = 0.2735x + 80.293

R2 = 0.9136

0

10

20

30

40

50

60

70

80

90

100

110

120

130

140

150

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240Elapsed Time (Months)

IRI (

in/m

i)

IRI Observed

IRI Predicted

IRI Calibrated

IRI Observed (Linear)

IRI Predicted (Linear)

IRI Calibrated (Linear)

Fig. 5.13 Compression sealants Westbound IRI comparison plots

116

IRI Comparisons, Watson Bowman 812 WB

Predictedy = 0.0162x + 65.915

R2 = 0.9137

Observedy = 0.0772x + 64.403

R2 = 0.246

Calibratedy = 0.0769x + 64.42

R2 = 0.9141

0

10

20

30

40

50

60

70

80

90

100

110

120

130

140

150

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

IRI (

in/m

i)

Observed

) Predicted

Calibrated

IRI Predicted (Linear)

IRI Observed (Linear)

IRI Calibrated (Linear)

Fig. 5.13(Contd.) Compression sealants Westbound IRI comparison plots

117

Transverse Slab Cracking Comparisons, Delastic V 687 WB

Predictedy = 3.5344x - 19.829

R2 = 0.8976

Observedy = 0.0006x - 0.0153

R2 = 0.9703

0

10

20

30

40

50

60

70

80

90

100

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

Tran

sver

se S

lab

Cra

ckin

g (%

)

Observed

Predicted

Transverse CrackingObserved (Linear)

Transverse CrackingPredicted (Linear)

Transverse Slab Cracking Comparisons, Techstar W 050 WB

Observedy = 4.243x - 39.567

R2 = 0.85

Predictedy = 0.0006x - 0.0153

R2 = 0.9703

0

10

20

30

40

50

60

70

80

90

100

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

Tran

sver

se S

lab

Cra

ckin

g (%

)

Observed Transverse CrackingTechstar W050 WB

Predicted Slab Cracking Techstar W050 WB

Transverse Cracking Observed (Linear)

Transverse Cracking Observed (Linear)

Fig. 5.14 Compression sealants Westbound transverse slab cracking comparison plots

118

Transverse Slab Cracking Comparisons, Watson Bowman 812 WB

Observedy = 3.9043x - 29.757

R2 = 0.8979

Predictedy = 0.0006x - 0.0153

R2 = 0.9703

0

10

20

30

40

50

60

70

80

90

100

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

Tran

sver

se S

lab

Cra

ckin

g (%

)

Observed

Predicted

Transverse CrackingObserved (Linear)

Transverse CrackingPredicted (Linear)

Fig. 5.14(Contd.) Compression sealants Westbound transverse slab cracking comparison plots

119

Spalling Comparisons, Delastic V 687 WB

Observedy = 0.0005x + 0.047

R2 = 0.0014

Predictedy = 2E-05x - 0.0007

R2 = 0.8402

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

1.2

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

Spal

ling

(%)

Observed

Predicted

Spalling Observed(Linear)

Spalling Predicted(Linear)

Spalling Comparisons, Techstar W 050 WB

Predictedy = 2E-05x - 0.0007

R2 = 0.8402

Observedy = 0.0083x + 0.3565

R2 = 0.0231

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

1.2

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

Spal

ling

(%)

Predicted

Observed

Spalling Predicted(Linear)

Spalling Obseved(Linear)

Fig. 5.15 Compression sealants Westbound spalling comparison plots

120

Spalling Comparisons, Watson Bowman 812 WB

Observedy = 0

R2 = #N/A

Predictedy = 2E-05x - 0.0009

R2 = 0.9047

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

1.2

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

Spal

ling

(%)

Observed

Predicted

Spalling Observed (Linear)

Spalling Predicted (Linear)

Fig. 5.15(Contd.) Compression sealants Westbound spalling comparison plots

121

IRI Comparisons, Crafco 903 SL WB

Predictedy = 0.0355x + 66.551

R2 = 0.977

Observedy = -0.0397x + 68.884

R2 = 0.0667

0

10

20

30

40

50

60

70

80

90

100

110

120

130

140

150

160

170

180

190

200

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

IRI (

in/m

i)

Predicted

Observed

IRI Predicted (Linear)

IRI Observed (Linear)

IRI Comparisons, Dow 888 WB

Predictedy = 0.0355x + 77.751

R2 = 0.977

Observedy = 0.4502x + 58.495

R2 = 0.756

Calibratedy = 0.4523x + 58.271

R2 = 0.9769

0

10

20

30

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50

60

70

80

90

100

110

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130

140

150

160

170

180

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200

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

IRI (

in/m

i)

Predicted

Observed

Calibrated

IRI Predicted (Linear)

IRI Observed (Linear)

IRI Calibrated (Linear)

Fig. 5.16 Silicone sealants Westbound IRI comparison plots

122

IRI Comparisons, Dow 890 WB

Predictedy = 0.0343x + 64.057

R2 = 0.9771

Observedy = 0.4585x + 64.72

R2 = 0.5643

Calibratedy = 0.4785x + 64.657

R2 = 0.9771

0

10

20

30

40

50

60

70

80

90

100

110

120

130

140

150

160

170

180

190

200

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

IRI (

in/m

i)

Predicted

Observed

Calibrated

IRI Predicted (Linear)

IRI Observed (Linear)

IRI Calibrated (Linear)

Fig. 5.16(Contd.) Silicone sealants Westbound IRI comparison plots

123

Transverse Slab Cracking Comparions, Crafco 903 SL WB

Observedy = 1.7505x - 12.91

R2 = 0.8497

Predictedy = 0.0006x - 0.0153

R2 = 0.9703

0

10

20

30

40

50

60

70

80

90

100

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

Tran

sver

se S

lab

Cra

ckin

g (%

)

Observed

Predicted

Transverse Cracking Observed(Linear)

Transverse Cracking Predicted(Linear)

Transverse Slab Cracking Comparisons, Dow 888 WB

Observedy = 3.8452x - 36.008

R2 = 0.9018

Predictedy = 0.0006x - 0.0153

R2 = 0.9703

0

10

20

30

40

50

60

70

80

90

100

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

Tran

sver

se S

lab

Cra

ckin

g (%

)

Observed

Predicted

Transverse Cracking Observed(Linear)

Transverse Cracking Predicted(Linear)

Fig. 5.17 Silicone sealants Westbound transverse slab cracking comparison plots

124

Transverse Slab Cracking Comparisons, Dow 890 SL WB

Observedy = 0.8849x - 11.889

R2 = 0.8317

Predictedy = 0.0005x - 0.0136

R2 = 0.9702

0

10

20

30

40

50

60

70

80

90

100

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

Tran

sver

se S

lab

Cra

ckin

g (%

)

Observed

Predicted

Transverse Cracking(Linear)

Transverse Cracking(Linear)

Fig. 5.17(Contd.) Silicone sealants Westbound transverse slab cracking comparison plots

125

Spalling Comparisons, Crafco 903 SL WB

Observedy = 0.0228x - 0.1522

R2 = 0.4521

Predictedy = 0.0437x - 0.8239

R2 = 0.9232

0

1

2

3

4

5

6

7

8

9

10

11

12

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

Spal

ling

(%)

Observed

Predicted

Spalling Observed(Linear)

Spalling Predicted(Linear)

Spalling Comparisons, Dow 888 WB

Observedy = 0.0145x - 0.0716

R2 = 0.7323

Predictedy = 0.0437x - 0.8239

R2 = 0.9232

Calibratedy = 0.0145x - 0.475

R2 = 0.9232

0

1

2

3

4

5

6

7

8

9

10

11

12

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

Spal

ling

(%)

Observed

Predicted

Calibrated

Spalling Observed (Linear)

Spalling Predicted (Linear)

Spalling Calibrated (Linear)

Fig. 5.18 Silicone sealants Westbound spalling comparison plots

126

Spalling Comparisons, Dow 890 SL WB

Observedy = 0.1929x + 0.286

R2 = 0.3699

Predictedy = 0.0413x - 0.7802

R2 = 0.923

0

1

2

3

4

5

6

7

8

9

10

11

12

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

Spal

ling

(%)

Observed

Predicted

Spalling Observed(Linear)

Spalling Predicted (Linear)

Fig. 5.18(Contd.) Silicone sealants Westbound spalling comparison plots

127

IRI Comparisons, Crafco 221 WB

Predictedy = 0.0355x + 70.351

R2 = 0.977

Observedy = 0.0142x + 71.622

R2 = 0.0046

Calibratedy = 0.0142x + 71.115

R2 = 0.9763

0

10

20

30

40

50

60

70

80

90

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

IRI (

in/m

i)

Observed Predicted Calibrated IRI Predicted (Linear)IRI Observed (Linear)IRI Calibrated (Linear)

IRI Comparisons, Crafco 444 WB

Predictedy = 0.0355x + 63.551

R2 = 0.977

Observedy = 0.1036x + 64.132

R2 = 0.314

0

10

20

30

40

50

60

70

80

90

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

IRI )

(in/

mi)

Predicted

Observed

IRI Predicted (Linear)

IRI Observed (Linear)

Fig. 5.19 Hot pour sealants Westbound IRI comparison plots

128

Transverse Cracking, Crafco 221 WB

Observedy = 1.4269x - 12.786

R2 = 0.68

Predictedy = 0.0006x - 0.0153

R2 = 0.9703

0

10

20

30

40

50

60

70

80

90

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

Tran

sver

se S

lab

Cra

ckin

g (%

)

Observed

Predicted

Transverse CrackingObserved (Linear)

Transverse CrackingPredicted (Linear)

Transverse Slab Cracking Comaparisons, Crafco 444 WB

Observedy = 2.1849x - 20.389

R2 = 0.7639

Predictedy = 0.0006x - 0.0153

R2 = 0.9703

0

10

20

30

40

50

60

70

80

90

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

Tran

sver

se S

lab

Cra

ckin

g (%

)

Observed

Predicted

Transverse Cracking Observed(Linear)

Transverse Cracking Predicted (Linear)

Fig. 5.20 Hot pour sealants Westbound transverse slab cracking comparison plots

129

Spalling Comparisons, Crafco 221 WB

Observedy = 0.0327x - 0.3694

R2 = 0.6178

Predictedy = 0.0437x - 0.8239

R2 = 0.9232

0

1

2

3

4

5

6

7

8

9

10

11

12

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

Spal

ling

(%)

Observed

Predicted

Spalling Observed (Linear)

Spalling Predicted (Linear)

Spalling Comparisons, Crafco 444 WB

y = 0R2 = #N/A

Predictedy = 0.0437x - 0.8347

R2 = 0.9232

0

1

2

3

4

5

6

7

8

9

10

11

12

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

Spal

ling

(%)

Observed

Predicted

Spalling Observed(Linear)

Spalling Predicted(Linear)

Fig. 5.21 Hot pour sealants Westbound spalling comparison plots

130

IRI Comparisons, No Sealants WB

Predictedy = 0.0355x + 61.951

R2 = 0.977

Observedy = 1.0906x + 59.347

R2 = 0.8139

Calibratedy = 1.0959x + 59.386

R2 = 0.977

0

50

100

150

200

250

300

350

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

IRI (

in/m

i)

Predicted

Observed

Calibrated

IRI Predicted

IRI Observed (Linear)

IRI Calibrated (Linear)

Fig. 5.22 No sealants Westbound IRI comparison plots

131

Transverse Slab Cracking Comparisons, No Sealants WB

Observedy = 1.3441x - 19.288

R2 = 0.7168

Predictedy = 0.0006x - 0.0153

R2 = 0.9703

0

5

10

15

20

25

30

35

40

45

50

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

Tran

sver

se S

lab

Cra

ckin

g (%

)

Observed

Predicted

Transverse CrackingObserved (Linear)

Transverse CrackingPredicted (Linear)

Fig. 5.23 No sealants Westbound transverse slab cracking comparison plots

132

Spalling Comparisons, No Sealants WB

Observedy = 0.0171x + 0.1729

R2 = 0.3636

Predictedy = 0.0437x - 0.8239

R2 = 0.9232

0

1

2

3

4

5

6

7

8

9

10

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240

Elapsed Time (Months)

Spal

ling

(%)

Observed

Predicted

Spalling Observed(Linear)

Spalling Predicted(Linear)

Fig. 5.24 No sealants Westbound spalling comparison plots

133

6 CONCLUSIONS AND RECOMMENDATIONS

6.1 Research Summary

This research presents a fundamental step-by-step approach toward utilizing

performance prediction programs to project the future condition of a pavement system

from the available data, and also to evaluate the effectiveness of pavement features using

such projections. The objectives of this study were to examine the evolution of pavement

performance prediction equations and concepts over the last two decades, present a

compilation of suitable computer applications for rigid pavement performance prediction

and life cycle costing, apply a selected program to the data obtained from the Ohio Route

50 Project and draw conclusions in the following areas: (1) applicability of performance

prediction for the project considered and its significance to future maintenance and

rehabilitation (M&R) operations; (2) data required for and computational methods

available for this purpose; (3) cost effectiveness of joint sealants used in the project.

6.1.1 Literature Review

The literature review presented spans the last two decades and encompasses

pavement performance data collection and categorization; techniques used for the

development of performance prediction equations; and studies conducted as well as

significant models developed by various organizations and research agencies for rigid

and flexible pavement systems.

134

It is found that statistical/empirical methods are most commonly used to analyze

pavement distress data collected over several years and develop regression equations for

distress predictions. Mechanistic-based equations and probabilistic analyses are some

less common but equally significant methods. Several federal, state, and other agencies,

such as Federal Highway Administration (FHWA), American Association of State

Highway and Transportation Officials (AASHTO), World Bank, etc., have been actively

involved in the development of all these types of equations.

Much research has been conducted in the area of flexible pavements nationally by

AASHTO, Arizona Department of Transportation (ADOT), Texas Department of

Transportation (TxDOT), as well as others, and by the World Bank internationally, to

develop regression equations for rutting, roughness, and composite distress indices

(Paterson, 1987). A number of research centers around the globe are in the process of

developing mechanistic-based performance equations for flexible pavement systems,

utilizing the Superior Performing Asphalt Pavements (SUPERPAVE) technology.

In the field of rigid pavements, the first comprehensive nationwide study

conducted to develop distress prediction equations for all types of rigid pavements was

the National Cooperative Highway Research Program (NCHRP)-Concrete Pavement

Evaluation Study (COPES), by Darter, et al. (1985). Simple regression equations were

developed for the common rigid pavement distresses. Since then, several equations have

been developed using national/statewide distress databases and statistical/empirical or

mechanistic-based analysis methods for distresses, such as transverse slab cracking, joint

faulting, joint spalling, and pavement smoothness, as well as for composite distress

indices.

135

6.1.2 Computer Programs Available

Upon compilation of equations for distress predictions from several studies,

computer programs have been coded, which can calculate future distresses of pavements.

Various computer applications have been developed to accomplish the objectives of

performance prediction, and of life cycle cost analysis (LCCA) for rigid as well as

flexible pavement systems. To examine the Ohio Route 50 Project, an application for

rigid pavements was required, incorporating mechanistic-based performance prediction,

life cycle cost estimation, and offering a variety of M&R options for the future.

MicroPAVER and Rigid Pavement Life Cycle Cost Analysis (RPLCCA) were considered

for adoption, but rigid pavement performance prediction program Pavespec 3.0 was

finally selected for this purpose. This program was developed for the FHWA for the

purpose of aiding the formulation of performance related specifications (PRS) for an as

constructed rigid pavement system. The software incorporates mechanistic-based distress

prediction models for transverse slab cracking, joint spalling, joint faulting and pavement

smoothness. Pavement section behavior can be simulated in terms of the distresses using

various as constructed inputs. The M&R module allows users to select from among

various options, and also to compare their life cycle costs using the LCCA module.

6.1.3 Current State-of-the-Practice

An equally important aspect of this research was to investigate the applicability

and usage of performance prediction in the pavement industry. Performance prediction

and LCCA have been integral parts of several pavement management systems (PMS) that

are used by various state highway agencies (SHA). Such tools involve collection, storage

136

and analysis of performance data, examination M&R scenarios and planning for repair

and construction of pavement systems.

The main aspects of a PMS are: (1) distress data collection; (2) data

categorization and analysis; (3) pavement performance modeling; and (4) application of

performance prediction for planning and maintenance. Even though these aspects are the

same for any PMS, each SHA has a specific methodology to accomplish these objectives.

The Illinois Department of Transportation (IDOT) has the most advanced system with

mechanistic-based distress prediction models, formulation of future trends for each

section in a pavement network and mapping using Geographic Information System (GIS)

technology. Similarly, efforts are underway to build GIS technology into the TxDOT

Pavement Information Management System (PIMS), as well. The latter employs

regression equations that enable distress prediction for a variety of pavement system

types. For its part, the Colorado Department of Transportation (CDOT) has the most

commonly used methodology for pavement management, incorporating regression curves

developed using data from past state pavement monitoring activities. In case such

historical data is not available, CDOT uses default curves for performance prediction of

individual pavement sections.

6.1.4 Sensitivity Analysis Using Pavespec 3.0

As mentioned in the previous section, Pavespec 3.0 was selected as the computer

application to be used for distress prediction and comparative analysis at the Ohio Route

50 Project. Before proceeding with this task, however, a sensitivity analysis was

137

performed in order to examine the behavior of the program in response to variations in

input values.

A total of seventy nine input parameters were identified. Each one of these was

assigned low, medium and high values so as to examine its effect on the final pavement

distresses, while holding all the other inputs constant. Approximately one hundred and

fifty simulations were completed to test the reasonableness of the program’s results. A

brief summary of the findings are presented in Table 6.1, which enumerates the set of

input parameters that had the greatest influence on each of the output pavement distress

measures. Results obtained are in good agreement with the conventional wisdom and

expert opinion.

6.1.5 Application to the Ohio Route 50 Project

The Ohio Route 50 Project was conducted in the period 1997-2001 and involved

the construction of a rigid pavement system, installation of several joint sealants and

observation of pavement system and joint sealant material behavior. The sealant material

performance, and the distresses of joint spalling, transverse slab cracking, corner breaks

and pavement roughness were documented by the University of Cincinnati research team.

Input parameters describing the pavement system were extracted from the field

data and design assumptions used for the Ohio Route 50 Project (Ioannides, et al., 2002).

About one hundred simulations were completed to predict pavement distresses and life

cycle costs for each test section that used a particular sealant material. Graphical

comparisons of observed, predicted and calibrated distresses for each section were made

to assess the applicability of performance prediction. A cost analysis of each sealant

138

material over the entire service life of the pavement was conducted to determine the

relative cost effectiveness of each sealant material.

Results presented indicate that more data is necessary to obtain better

comparisons and calibrations and to improve the accuracy of projections with respect to

future pavement system behavior. It is also noted that the predicted distresses from the

program are much lower in severity than most of the observed data.

6.2 Conclusions

Graphical comparisons of observed, predicted and calibrated pavement distresses

indicate that values from the eastbound lanes of the Ohio Route 50 Project provide plots

with a higher coefficient of correlation (R2) than the values from the westbound lanes.

This can be attributed to the higher number of data points available for analysis and also

the longer period of monitoring activities. The periods of evaluation of eastbound lanes

and westbound lanes span over 40 months and 29 months, respectively. Additionally, the

international roughness index (IRI) values present the highest R2, in both eastbound and

westbound lanes.

A comparison of these values for each sealant and distress types of eastbound and

westbound lanes is presented in Table 6.2. It is evident that a greater number of data

points spread over a large period of time resulted in regression equations with the highest

R2 values. From a practical standpoint, this indicates that in order to effectively utilize

139

pavement performance prediction methods, a threshold number of data points spread over

a specific minimum period of time need to be gathered from the pavement system.

The graphical comparisons of observed, predicted and calibrated distresses for

each sealant type, present several instances in which the slopes of calibrated curves are

several times greater than the slopes of the predicted curves. This behavior, though

common for all distresses, is pronounced in the case of transverse slab cracking. Based

on a mechanistic analysis, Ioannides, et al., (2000) attributed the observed high levels of

this distress to the length, L, of the pavement slabs, expressed as a ratio to the radius of

relative stiffness, l, of the slab-subgrade system. Site specific factors, such as an

unexpected flood that weakened the subgrade and construction issues, were also cited as

possibly responsible for the premature deterioration of the pavement slabs. Pavespec 3.0,

however, predicts minimal transverse slab cracking percentages for all sections. The

incongruence of observed and predicted results may reflect the importance of the site

specific factors noted and point to the need to tailor the prediction model accordingly.

Furthermore, when such discrepancies occur, data calibration assumes a more significant

role, but Pavespec 3.0 limits this to linear regression.

The cost effectiveness of each sealant material was also determined, assuming

sealant replacement intervals between three and six years over the entire service life of

the pavement system, thereby formulating a common M&R plan for all pavement

sections. Projected costs emanating from sealant material and joint related repair, and

frequency of material replacement for the entire life cycle of the pavement system, were

the criteria used. Results indicate that compression seals, with the exception of Techstar

W-050, were the most cost effective sealant and needed minimal replacement and repair.

140

Among sections with silicone sealants, westbound sections showed better performance

and, therefore, higher cost effectiveness than their eastbound counterparts. This affirms

the effects of site-specific issues, mainly related to sealant installation and construction

aspects, which were highlighted by Ioannides et al (2002). The hot pour sealants were

not consistent in their cost effectiveness measures and so a conclusion cannot be made

based on the results.

6.3 Recommendations

This research presents the basic framework and guidelines for the application of

performance prediction and life cycle costing methods. It is evident from the

conclusions, that performance prediction is a powerful tool in evaluating various M&R

options, and the cost effectiveness of pavement features, such as sealing, overlays, etc.

The findings of this research show that the nature and quantity of data available, analysis

and calibration methods used (such as linear or non linear regression) are highly

significant. A good correlation between the observed and predicted distresses with high

R2 values results in accurate predictions. The primary success factors, however, are the

accuracy and capabilities of the performance prediction tool itself.

In the current research, distress and performance data were collected over a

relatively short period of time. In order to use performance prediction methods

accurately for comparison and analysis, the appropriate distress data have to be recorded

over a long period of time. The threshold number of data points required and the period

141

over which data collection should continue cannot be determined from this research

alone.

By using distress prediction and LCCA methods, application of compression

seals, with the exception of Techstar W-050, can be recommended for the test pavement

considered and similar scenaria. Compression sealants resulted in fewer replacements

and moderate costs over the simulated service life of the pavement system.

When applied to Ohio Route 50 pavement system, the calibration feature in

Pavespec 3.0 does not provide accurate results for transverse slab cracking and needs to

be reconsidered. Moreover, the life cycle costing module of this application can be

improved further. Costs incurred for the construction and maintenance of a pavement

system can be represented in a more detailed manner. Perhaps, the framework presented

in FHWA’s RealCost 2.1, which includes detailed construction, agency and user cost

considerations in the calculation of deterministic and probabilistic life cycle costs for

various alternatives, can provide a guide for this purpose.

142

Table 6.1 Input parameters with maximum influence on pavement distresses

JOINT FAULTING(in.)

PCC TRANSVERSE SLAB CRACKING (%)

JOINT SPALLING (%)

PAVEMENT SMOOTHNESS (IRI) (in./mi)

Pavement Type Concrete strength Joint Sealant Type

Pavement Type

Dowel Bar Diameter

PCC Slab Thickness Water Cement Ratio

Dowel Bar Diameter

PCC Slab Thickness

Climate Zone Number of Freeze Thaw Cycles

Average Annual Freezing Index

Shoulder Type Traffic Factors Concrete Strength

Number of Freeze Thaw Cycles

k - Value Percentage Trucks Slab Thickness Days above 90ºF Average Daily Traffic

Base thickness Air Content Concrete Strength

Average Annual Precipitation

k-Value Slab Thickness

Joint Spacing Air Content Stress Load Transfer

Efficiency

143

Table 6.2 Correlation coefficients of observed distress curves for eastbound and westbound lanes

EASTBOUND SECTIONS IRI (in./mi.)

TRANSVERSE CRACKING (%) SPALLING (%)

PERIOD OF OBSERVATION (MONTHS) 40 12 23

NUMBER OF DATA POINTS 7 3 5

JOINT SEALANTS IRI (R2)

TRANSVERSE CRACKING (R2) SPALLING (R2)

Delastic V 687 0.7133 0.6905 0.6482 Techstar W 050 0.4719 0.4808 1

Watson Bowman 0.6437 0.9423 0.6259 Crafco 221 0.2923 0.9231 1 Crafco 444 0.1553 0.7249 1 Crafco 902 0.0721 0.0168 1 Crafco 903 0.8188 0.4808 0.6534 Dow 888 0.5384 0.596 0.0198 Dow 890 0.6936 0.1196 0.4949

No Sealant (2) 0.8602 0.4808 0.5836 No Sealant (6) 0.8623 0.4808 0.9986

WESTBOUND SECTIONS IRI (in./mi.)

TRANSVERSE CRACKING (%) SPALLING (%)

PERIOD OF OBSERVATION (MONTHS) 29 19 23

NUMBER OF DATA POINTS 6 4 5

JOINT SEALANT IRI (R2) TRANSVERSE CRACKING (R2) SPALLING(R2)

Delastic V 687 0.6257 0.9703 0.0014 Techstar W 050 0.9815 0.85 0.0231

Watson Bowman 812 0.246 0.8979 1 Crafco 444 0.314 0.7639 1 Crafco 221 0.0046 0.68 0.6178 Crafco 903 0.0667 0.8497 0.4521 Dow 888 0.756 0.9703 0.7323 Dow 890 0.5643 0.8317 0.3699

No Sealant 0.8139 0.7168 0.3636

144

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