This report describes an analysis that was conducted to investigate the development of roughness

in pavements using data collected for the Long-Term Pavement Performance (LTPP) program.

The analysis included: (1) investigation oftime-sequence roughness data collected at test

sections to study trends in the development of roughness, (2) comparison between International

Roughness Index and Ride Number, (3) development of models to predict changes in roughness,

(4) investigation of roughness characteristics of new flexible and rigid pavements built for

Specific Pavement Studies in the LTPP program, and (5) investigation of roughness

characteristics of flexible and rigid pavements that were subjected to different rehabilitation

strategies.

Charles J.

Director

Office of Engineering

Research and Development

This document is disseminated under the sponsorship of the Department of Transportation in the

interest of information exchange. The United States Government assumes no liability for its

contents or use thereof. This report does not constitute a standard, specification, or regulation.

The United States Government does not endorse products or manufacturers. Trademarks or

manufacturers' names appear herein only because they are considered essential to the object of

this document.
\. Report No. I 2. Government Accession No. 3. Recipient's Catalog No.

FHWA-RD-97-147

4. Title and Subtitle 5. Report Date

INVESTIGATION OF DEVELOPMENT OF PAVEMENT May 1998

ROUGHNESS 6. Performing Organization Code

7. Author(s) 8. Performing Organization Report No.

Perera, R.W., Byrum, C., and Kohn, S.D.

9. Performing Organization Name and Address 10. Work Unit No. (TRAIS)

Soil and Materials Engineers, Inc. NCPC6B

43980 Plymouth Oaks Blvd.

I \. Contract or Grant No. Plymouth, Michigan 48170 DTFH61-95-C-00124

12. Sponsoring Agency Name and Address 13. Type of Report and Period Covered

Office of Engineering R&D Final Report

Federal Highway Administration June 1995 - August 1997

6300 Georgetown Pike 14. Sponsoring Agency Code

McLean, Virginia 22101-2296

IS. Supplementary Notes

Contracting Officer's Technical Representative (COTR): Cheryl Allen Richter, HNR-30

16. Abstract

The Long-Term Pavement Performance (LTPP) program was designed as a 20-year study of

pavement performance. The LTPP program consists of two programs: (1) General Pavement

Studies (GPS) that use in-service test sections in either their original design phase or in their first

overlay phase and (2) Specific Pavement Studies (SPS) that investigate the effect of specific

design features on pavement performance. A major data collection effort at these test sections is

the collection oflongitudinal profile data along the wheel paths, which is performed annually.

This report describes an investigation that was carried out to study the change in roughness at the

test sections using the profile data. At the time this analysis was carried out, profile data were

generally available for 4 years in the database. This report describes: (1) investigation of timesequence roughness data collected at GPS test sections to study trends in development of

roughness, (2) comparison between International Roughness Index and Ride Number,

(3) development of models to predict changes in roughness, (4) investigation of roughness

characteristics of new flexible and rigid pavements built for the SPS program, (5) investigation of

roughness characteristics of flexible and rigid pavements that were subjected to different

rehabilitation strategies under the SPS program, and (6) recommendations for quality assurance

and profiling frequency for the test sections.

17. Key Words 18. Distribution Statement

Roughness, pavement performance, pavement profile, No restrictions. This document is.

international roughness index, long-term pavement available to the public through

performance, flexible pavements, rigid pavements. the National Technical

Information Service, Springfield,

Virginia 22161.

19. Security Classif. (of this report) 120. Security Classif. (oflhis page) 21. No. of Pages I 22. Price

Unclassified Unclassified 306
• • •

APPROXIMATE CONVERSIONS TO SI UNITS APPROXIMATE CONVERSIONS FROM SI UNITS

Symbol When You Know Multiply By To Find Symbol Symbol When You Know Multiply By To Find Symbol

lENGTH lENGTH

in inches 25.4 miltimeters mm mm miltimeters 0.039 inches in It feet 0.305 meters m m meters 3.28 feet It yd yards 0.i14 meters m m meters 1.09 yards yd mi m~es 1.61 kilometers km km kilometers 0.621 miles mi

AREA AREA

in' square inches ~.2 square millimeters mm'mm' square m~limeters 0.0016 square inches in' ftI square teet 0.0i3 square meters m'

m' square meters 10.764 square feet ftI

yd' square yards 0.836 square meters m'

m' square meters 1.195 square yards yd' ac acres 0.405 hectares ha ha hectares 2.47 acres ac mil square m~es 2.58 square kilometers km'

km' square kilometers 0.386 square miles mi'

VOLUME VOLUME

f10z fluidounces 29.57 milliliters mL mL mnliliters 0.034 fluidounces fI oz gal gallons 3.785 liters L L liters 0.264 gallons gal ftI cubic teet 0.028 cubic meters m'

m' cubic meters 35.71 cubic feet It' y,OJ cubic yards 0.765 cubic meters m'

m3 cubic meters 1.307 cubic yards yd'

NOTE: Volumes greater 1han 1000 I shall be shown in m'.

_. MASS MASS

III

_.

oz OUnCl8S 28.35 grams g g grams 0.035 ounces oz Ib pounds ..0.45-4 kilograms kg kg kilograms 2.202 pounds Ib

T short tons (2000 Ib) 0.907 megagrams Mg Mg megagrams 1.103 short tons (2000 Ib) T

(or "metric ton") (or "l") (or "n (or "metric ton")

TEMPERATURE (exact) TEMPERATURE (exact)

Of Fahrenheit 5(F-32)19 Celcius "C "C Celcius 1.8C + 32 Fahrenheit of temperature or (F-32)11.8 temperature temperature temperature

IllUMINATION IllUMINATION

Ie Ioot-candles 10.76 lux Ix Ix lux 0.0929 foot-c:andles fc fI loot-Lamberts 3.426 candelalm' cd'm'

cdlm'

candelalm' 0.2919 Ioot·Lamberts fI

FORCE and PRESSURE or ~'''RESS FORCE and PRESSURE or STRESS

Ibf poundtorce 4.45 newtons N N newtons 0.225 poundforce Ibf

Ibllln' poundforce per 6.89 kilopascals kPa kPa kilopascals 0.145 poundforce per Ibflinz

squ818 inch square inch

• SI is the symbol lor !he Intemalional System of Units. Appropriate (Revised September 1993)

rounding should be made to comply with Seclion 4 of ASTM E380.
~

Chapter 1: Introduction 1

1.1 Long-Term Pavement Performance Program 1

1.1.1 General Pavement Studies (GPS) 2

1.1.2 Specific Pavement Studies (SPS) 2

1.1.3 Data Collection at GPS and SPS Sections 2

1.2 Research Tasks 4

1.3 Organization of the Report 5

Chapter 2: Roughness Development at GPS Sections 7

2.1 Profile Data Collection at GPS Sections 7

2.2 Examination of Changes in IRI at GPS Sections 9

2.2.1 Availability of Profile Data 9

2.2.2 Analysis Procedure 9

2.2.3 Examination ofIRI Plots 11

2.2.4 Investigation of Sections With Inconsistent IRI Data 18

2.3 Summary 23

Chapter 3: Comparison Between International Roughness Index and

Ride Number 27

3.1 International Roughness Index (IRI) 27

3.2 Ride Number (RN) 28

3.3 Computation of RN ~ 29

3.4 Comparison Between COY ofIRI and RN 30

3.5 Comparison Between Indices '" '" .33

3.6 Comparison of Trends in IRI and RN at Selected Sections .37

3.7 New Developments in Rideability Indices 37

3.8 Profile Statistic To Be Used in Analysis 41

3.9 Summary 41

Chapter 4: Data Synthesis 43

4.1 Selection of Data Elements .43

4.2 Development of Databases for Analysis .44

4.3 Data Manipulations 45

4.3.1 Laboratory Test Data 45
fw

4.3.2 Layer Thickness Data 45

4.3.3 Traffic Data 48

4.4 Data Availability 52

4.5 Initial PSI of GPS Sections 52

4.6 Data Distribution and Correlation Analysis 53

Chapter 5: Models to Predict Roughness Development at GPS Sections 55

5.1 General Trends in IRI Development.. ; 55

5.1.1 IRI Trends for GPS Experiments 55

5.1.2 Methods Used for Modeling 61

5.1.3 Definition of Variables Used in Modeling IRI 68

5.2 GPS-l: Flexible Pavements on Granular Bases 70

5.2.1 Dry Freeze Environment. 73

5.2.2 Dry-No Freeze Environment.. 80

5.2.3 Wet Freeze Environment 83

5.2.4 Wet-No Freeze Environment 92

5.2.5 GPS-l Summary 101

5.3 GPS-2:Flexible Pavements on Treated Bases l02

5.3.1 GPS-2 Asphalt-Treated Bases 104

5.3.2 GPS-2 Hot Mix AC Bases 106

5.3.3 GPS-2 Cement-Aggregate Mixture Bases 110

5.3.4 GPS-2 Soil-Cement Bases 113

5.3.5 GPS-2 Summary 113

5.4 GPS-3: Jointed Plain Concrete Pavements ,.116

5.4.1 Doweled GPS-3 in Wet Environment.. 119

5.4.2 Undoweled GPS-3 in Wet Environment.. 123

5.4.3 GPS-3 Sections in Dry Environment 127

5.4.4 GPS-3 Summary 130

5.5 GPS-4: Jointed Reinforced Concrete Pavements 131

5.5.1 GPS-4 IRI Prediction ModeL 134

5.5.2 Performance of Specific GPS-4 Sections 134

5.5.3 GPS-4 Summary 137
~

5.6 GPS-5: Continuously Reinforced Concrete (CRC) Pavements 138

5.6.1 GPS-5 IRI Prediction Model.. 138

5.6.2 Performance of Specific GPS-5 Sections 140

5.6.3 GPS-5 Summary '" 141

5.7 Analysis of Overlaid Pavements 142

5.7.1 Analysis Approach 142

5.7.2 Roughness Development at GPS-6 Sections 143

5.7.3 Roughness Development at GPS-7 Sections 150

5.7.4 Roughness Development at GPS-9 Sections 157

5.7.5 Summary for Overlaid Pavements '" 160

5.8 IRI Prediction Using Linear Models 161

Chapter 6: Roughness Characteristics ofSPS-l and SPS-2 Projects 165

6.1 Roughness Characteristics of SPS-l Projects 165

6.1.1 Description of SPS-l Experiment.. 165

6.1.2 Data Availability for SPS-l Projects 166

6.1.3 IRI After Construction for SPS-l Sections 167

6.1.4 Roughness Development at SPS-l Sections ~169

6.1.5 Summary for SPS-l Projects 176

6.2 Roughness Characteristics of SPS-2 Projects 176

6.2.1 Description of SPS-2 Experiment 176

6.2.2 Data Availability for SPS-2 Projects 177

6.2.3 IRI After Construction for SPS-2 Sections 179

6.2.4 Changes in IRI at SPS-2 Sections 184

6.2.5 Summary for SPS-2 Projects 191

Chapter 7: Roughness Characteristics ofSPS-5 and SPS-6 Projects 193

7.1 Roughness Characteristics of SPS-5 Projects 193

7.1.1 Description of SPS-5 Experiment 193

7.1.2 Data Availability for SPS-5 Projects 195

7.1.3 IRI Values of SPS-5 Sections Prior to Rehabilitation 195

7.1.4 IRI Values of SPS-5 Sections After Rehabilitation 199

7.1.5 Rate ofIncrease ofIRI at SPS-5 Sections 203

7.1.6 Summary for SPS-5 Projects 207
fw

7.2 Roughness Characteristics ofSPS-6 Projects 208

7.2.1 Description of SPS-6 Experiment.. 208

7.2.2 Data Availability for SPS-6 Projects ~ 209

7.2.3 IRI Values at SPS-6 Sections Prior to Pavement Rehabilitation 210

7.2.4 IRI Values at SPS-6 Sections After Pavement Rehabilitation 213

7.2.5 Rate ofIncrease ofIRI at SPS-6 Sections 217

7.2.6 Summary for SPS-6 Projects 221

Chapter 8: Recommendations for Quality Assurance and Profiling Frequency 223

8.1 Quality Assurance Procedures 223

8.1.1 Current Profile Data Collection Procedure 223

8.1.2 Recommendations for Quality Assurance .224

8.2 Profiling Frequency 226

8.2.1 Analysis Procedure 226

8.2.2 Recommendations for Profiling Frequency 227

8.3 Summary 229

Chapter 9: Summary of Key Findings 231

9.1 Development of Roughness at GPS Sections 231

9.2 Comparison Between IRI and RN 232

9.3 Roughness Models for GPS Sections 234

9.3.1 General Trends In Roughness Development 234

9.3.2 Models for Predicting IRI 236

9.3.3 General Observations on IRI Patterns 238

9.4 Roughness Characteristics ofSPS-1 and SPS-2 Projects 241

9.5 Roughness Characteristics ofSPS-5 and SPS- 6 Projects 243

9.6 Recommendations for Quality Assurance and Profiling Frequency 247

Chapter 10: Conclusions and Recommendations 249

10.1 Conclusions 249

10.2 Recommendations 254
fw

AppendixB Output From Linear Regression Analysis 263
2.2.12 Section with inconsistent IRI values caused by variations in the profiled

path 20
3.4.5 ~elationship between COY ofIRI and RN for GPS-5 sections .32

4.3.2 Graphical representation of traffic analysis scheme used for sections

with more than 4 years of traffic data 50

4.3.3 Graphical representation of traffic analysis scheme used for sections

with less than 4 years of traffic data 51

5.1.1 Trends in IRI development for GPS-1 through GPS-5 sections

(each line segment is one LTPP section) 56

5.1.3 Plots showing how different growth rate functions affect the

development of an arbitrary distress type 62
5.1.5 General distribution of the four climate classifications within the

continental United States 64

5.2.1 IRI development over time for GPS-l sections classified

according to LTPP regions 71

5.2.4 Correlation matrices used for review of GPS-l dry freeze

data and model building 76

5.2.6 Sensitivity of the dry freeze IRI prediction model to changes

in subgrade conditions. . 79

5.2.8 Sensitivity of the dry-no freeze IRI prediction model to changes in

traffic volume. holding all other parameters constant. 82

5.2.10 Sensitivity of the wet-freeze IRI prediction model to changes

in estimated SN. by varying AC thickness 85

5.2.12 Sensitivity of the wet freeze model to changes in number of freezethaw cycles/yr. . 88

5.2.14 Sensitivity of the wet freeze model (P200<20%) to changes in layer

properties at constant SN. . 91
5.2.16 Sensitivity of the wet-no freeze IRI prediction model (P200>50%)

to changes in estimated SN, holding AC thickness constant 95

5.2.18 Sensitivity of the wet-no freeze IRI prediction model (20%<P200<50%)

to changes in traffic volume/yr, holding all other parameters constant 97

5.2.20 Sensitivity of the wet no freeze IRI prediction model (P200<20%) to

changes in traffic volume/yr, holding all other parameters constant. 100

5.3.1 Plots showing the development of roughness for the GPS-2

sections within each LTPP geographical region 103

5.3.3 Sensitivity of the AC-treated base GPS-2 model to changes in

subgrade conditions 107

5.3.5 Sensitivity of the HMAC base GPS-2 IRI model to changes in traffic

volume, holding all other parameters constant 109

5.3.7 Sensitivity of the cemented-aggregate base model to changes in layer

thickness, holding all other parameters constant 112

5.3.9 Sensitivity of the GPS-2 soil-cement base model to changes in sub grade

conditions 115
5.4.3 Sensitivity of the GPS-3 (doweled) wet regions IRI model to changing

climatic conditions 122

5.4.5 Sensitivity of the GPS-3 (undoweled) wet regions IRI model to

increasing traffic rates 125

5.4.7 Sensitivity of the GPS-3 dry regions IRI model to changes in climate,

holding all other parameters constant 129

5.5.1 Trends in IRI development for the GPS-4 sections according

to LTPP regions 132

5.5.3 Sensitivity of GPS-4 IRI model to changes in slab thickness, holding

all other parameters constant. 136

5.6.1 IRI development for GPS-5 sections classified according to LTPP

geographical regions 139
6.1.5 Difference in IRI between right and left wheel path for test sections

in the SPS-1 project in Alabama 171

6.1.6 Comparison between left- and right-wheel-path IRI for SPS-1

test sections 171

6.2.5 Comparison between left- and right-wheel-path IRI for SPS-2

test sections .' 182
6.2.13 Filtered right-wheel-path profile for two profile dates for section 14 in

the Michigan SPS-2 project 190

7.1.1 IRI of test sections prior to pavement rehabilitation for selected

SPS-5 projects 197

7.1.6 Average IRI after overlay for sections receiving minimum and intensive

surface preparation prior to overlay 202

7.2.1 IRI of test sections prior to pavement rehabilitation for selected

SPS-6 projects 211
LIST OF FIGURES (Continued)

Fi~eNo, ~

7.2,5 IRI after overlay versus IRI before overlay for section 6 in SPS-6 projects ".,.215

7,2.6 Rate of increase ofIRI for selected SPS-6 projects ..""" "" " """".""" ..""".218

7.2.7 IRI values for SPS-6 project in Illinois." "."." ",."" "" " .." .."" ".",,220

7.2,8 IRI values for SPS-6 project in Michigan" .."" ...""." ...""."" "..."" ...."."" "...220
2.2.2 Percent distribution oftest sections according to availability of

time-sequence IRI data l 0

2.2.4 Sections with inconsistent IRI data summarized according to GPS

experiment. 23

2.2.5 Sections with inconsistent IRI data classified according to LTPP region .: 24

5.1.1 Percentage of GPS sections showing statistically significant linear

relationship between IRI and pavement age 59

5.1.2 Distribution of sections according to rate of increase oflRI obtained

from linear regression 59

5.1.3 Percentage distribution of sections according to rate of change

of IRI obtained from linear regression 60

5.1.4 Percentage distribution of sections classified according to coefficient

of determination (R2) obtained from linear regression 60

5.3.2 Parameters with highest correlation to IRI for the GPS-2 data sets

(separated by base type) 104
LIST OF TABLES (Continued)

Table No. fw

5.5.1 Ten parameters with highest correlation to IRI for GPS-4 sections 133

5.6.1 Ten parameters with highest correlation to IRI for GPS-5 sections 140

5.7.1 Frequency distribution of slope for GPS-6A sections 148

5.7.2 Correlation coefficients of slopes for GPS-6A sections 149

5.7.3 Frequency distribution of slope for GPS-7A sections , 154

5.7.4 Correlation coefficients of slope for GPS-7A sections 156

5.7.5 Pavement types in the GPS-9 experiment.. 158

5.7.6 Frequency distribution of slope for GPS-9 sections 158

6.1.1 Structural properties of SPS-1 sections 166

6.1.2 SPS-1 projects for which IRI data were available 167

6.2.1 Structural properties of SPS-2 sections 178

6.2.2 SPS-2 projects for which profile data are available 178

6.2.3 Average IRI for different base types in the SPS-2 projects 183

6.2.4 Average IRI of SPS-2 projects for the two PCC flexural strengths 185

6.2.5 Comparison ofIRI values ofSPS-2 projects in Michigan and Washington 185

6.2.6 IRI values at the Arizona SPS-2 project 189

7.1.1 Treatments applied to SPS-5 test sections 194

7.1.2 Surface preparation activities for SPS-5 test sections 194

7.1.3 SPS-5 projects for which profile data are available 196

7.1.4 Pavement structure prior to rehabilitation for SPS-5 projects 196

7.1.5 IRI before rehabilitation for SPS-5 test sections 198
7.1.6 Difference between maximum and minimum IRI of test sections

in an SPS-5 project before pavement rehabilitation 198

7.1.7 IRI after overlay for test sections receiving an overlay in

the SPS-5 experiment. 202

7.1.8 IRI before and after pavement rehabilitation for

control section in the SPS-5 projects 204

7.2.6 Difference between maximum and minimum IRI of test sections

in an SPS-6 project prior to treatment.. 212

7.2.8 IRI before and after maintenance for sections not receiving an

AC surface in the SPS-6 experiment 217

8.1.1 Cumulative frequency distribution of COY of IRI obtained

from five replicate runs 224

8.2.1 Percentage of GPS sections showing statistically significant

linear relationship between IRI and pavement age 226
8.2.2 Distribution of COY of GPS sections not exhibiting a statistically

significant linear relationship according to ranges ofCOV .227

9.3.1 Percentage of GPS sections showing statistically significant linear

relationship between IRI and pavement age 235

9.3.2 Distribution of sections according to rate of increase ofIRI obtained

from linear regression 235
AASHTO

AC

ACI

ASTM

COY

CRCP

ESAL

FHWA

GPS

IRI

JPCP

JRCP

KESAL

LTPP

NCHRP

NIMS

PCC

PSI

RIMS

RMSVA

RMS

RN

SHRP

SN

SPS

UMTRI

WIM

American Association of State Highway and Transportation Officials

Asphalt Concrete

American Concrete Institute

American Society of Testing and Materials

Coefficient of Variation

Continuously Reinforced Concrete Pavements

Equivalent Single-Axle Loads

Federal Highway Administration

General Pavement Studies

International Roughness Index

Jointed Plain Concrete Pavements

Jointed Reinforced Concrete Pavements

Kilo Equivalent Single-Axle Loads

Long- Term Pavement Performance

National Cooperative Highway Research Program

National Information Management System

Portland Cement Concrete

Present Serviceability Index

Regional Information Management System

Root Mean Square Vertical Acceleration

Root Mean Square

Ride Number

Strategic Highway Research Program

Structural Number

Specific Pavement Studies

University of Michigan Transportation Research Institute

Weigh in Motion
It is believed that the general public perceives a good road as one that provides a smooth

ride. Studies at the road test sponsored by the American Association of State Highway and

Transportation Officials (AASHTO) showed that the subjective evaluation of the pavement,

based on mean panel ratings, was primarily influenced by roughness. Therefore, the

development of roughness in pavements is a major issue for highway agencies. This report

presents the results of a study conducted to investigate the development of roughness in

pavements. Data collected for the Long-Term Pavement Performance (LTPP) program, which

was part of the Strategic Highway Research Program (SHRP), were used in this study.

SHRP was a 5 year, $150 million research program that began in 1987. The research

areas targeted under SHRP were asphalt, pavement performance, concrete and structures, and

highway operations. One aspect of SHRP was the LTPP program. The LTPP program was

designed as a 20-year study. The first 5 years of the program were administrated by SHRP, and,

thereafter, the administration ofthe program was transferred to the Federal Highway

Administration (FHW A).

The objectives ofthe LTPP program are to: (1) evaluate existing design methods;

(2) develop improved design methods and strategies for the rehabilitation of existing pavements;

(3) develop improved design equations for new and reconstructed pavements; (4) determine the

effects ofloading, environment, material properties and variability, construction quality, and

maintenance levels on pavement distress and performance; (5) determine the effects of specific

design features on pavement performance; and (6) establish a national long-term pavement

database to support SHRP objectives and future needs.

To accomplish the described goals, the LTPP program was divided into two

complementary programs. The first program, called General Pavement Studies (GPS), uses inservice pavement test sections in either their original design phase or in their first overlay phase.

The second program, called Specific Pavement Studies (SPS), investigates the effect of specific

design features on pavement performance.
Under the GPS program, more than 800 test sections were established on in-service

pavements in all 50 States and in Canada. The GPS sections generally represent pavements that

incorporate materials and structural designs used in standard engineering practices in the United

States and in Canada. Each GPS section is 152 m (500 ft) in length and is located in the outside

traffic lane. The data collected at the GPS sections include: climatic, material properties, traffic

frequency, deflection, profile, distress, and friction data. The objective of the GPS program is to

use the collected data to develop improved pavement design procedures. The GPS sections are

categorized into different experiments based on pavement type as shown in table 1.1.1.

The SPS experiments are designed to study the effect of specific design features on

pavement performance. Each SPS experiment consists of multiple test sections. The SPS

experiments that were designed for the LTPP program are shown in table 1.1.2.

The SPS-1 and SPS-2 projects involve the construction of new asphalt concrete (AC) and

portland cement concrete (PCC) pavements, respectively. Varying structural properties are used

in the construction ofthe different test sections. SPS-3 and SPS-4 experiments study the effect.

of different maintenance strategies on existing AC and PCC pavements. In the SPS-5 and SPS-6

experiments, different rehabilitation techniques are used in the rehabilitation of existing AC and

PCC pavements, respectively. The SPS-7 experiment studies bonded PCC overlays ofPCC

pavements. The SPS-8 experiment consists of AC and PCC test sections that are constructed on

low-volume roads and are designed to study environmental effects on these test sections. The

SPS-9 experiment consists oftest sections that are constructed with the Superpave asphalt

concrete mixes that were developed in the SHRP program. In this study, the SPS-1, SPS-2, SPS5, and SPS-6 projects were analyzed.

Data collection for the LTPP program is performed by four regional contractors. The

data collected at the GPS and SPS sections in each region are stored in the Regional Information

Management System (RIMS), which is maintained by each contractor. These data are uploaded

to the National Information Management System (NIMS) after undergoing quality-control

checks. The data from the NIMS are available to the research community. A major data
(jt-'~

Number Experiment Type

GPS-1 Asphalt Concrete Pavement on Granular Base

GPS-2 Asphalt Concrete Pavement on Stabilized Base

GPS-3 Jointed Plain Concrete Pavement

GPS-4 Jointed Reinforced Concrete Pavement

GPS-5 Continuously Reinforced Concrete Pavement

GPS-6 Asphalt Concrete Overlay of Asphalt Concrete Pavement

GPS-7 Asphalt Concrete Overlay of PCC Pavement

GPS-9 Unbonded PCC Overlay of PCC Pavement

SPS

Number Experiment Type

SPS-1 Strategic Study of Structural Factors for Flexible Pavements

SPS-2 Strategic Study of Structural Factors for Rigid Pavements

SPS-3 Preventive Maintenance Effectiveness for Flexible Pavements

SPS-4 Preventive Maintenance Effectiveness for Rigid Pavements

SPS-5 Rehabilitation of Asphalt Concrete Pavements

SPS-6 Rehabilitation of Jointed Portland Cement Concrete Pavements

SPS-7 Bonded PCC Overlay on PCC Pavements

SPS-8 Study of Environmental Factors in the Absence of Heavy Loads

SPS-9 Validation of SHRP Asphalt Specification and Mix Design
collection effort at the GPS and SPS sections is the collection of profile data that is performed

annually. The International Roughness Index (lRI) is computed from the profile data, and the

00, as well as the profile elevation data, are stored in the NIMS.

There were six main tasks in this research study, and a brief description of the work

performed for each task is presented in the following sections.

The collection of profile data for the GPS sections generally commenced on or after

1990. The time sequence IRI values for the GPS sections were investigated to observe the

changes in 00 with time.

The IRI was developed during a World Bank study. A primary requirement that was used

during the development of IRI was that it should correlate well with the output from responsetype roughness measuring equipment. Ride Number (RN) is a roughness index that is correlated

with the subjective opinion ofthe road users, and procedures for computing the RN are presented

by Spangler and Kelly.(\) The data from two studies that were sponsored by the N~tional

Cooperative Highway Research Program (NCHRP) and two studies funded by the Ohio

I?epartment of Transportation were used in developing RN. Relationships between 00 and RN

were investigated, and, for a select group of GPS sections, changes in 00 and RN over time were

compared. On the basis of results of this analysis, the 00 was selected as the roughness

parameter to be used in the other tasks.

The overall trends in the change in IRI for each GPS experiment were investigated.

Models were developed to predict roughness for each GPS experiment. The time-sequence IRI

values at the GPS sections were related to variables associated with that section that can affect

the development of roughness, such as subgrade type, traffic, and environmental parameters, to

develop these models.
An SPS-l project consists of AC surfaced test sections that have different layer types and

thicknesses. An SPS-2 project consists of jointed PCC test sections that have different slab

thicknesses, PCC strengths, and base types. The investigation of roughness characteristics at

SPS-l and SPS-2 projects provided information on roughness characteristics in newly

constructed pavements.

In the SPS-5 and SPS-6 experiments, different rehabilitation techniques are used in the

rehabilitation of existing AC and PCC pavements, respectively. An SPS-5 experiment has nine

test sections, while an SPS-6 experiment has eight test sections. The reduction in roughness

resulting from different rehabilitation techniques for SPS-5 and SPS-6 experiments, as well as

the development of roughness at the test sections, were studied.

On the basis ofthe analysis of the time-sequence IRI values at GPS sections,

recommendations for quality assurance during profiling were developed. The trends in change in

IRI observed at the test sections were used to develop guidelines for profiling frequency at the

GPS sections.

Chapter 2 describes the changes in IRI with time that were observed for the GPS sections.

Chapter 3 presents a comparison between the IRI and RN. Chapter 4 describes the data elements

that were selected for modeling the development of IRI and the data synthesis methods that were

used with data obtained from NIMS. Chapter 5 analyzes the overall trends in IRI that were

observed for each GPS experiment and presents the models that were developed to predict the

development oflRI. Chapter 6 describes roughness characteristics at SPS-l and SPS-2 projects,

while chapter 7 describes roughness characteristics at SPS-5 and SPS-6 projects.

Recommendations for quality assurance during profile data collection and recommendations for

data collection frequency for GPS test sections are presented in chapter 8. Chapter 9 presents a

summary of the key findings, while chapter 10 presents conclusions and recommendations for

future research.

The development of roughness at GPS sections was studied using the IRI as the

roughness index and the time-sequence IRI data that were available for each GPS section. The

study of the development of roughness at GPS sections consisted of examining the timesequence IRI data at each GPS section to study changes in 00.

In the LTPP program, the United States and Canada are divided into four regions: North

Atlantic, North Central, Southern, and Western. Figure 2.1.1 shows the area included in each

region. A regional contractor in each of these regions performs data collection at all GPS

sections located within the region. Each regional contractor uses a K.J. Law pro filer that is

equipped with optical sensors for profile data collection. These pro filers collect elevation data at

25-mm (l-in) intervals, the collected data are averaged using a 305-mm (l2-in) moving average,

and the data are recorded at 152-mm (6-in) intervals.

All GPS sections are generally profiled annually, and the profile data along the left and

the right wheel paths are collected. Although the time difference between two consequent dates

when a section is profiled is generally close to a year, differences of several months can be

present. This results in data being collected during different seasons at a section. For example, a

given section may be profiled during fall in one year, and in the subsequent year, it may be

profiled during winter. At each GPS section, the regional contractors collect data to obtain five

profiler runs that satisfy the following criteria:

1. The both-wheel-path IRI (average IRI of left and right wheel path) of three profiler runs are

within 1 percent of the mean both-wheel-path 00 computed from the five profiler runs.

2. The standard deviation of the both-wheel-path IRI for the five pro filer runs is less than 2

percent of the mean both-wheel-path 00 computed from the five pro filer runs.

If the profile data collected from the first five pro filer runs do not satisfy the two criteria,

additional pro filer runs are performed at the section in order to obtain five profiler runs that

satisfy the two criteria. However, the maximum number of profile runs performed at a section is

limited to nine runs. The IRI computed from the profile data is stored in the RIMS maintained

by the regional contractors. The 00 data are uploaded annually to the NIMS after they are
's.lsIcACHEWAN '

••• $1(' •••

MANTOBA , '.•.. ,

•• .~

D

HAWAII

H
subjected to quality-control checks. The profile elevation data collected by the pro filers are also

stored in the NIMS.

Table 2.2.1 presents the number of GPS sections in the LTPP program categorized

according to the GPS experiment and the LTPP region. In this table, GPS-6B and GPS-7B

experiments are overlaid sections for which the condition of the pavement prior to the overlay is

known. The time-sequence IRI data for the GPS sections were obtained from the NIMS. A

review of the data indicated that recent IRI data were not available in the NIMS. As data that

have not been uploaded to NIMS are available in RIMS, in order to use the latest available IRI

data in the analysis, the IRI data of the GPS sections were obtained from RIMS. The percent

distribution of test sections for each GPS experiment according to the number of times the

sections have been profiled is presented in table 2.2.2. Generally, sections that have been

profiled only once or twice for GPS experiments 1 through 5 have been dropped from the LTPP

program or have been overlaid, and some of the overlaid sections are now either a GPS-6B or a

GPS- 7B section.

The time-sequence IRI data at each GPS section were examined to study how the IRI

changes over time. All GPS sections were included in this analysis. For each profile date at a

GPS section, IRI for the five profiler runs performed on that day are available. The IRI of the

five pro filer runs for a given profile date were averaged to obtain the average IRI for each wheel

path. For each GPS section, these data were used to prepare a plot showing the variations in IRI

along the left and the right wheel path with pavement age. The pavement age corresponding to

each profile date was computed by subtracting the construction date of the pavement from the

profile date. For overlaid pavements (GPS-6, -7 and -9), the pavement age was computed with

reference to the overlay date, which was treated as the construction date.

Figure 2.2.1 shows an example of a plot that was developed for each GPS section. The xaxis of the plot represents the pavement age, and the left- and right-wheel-path IRI corresponding

to each profile date at that section are shown in the plot. The GPS section number; profile dates;

age of the pavement; and the left-, right- and both-wheel-path IRI (average ofleft- and right-
Table 2.2.2. Percent distribution oftest sections according to availability oftime-sequence IRI

data.
wheel-path IRI) at each profile date are shown at the top of each plot. The both-wheel-path IRI

represents the overall IRI for the section.

The change in IRI over time at a GPS section is expected to fall into one of the following

two categories:

1. Increase in 00: The IRI along both the left and the right wheel paths will increase with time,

as shown in figure 2.2.2. However, it is also possible for the IRI to increase in only one wheel

path, while the IRI along the other wheel path remains stable, as shown in figure 2.2.3. For both

cases, the both-wheel-path IRI increases with time.

150

125

- 100 'E

c 75 :::.

i 50

25

-"Ii

••

-+- Left Wheel Path

_ Right Wheel Path

4 6

Pavement Age (yr)
120

100

80

-

!

60

jg

40

20

0

0

-+- Left Wheel Path

__ Right Wheel Path

4

Pavement Age (yr)

1 in/mi = 0.0158 m/km

Figure 2.2.2. Section showing an increase in IRI for both wheel paths.

160

140

120

-

100

!

80

jg 60

40

20

J' ,/

~

•....••••

~ ~~ ~ ~

-+- Left Wheel Path

__ Right Wheel Path

1 in/mi = 0.0158 m/km

Figure 2.2.3. Section showing an increase in IRI for one wheel path.
2. IRI Remains Stable: There may be minor variations in the IRI over the years, but overall, the

IRI remains relatively stable. An example of such behavior is shown in figure 2.2.4, where the

both-wheel-path IRI remains stable over the years, although there are minor variations in IRI

along the left and the right wheel paths. The minor variations may be caused by differences in

the profiled path or seasonal variations in the pavement profile.

100

80

- 60 I

e:; 40

20

•.........-- ~ ~ ~

- ~

L

I

-+- Left Wheel Path

__ Right Wheel Path

1 inlmi = 0.0158 m/km

Figure 2.2.4. Section with a relatively stable IRI over time.

An examination of the IRI versus age of pavement plots indicated that some GPS sections

showed IRI trends that deviated from the two behavior patterns that were described. The GPS

sections for which the IRI behavior deviated from these two patterns could be classified into one

of the following four groups.

Group 1: The IRI of both the left and right wheel paths, or one ofthe wheel paths, for a given

profiling date was considerably higher than the IRI obtained for the profiling dates before and

after this date. An example of such behavior that occurs in both wheel-paths is shown in figure

2.2.5, while an example of such an occurrence in one wheel path is shown in figure 2.2.6.

Group 2: The IRI of both the left and the right wheel paths, or one of the wheel paths, for a

specific profiling date was considerably lower than the IRI obtained for the profiling dates before

and after this date. An example of such behavior that occurs in both wheel paths is shown in

figure 2.2.7, while an example of such an occurrence in one wheel path is shown in figure 2.2.8.
160

140

120

-

100

·e

!

80

~ 60

40

20

ilL ,,-::

Iii

10

Pavement Age (yr)

--+- Left Wheel Path

___ Right Wheel Path

1 in/mi = 0.0158 m/km

Figure 2.2.5. Section showing Groupl behavior along both wheel paths.

120

100

80

i'

I

60

i 40

20

/.~

~ --

--+- Left Wheel Path

___ Right Wheel Path

1 in/mi = 0.0158 m/km

Figure 2.2.6. Section showing Group 1 behavior along one wheel path.
140

120

100

- 80 ! 60 g;

40

20

--

..•...•....

• ...-

__ t

.........--

4

Pavement Age (yr)

-+- Left Wheel Path

__ Right Wheel Path

1 in/mi = 0.0158 m/km

Figure 2.2.7. Section showing Group 2 behavior along both wheel paths.

160

140

120

-

100

!

80

g; 60

40

20

•

-a.. ..•

w' .••.......

•.....• -+- Left Wheel Path

__ Right Wheel Path

1 in/mi = 0.0158 m/km

Figure 2.2.8. Section showing Group 2 behavior along one wheel path.
Group 3: The IRI for both the left and the right wheel paths for different profiling dates were

highly variable. An example of this behavior is shown in figure 2.2.9.

Group 4: An abrupt decrease in IRI was observed for both wheel paths, and the IRI at the

subsequent profiling date remained close to this 00. An example of this behavior is shown in

figure 2.2.10.

The inconsistent 00 data that were observed at the sections may be caused by the

following factors:

Variations in Profiled Path: A profile is a two-dimensional slice of road surface taken along a

specific path. If a measurement is repeated, the same profile can only be expected if the same

imaginary line is followed. Variations in the profiled wheel path for the different years can cause

changes in the IRI. In some pavements, there is considerable transverse variability, which can

cause considerable variations in the IRI, depending on the wheel path that is followed. If

distresses are present in the vicinity of the wheel path, this can result in an IRI that is different

between the years, and large variations in 00 can occur depending on whether the distress is

included or omitted in the profile. For a given year, the profiler operator may follow a specific

path and obtain five profiler runs that satisfy the data collection criteria. In the following year, at

the same section, the operator may follow a different path and obtain five profiler runs that satisfy

the data collection criteria, but whose 00 may be vastly different from the IRI of the previous

year.

Temperature or Seasonal Effects: Changes in IRI can occur in pavements because of changes in

profile caused by temperature or seasonal effects. In jointed PCC pavements, variations in the

profile of the pavement can occur due to temperature effects. On days where the air temperature

changes significantly over a 24-h cycle, the profile of a jointed PCC pavement can change during

the day from curling actions caused by temperature variations. In the night and the early morning

hours when the temperature at the pavement surface is lower than the temperature at the bottom of

the pavement, the pavement surface contracts relative to the bottom, and the edges of the slab curl

upward. In the afternoon hours, the slabs generally assume a flat shape. The magnitude of the

slab curl depends on the slab length, slab thickness, support conditions below the slab, and the

temperature gradient in the slab. Although the GPS sections are profiled annually, differences of

several months can be present between subsequent profiling dates. This can result in the section

being profiled during different seasons. The profile of a pavement can change because of

moisture effects on the subgrade that cause the subgrade to swell or shrink, and during winter
180

160

140

120

--I

100

80 j!; 60

40

20

0

0

100

80

-- 60 'E

c;.

j!; 40

20

0

0

-+- Left Wheel Path

__ Right Wheel Path

1 in/mi = 0.0158 m/km

Figure 2.2.9. Section showing Group 3 behavior.

-+- Left Wheel Path

__ Right Wheel Path

1 in/mi = 0.0158 m/km

Figure 2.2.10. Section showing Group 4 behavior.
months, frost heave of the subgrade and base layers can cause variations in the pavement profile.

Both of these effects will contribute to variations in the IRI.

Maintenance Activities: Maintenance activities on a section can change the IRI of a section.

Repair of distressed areas can lead to a reduction in IRI of the pavement. At a few sections, it

was observed that the IRI showed an abrupt decrease in a particular year, and then remained

generally close to this value in future profiling dates. This decrease in IRI may have been caused

by maintenance activities on the section. Maintenance activities may also cause the IRI of the

section to increase. When maintenance activities are carried out in the GPS sections, they are

documented in the maintenance table in the NIMS. However, there were very few entries in

these tables. The inconsistent IRI data that were observed could be attributed to maintenance

activities for only three cases, on the basis of data available in the maintenance tables. There is a

possibility that all the maintenance activities that have been performed on the sections have not

been entered in these tables.

Data Collection Errors: Data collected by the K.J. Law optical pro filers that are used in the LTPP

program are affected by sunlight. The pro filers have a shroud that prevents the sunlight from

interfering with the optical sensors. Sunlight creeping under the shroud of the pro filer can

contaminate the collected data, which causes an increase in IRI. Malfunctions in the

accelerometer, or incorrectly calibrated height sensors or accelerometers in the pro filer may also

cause invalid data to be collected. Equipment problems can also cause spikes to be introduced

into the profile. All these conditions will cause erroneous profile data to be collected.

GPS sections showing a behavior pattern similar to that described for Groups 1 through 4

were considered to have inconsistent IRI data. The profile elevation data of a selected group of

GPS sections for GPS experiments 1 through 5 that had inconsistent IRI data were reviewed to

determine if the cause of that behavior could be determined. The review of the data consisted of

viewing the profile data for the year in which the IRI was questionable, as well as viewing the

data for the previous and subsequent profile date. The profile elevation data were reviewed with

the use of Profs can software.(2) This software is used in the LTPP program to review profile data

and compute roughness indices.
The following are examples of test sections where the cause of the inconsistent IRI could

be identified.

Case 1 - Spikes in Profile Data: Figure 2.2.11 shows a section where the left-wheel-path IRI for

the fourth profile date was high when compared with the other years. A review of the profile

elevation data indicated that there was a non-pavement-related spike in the data, which resulted

in the high IRI. When the spike was removed, the left-wheel-path IRI decreased from 1.56 to

1.31 mIkm (99 to 83 in/mi).

120

100

80

-

!

60

i

40

20

/~-

p--

-+- Left Wheel Path

__ Right Wheel Path

1 in/mi = 0.0158 mIkm

Figure 2.2.11. Section with inconsistent IRI values caused by a spike in profile data.

Case 2 - Variations in the Wheel Path: For the section shown in figure 2.2.12, the right-wheelpath IRI for the second profile date was high when compared with the other dates. This section

has transverse cracks. The driver appeared to have followed a wheel path closer to the shoulder

during that year and picked up the wide cracks near the shoulder. This resulted in the increase in

IRI observed for the section in that year.

Case 3 - Equipment Problems: In the third profile date for the section shown in figure 2.2.13, the

IRI for both the left and right wheel paths were high when compared with the other years. A

review of the data indicated that the accelerometer in the vehicle may not have been working

properly for this profile date.
160

140

120

•.... 100 "e

g 80

jg 60

40

20

"

/ ••

•••••• ~

-+- Left Wheel Path

___ Right Wheel Path

1 in/mi = 0.0158 m/km

Figure 2.2.12. Section with inconsistent IRI values caused by variations in the profiled path.

160

140

120

•.... 100

~ 80

:::.

jg 60

40

20

ilL ~

i=i

10

Pavement Age (yr)

-+- Left Wheel Path

___ Right Wheel Path
Case 4 - Maintenance Activities on Section: Figure 2.2.14 shows a section whose IRI for both

the left and the right wheel paths showed an abrupt reduction for the fifth profile date and,

thereafter, remained close to this value at the sixth profile date. A review of the maintenance

data files indicated that an aggregate seal coat had been placed on the section after the fourth

profile date. The decrease in the IRI is attributed to the aggregate seal coat.

160

140

120

-

100

'E

!

80

!i 60

40

20

...•

I. -1\

-- "",-..- :

-

----+-- Left Wheel Path

__ Right Wheel Path

1 inlmi = 0.0158 m/km

Figure 2.2.14. Section with inconsistent IRI values caused by maintenance activities.

For the selected group of test sections reviewed, the cause of the inconsistent data could

be identified for 53 percent of the cases. For 23 percent of the cases, the cause of the inconsistent

data was attributed to variations in the profiled path. For most of the other 30 percent of the

cases, the inconsistent IRI values were caused by spikes in the data. The review of the sections

that had inconsistent IRI data was carried out by visually viewing the profile elevations using the

Profscan program. A more detailed investigation of these sections could be carried out using

filtering techniques. Such an analysis may explain the cause of the inconsistent IRI data for the

sections for which a reason for the inconsistent IRI data could not be identified by reviewing the

profile elevation data. However, this task was outside the scope of work for the contract. We

recommend that an investigation of such behavior patterns be conducted with the use of filtering

techniques, as such an investigation could yield valuable insight into variability of time-sequence

IRI data.
In some sections, if only the both-wheel-path IRI is examined, a clear inconsistency in

IRI is not seen. The averaging ofthe individual wheel-path data masks the performance. The

IRI for two such sections are shown in table 2.2.3. At GPS section 421598, the left-wheel-path

IRI for 12/17/92 is less than the IRI for the other dates. At GPS section 014929 for profile date

2/17/94, the left- as well as the right-wheel-path IRI are different from the values obtained for the

previous years, with the left-wheel-path IRI being lower, and the right-wheel-path IRI being

higher.

Table 2.2.4 presents the number of sections that had inconsistent IRI data for each GPS

experiment, and also indicates whether the inconsistency is seen for the both-wheel-path IRI, or

if individual wheel-path IRI has to be examined to see the inconsistency. Table 2.2.5 shows the

number of sections in each region where inconsistent time-sequence IRI data were observed and

categorized according to the GPS experiment. The percentage of sections that had inconsistent

IRI data for the four LTPP regions were: North Atlantic - 38 percent, North Central- 30 percent,

Southern - 15 percent, and Western - 28 percent. The lowest percentage was observed in the

Southern region, where all sections are located in the no-freeze environmental zone. Appendix A

includes a listing of all sections that show inconsistencies in IRI data.
Note 2: The time-sequence data for the both-wheel-path IRI does not clearly indicate an

inconsistency, but the IRI of a wheel path shows an inconsistency.

The time-sequence IRI data at each GPS section were examined to study how the IRI

changes over time. The IRI patterns observed for the sections were: (l) IRI along both the left

and the right wheel path, or along one wheel path, increases with time; (2) IRI remains relatively

stable over time, with minor variations between the profile dates; (3) IRI of both the left and the

right wheel path or one of the wheel paths for a given profiling date is higher than the IRI

obtained for the profiling dates before and after this date; (4) IRI of both the left and the right

wheel paths or one of the wheel paths was lower than the IRI obtained for the profiling dates

before and after this date, (5) IRI for both the left and the right wheel paths for different profiling

dates were variable; and (6) An abrupt decrease in IRI was observed on both wheel paths, and the

IRI at the subsequent dates remained close to this IRI. The sections that showed a IRI pattern

similar to causes (3) through (6) were classified as sections that showed inconsistent IRI values,

and 27 percent of GPS sections showed such a behavior.

The cause of this inconsistent IRI behavior can be attributed to one of the following

causes: (1) variations in the profiled paths between the years; (2) temperature or seasonal effects;

(3) maintenance activities at a section; or (4) data collection errors. A brief preliminary review
Region GPS Total I est Numoer ot :::>ectlons I-'ercentage

Experiment Sections in InconsIstent inconsistent lotal of Sections

Experiment Both- IRI in One With

Wheel-Path Wheel Path Inconsistent

IRI (Note 1) (Note 2) IRI (%)

North lil-':::>1 46 15 3 18 39

Atlantic GPS2 28 9 1 10 36

GPS3 15 5 1 6 40

GPS4 13 5 U 5 38

GPS5 10 2 2 4 27

GPSo 11 3 1 4 30

GI-'S( 10 3 2 0 OU

ljl-'S9 3 2 U 2 o(

Nonn lil-':::>1 34 1U 1 11 32

Central lil-':::>2 0 U 3 3 50

lil-':::>3 OU 14 3 17 34

GPS4 37 8 4 12 32

GPS5 25 4 2 6 24

GPS6 24 6 3 9 38

GPS7 33 4 4 8 24

lil-':::>9 11 2 1 3 2f

:::>outnern lil-':::>1 (8 8 0 13 1(

lil-':::>2 67 8 5 13 19

GPS3 28 1 3 4 14

GPS4 16 1 3 4 25

GPS5 35 2 0 2 0

ljPSo 2f U 2 2 (

lil-':::>( ( U 1 1 14

lil-':::>9 9 U 2 2 22

Western lil-':::>1 65 13 3 16 20

lil-':::>:.I: 35 10 2 1:.1: 34

GPS3 36 10 3 13 36

GPS4 0 0 0 0 0

GPS5 9 1 U 1 11

GPSo 4U 5 2 7 18

ljl-'S7 0 3 1 4 80

lil-':::>9 0 1 1 2 4U

Note 2: The time-sequence data for the both-wheel-path IRI does not clearly indicate an

inconsistency, but the IRI of a wheel path shows an inconsistency.
was performed on a selected group of sections that showed inconsistent IRI to see if the cause of

the behavior could be determined. For the selected group of test sections that were reviewed, the

cause of the inconsistent data could be identified for 53 percent of the cases. For 23 percent of

the cases, the cause of the inconsistent data was attributed to variations in the profiled path. For

most of the other 30 percent ofthe cases, inconsistent IRI values were caused by spikes in the

profile data.

CHAPTER 3: COMPARISON BETWEEN INTERNATIONAL ROUGHNESS INDEX

AND RIDE NUMBER

Longitudinal profile data can be used to compute a variety of profile indices. Examples

of such indices are International Roughness Index (IRI), Ride Number (RN), and Root Mean

Square Vertical Acceleration (RMSV A). Profile indices are single numerical values used to

quantify the ride quality of a pavement. For each profile index, a description of the ride quality

is associated with a range of the ride index. Each ride index is sensitive to different components

of road profile. Prior to developing predictive functions, a study of different indices was

necessary. This chapter presents a comparison between IRI and RN, computed from the profile

data measured at the GPS sections.

The IRI is a widely accepted roughness index in the United States and elsewhere in the

world. FHW A requires States to use the IRI as the standard roughness index for reporting the

roughness at the Highway Performance Monitoring Sections. The IRI was developed from the

data obtained from the International Road Roughness experiment, which was sponsored by the

World Bank and conducted in 1982 in Brazil?) The research results from a NCHRP project that

was performed in the late 1970's was utilized during the development of the IRI.(4) The computer

programs required to compute the IRI are presented in the World Bank Technical Paper Number

46.(5) A primary requirement of the development oflRI was that it should correlate well with

response-type roughness measuring equipment being used at the time. 00 has also been found to

correlate well with subjective opinions of road users.

Technically, the 00 is a mathematical representation of the accumulated suspension stroke

of a vehicle, divided by the distance traveled by the vehicle. The 00 is calculated mathematically

from the measured longitudinal profile with use of a quarter car simulation along a single wheel

path. The quarter car includes: one tire represented with a vertical spring, the mass of the axle

supported by the tire, a suspension spring and a damper, and the mass of the body supported by

the suspension for that tire. A simulation speed of 80 kmIh (50 mi/h) is used for the quarter car,

and the simulated suspension motion is linearly accumulated and divided by the length of the

profile to yield the 00. The coefficients used in the mathematical equations were those that

provided the maximum correlation to the output of response-type roughness measuring systems.

As inertial pro filers typically measure longitudinal profiles along the two wheel paths, the average

IRI for the section can be obtained by computing the IRI for each wheel path, and then averaging
the two values. The IRI is sensitive to wavelengths between 1 and 30 m (4 and 100 ft). The

response of a sinusoidal input to the IRI quarter car filter is shown in figure 3.1.1.(6) The

amplitude of the output sinusoiq is the amplitude of the input multiplied by the gain shown in the

figure. As seen in figure 3.1.1, the IRI filter has a maximum sensitivity at wavelengths of about

2.1 m (7 ft) and 15.2 m (50 ft).

I

~

V

/

--- J

I

/

/

/

~ t--.

-

Long before the advent of high-speed profiling, highway engineers have been interested

in estimating the opinion of the traveling public about the roughness of the roads. The Present

Serviceability Index (PSI), which estimates the opinion ofthe road users of a pavement with the

use of roughness and distress information, was developed during the AASHTO road test. The

PSI has a scale from 0 to 5, with a score of 5 being assigned to a perfect pavement. Studies at the

AASHTO road test have shown that about 95 percent of the information about the serviceability

of the pavement is contributed by the roughness of its surface properties.

The RN assigns a score of between 0 and 5 for a pavement and is computed by an

algorithm that provides a method to predict mean panel ratings from the measured profiles. The

procedure for computing the RN was presented by Spangler and Kelly.(l) The research input for
the development of RN was provided through two research projects sponsored by the Ohio

Department of Transportation(1,7) and two NCHRP projects.(8,9) In these experiments, panel

ratings were obtained at test sections, and longitudinal profile data along the two wheel paths of

the test sections were collected using a KJ. Law profiler. The data were used to develop a

profile analysis algorithm to predict mean panel rating from measured profile. This was

accomplished by using statistical techniques to find the spatial frequency bands of measured

longitudinal profile roughness that correlate to subjective panel ratings of ride quality.

The RN ranges from 0 to 5, with 5 representing a perfect road, and 0 representing a road

that is impassable. The RN computation method requires road profile data to be measured in two

wheel paths at 25-mm (I-in) intervals, averaged over a 305-mm (12-in) distance, and stored at

152-mm (6-in) intervals. Profiles along both the left and the right wheel path are required for the

computation ofRN. Only wavelengths in the range of 0.48 to 2.4 m (1.6 to 8 ft) are considered

in the computation ofRN. The computation method computes a profile index for each wheel

path. The profile index is the root mean square of the measured elevation profile after the

amplitude of the long wave length content of elevation profiles has been attenuated by a secondorder high-pass spatial filter having a natural frequency of 0.41 cycles/m (0.125 cycles/ft). A

nonlinear transform is used to relate the profile index to the RN. The RN's computed for the left

and the right wheel paths are then averaged to obtain the RN for the section. The RN computed

by the algorithm had a coefficient of determination (R2) of 0.84 with the actual measured

subjective ride quality and a standard error of estimate of 0.29. The subjective ride quality

estimates produced by this practice were found to be not significantly different in respect to

pavement type, road class, and vehicle size.

The GPS sections are profiled annually, and the profile data along the left and the right

wheel paths are collected. Multiple pro filer runs are made over each section in order to obtain

five pro filer runs that satisfy the criteria described in section 2.1 of this report. The profile

elevation data of the selected runs, as well as the IRI computed from the profile data, are stored

in NIMS. However, the RN of these profiles is not computed.

All longitudinal profile elevation data for the GPS sections that are available in the NIMS

were obtained to compute the RN. Examination ofthe profile elevation data revealed that data

up to 1993 were available in the NIMS; for some sections, data were only available up to 1992.

The computer program developed by Spangler and Kelly was used to compute the RN.(l) As
five profile runs are available in the NIMS for each profile date, the standard deviation and the

coefficient of variation (COV) of the RN corresponding to each profile date were also computed.

For a given profile date, the COY ofthe RN for a specific GPS section was computed as

described previously. For the same section, and the same profile date, the COY of the bothwheel-path IRI (average ofleft- and right-wheel-path IRI) was also computed. Thereafter, plots

between the COY ofRN and COY ofIRI were prepared separately for GPS experiments I

through 5. These plots are shown in figures 3.4.1 through 3.4.5. For each GPS experiment, all

sections belonging to that experiment and all available profile dates were used in preparing these

plots.

One profile data collection criterion at the GPS sections requires the standard deviation of

the five selected profile runs to be less than 2 percent of the mean IRI (see section 2.1). This

criterion is equivalent to specifying that the COY of the IRI computed from the five selected

profile runs should be less than 2 percent. This value was 3 percent at the beginning of the LTPP

program, and later it was reduced to 2 percent. Figures 3.4.1 through 3.4.5 indicate that, except

8.00

•

••

6.00

~t.. • • • •

i ••

'0 4.00

~

••

•• •

0

•

2.00

•

•• •

•

0.00

0.00 2.00 4.00 6.00 8.00

COY RN (lYe)
8.00

•

6.00 • •

•

~ •

•• •

i

4.00

•

•

'0

~

•• (,)

•

•

2.00 I

• • •

•

•

0.00

0.00 2.00 4.00 6.00 8.00

COY RN (e/o)

6.00

~ • •

i

• •

4.00 •

'0

>

0

(,) •

••

2.00 •

•

0.00 •

0.00 2.00 4.00 6.00 8.00

COY RN(%)
6.00

~

jg •

•

'0 4.00

0

> • • •

(J

2.00 ••

•

• •

•.•I.~#·••

• •

• •

0.00

. ~ .

•

0.00 2.00 4.00 6.00 8.00

COV RN(%)

6.00

•

l

jg • •

4.00

'0 •

~ •

(J

• •

• • •

2.00

• •• • .~. •

•

0.00

0.00 2.00 4.00 6.00 8.00

COY RN(%)
for a few points, the majority of data points have a COY less than 3 percent. This indicates that

the five profile runs for a specific test date that is available in NIMS have very repeatable IRI

values.

Figures 3.4.1 through 3.4.5 show that the COY ofIRI and RN can have different values at

the same section. There are cases where low COY values ofIRI have high COY values ofRN,

and vice versa. The range of wavelengths that contribute to the 00, which is between 1.2 and 30

m (4 and 100 ft) is different than the range of wavelengths that contribute to RN, which is

between 0.48 and 2.4 m (1.6 and 8 ft). Therefore, depending on the wavelength content of the

multiple pro filer runs, variability in the wheel paths can affect the two indices differently and

cause differences in the COY between the two parameters.

Figures 3.5.1 through 3.5.5 show the relationship between IRI and RN for GPS

experiments 1 through 5. Each point in a plot represents the average IRI and the average RN

computed from the five profiler runs available for a specific profiling date at a section. Each plot

includes data for all sections and all profile dates that were considered in the analysis.

The plots for the AC-surfaced sections (GPS-1 and GPS-2) show a wider scatter in the

relationship between 00 and RN at the higher IRI when compared with the PCC sections (GPS-3

through GPS-5). The GPS-3, -4 and -5 sections show a closer band than the AC sections. It

appears that the roughness component wavelength of these sections is from the shorter

wavelengths. These plots show that a unique relationship does not exist between IRI and RN.

As these plots show, for a specific value ofRN there is a wide range ofIRI that would have the

same RN. These variations are caused by differences in the wavelength content in the profiles.

For a simple illustration of this phenomenon, consider the two profiles shown in figure

3.5.6. Both profiles have 00 values that are close to each other, but the RN's are substantially

different. The IRI is sensitive to wavelengths from 1.2 to 30 m (4 to 100 ft), while the RN is

only sensitive to wavelengths from 0.48 to 2.4 m (1.6 to 8 ft). The short wavelength content in

the first profile is larger than the second profile, and therefore it has a lower RN.
300

•

250

•

200

......

I 150

i

100

50

0

1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00

RN

1 in/mile = 0.0158 rnIkm

Figure 3.5.1. Relationship between IRI and RN for GPS-l sections.

300

250

200

:i

I

•

150

i

100

50

0

1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50

RN

1 in/mile = 0.0158 rnIkm

Figure 3.5.2. Relationship between IRI and RN for GPS-2 sections.
300

250

• ••

200

.-I 150

i

100

50

0

1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50

RN

1 in/mile = 0.0158 m/km

Figure 3.5.3. Relationship between IRI and RN for GPS-3 sections.

300

250

200

.-I 150

i

100

50

0

1.00 2.50 3.00

RN

1 in/mile = 0.0158 m/km

Figure 3.5.4. Relationship between IRI and RN for GPS-4 sections.
300

250

200

;..f 150

~

100

o --~~- ----.--.-j-.---.--- -.~

1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50

RN

1 in/mile = 0.0158 m/km

Figure 3.5.5. Relationship between IRI and RN for GPS-5 sections.

PJevation (in)

0.5

100

Distance (ft)

PJevation (in)

0.5

Amplitude = 0.25 in, Wavelength = 50 ft

o 50 100

Distance (ft)

1 in = 25.4 mm, 1 ft = 0.305 m

Figure 3.5.6. Dependency ofIRl and RN on wavelength content of profiles.
A specific IRI value does not indicate a unique profile associated with the IRI value.

There could be numerous profiles, each having a different wavelength content, but the IRI of

these profiles can be the same value. The RN of these profiles is only sensitive to wavelengths

between 0.48 and 2.4 m (1.6 to 8 ft), and, depending on the wavelength content between these

limits, the profiles that have the same IRI will have different values for RN. This is the cause of

the nonuniqueness of the relationship between IRI and RN that is shown in figures 3.5.1 through

3.5.6.

A comparison between the variation of IRI and RN over time for a selected group of

sections in GPS experiments 1 through 5 was performed. The IRI and RN that were computed

from the time-sequence profile data at selected GPS sections were plotted against pavement age.

Figures 3.6.1 through 3.6.5 present the comparison between these two parameters for four

sections in GPS experiments 1 to 5, respectively. The selected sections presented for each GPS

experiment include sections having low as well as high IRI values. These plots show that both

the IRI and the RN follow the same trend.

The algorithm presented by Spangler and Kelly(\) for computing the RN is only valid for

profiles obtained from pro filers that obtain data at 25-mm (l-in) intervals, and then apply a 305-

mm (l2-in) moving average and record data at 152-mm (6-in) intervals. Currently only KJ. Law

profilers satisfy these data collection criteria; this limitation prevents the RN from being

computed for profiles collected by other profiling devices.

The University of Michigan Transportation Research Institute (UMTRI) developed a new

ride index called the Rideability Number in a research project that was sponsored by the

FHW A. (1 0) Data obtained from a panel rating study conducted in Minnesota, as well as the data

collected for the NCHRP and Ohio Department of Transportation studies, were used in

developing this parameter. The final report documenting this research was published in June

1996. This report was published after the analysis described previously in this chapter was

performed. The Rideability Index developed by UMTRI is not restricted to a specific profiling

sample interval, and can be computed for any data sampling interval. However, profiles obtained

from ultrasonic devices do not have sufficient resolution to accurately measure short wavelengths,
1 150

1

it 100

)150

it 100

r

:"4.'

,

I150

it 100

1 in/mi = 0.0158 m/km

Figure 3.6.1. Comparison of trends between RN and IRI for GPS-1 experiment.

Section 18-2008

I.=)

B

/'b-+-RN Bi:i

iil 100

50t 4.'

0 ,

10 l' 20 ~ 30

A •• (y.)

Section 56-2020

~

200 2.'

I150

Bi:i

it 100 .r

50 ..,

0 ,

0 10 l' 20 ~ 30

A •• <y<)

Section 36-1643

~ 2

200

t

B

I150

3

3.' i:i

it 100

4

50 ..,

0 ,

0

, 10 l' 20 ~ 30

A•• (y.)

Section 28-1016

~o 2

200 B

I1'0

3

Bi:i

it 100

a::II 4

50 4.'

0 ,

0

, 10 l' 20 ~ 30

AIO<Y<)

1 in/mi = 0.0158 m/km

Figure 3.6.2. Comparison of trends between RN and IRI for GPS-2 experiment.
1

130

ii 100

30

~2.5

L--=:J 35~

)130

ii 100

i!I I. :1100

30

o I I I

10 15 20

"-' (yr)

r-;..

4.5

( f 5

10 15 20

A •• (yr)

1 in/mi = 0.0158 m/km

Figure 3.6.3. Comparison of trends between RN and IRI for GPS-3 experiment.

F

-+-1Rl 1;·5 L--=:J 35~

4

4.5

I I S

20 25 30

I

15

A •• (yr)

r-:,.

4.5

5

I

15

"-' (yr)

r-;..

4.5

5 I

15

A•• (yr)

r-;..

4.5

5

1 in/mi = 0.0158 m/km

Figure 3.6.4. Comparison of trends between RN and IRI for GPS-4 experiment.
~~I [d,~

S 10 IS 7ll

Ale (yr)

f':,.

4.S

S I

IS

AIe(yr)

r

:,.

4.S

S

iI ISO

l

Ii 100

ii ISO

:s ~

Ii 100

1 in/mi = 0.0158 m/km

Figure 3.6.5. Comparison of trends between RN and IRI for GPS-5 experiment

and the Rideability Index cannot be computed for profiles obtained from these devices. The

Rideability Index is computed by: (1) first filtering the profile with a band-pass filter, (2)

computing a Root Mean Square (RMS) value from the filtered profile called the profile index,

and (3) transforming the profile index to the Rideability Index. The Rideability Index is

influenced by wavelengths ranging from 0.6 to 9.1 m (2 to 30 ft). For a profile, the RN

computed from the Spangler and Kelly algorithm is not equal to the Rideability Index developed

by UMTRI. This is because the wavelengths and the gain factors that are used by the two indices

are different. However, there is a very good correlation between these two indices. As there is a

very good correlation between the RN and the Rideability Index, relationships between the

Rideability Index and the IRI are expected to be similar to the relationships that were observed

,

fortheRN.

Currently, an American Society of Testing and Materials (ASTM) standard for computing

the RN, which uses the algorithm for Rideability Index developed by UMTRI, has been proposed

and is going through the balloting process of ASTM.
An objective ofthe comparison between the IRI and the RN was to determine which of

the two statistics would be used to develop models to predict roughness development. The IRI

was selected as the parameter to be used in future analyses on the basis of the following factors:

1. A comparison of the trends in time-sequence IRI and RN that was performed at selected

sections did not show a difference in the observed trends.

2. Profile elevation data are required for computing the RN, as this parameter is not a computed

quantity that is stored in the NIMS. The latest profile elevation data that were available in

the NIMS at the time the profile data were obtained for this study was 1993. For some

sections, data were available only up to 1992. For most sections, this would provide profile

data for less than 3 years. More recent IRI data for the GPS sections were available from the

regional contractors in the RIMS. Therefore, at each test section, more time-sequence IRI

data were available compared with time-sequence data for the RN. Because roughness trends

can be better defined with more data points, using the IRI in the future analysis of this study

presented a clear advantage.

'3. The RN used in this analysis was based on the algorithm presented by Spangler and Kelly.(l)

Efforts are currently under way by ASTM to introduce the Rideability Index (developed by

UMTRI) as a standard method to compute a ride index. Basing our analysis on an index that

is declining in relevance is not justifiable.

A comparison between the IRI and the RN (computed from the algorithm developed by

Spangler and Kelly) indicated that a unique relationship between the IRI and RN does not exist.

The IRI is sensitive to wavelengths between 1.2 and 30 m (4 and 100 ft), while the RN is

sensitive to wavelengths between 0.48 and 2.4 m (1.6 and 8 ft). For a specific value ofRN, there

could be a range ofIRI values that correspond to that RN. For example, consider two profiles

that have similar short wavelength contents, but different long wavelength contents. As the RN

is sensitive to the short wavelength content, the two profiles can have the same RN, but as the

long wavelength contents are different, they can have different IRI values. The study of trends in

IRI and RN at selected sections indicated that these two parameters generally have similar trends.

As the IRI has a maximum sensitivity at wavelengths of2.1 m (7 ft) and 15.2 m (50 ft), short
wavelength content also has a significant contribution to the IRI. Therefore, short wavelength

contents that are between 0.48 and 2.4 m (1.6 and 8 ft) that reduce the RN will cause an increase

in the 00. The RN computation method of Spangler and Kelly requires road profile data

measured in two wheel paths at 25-mm (I-in) intervals, averaged over a 305-mm (12-in)

distance, and stored at I 52-mm (6-in) intervals. The UMTRI developed a new ride index called

the Rideability Number in a research project that was sponsored by the FHW A. The Rideability

Index can be computed from profiles obtained for any sampling interval. Efforts are currently

under way by ASTM to introduce the Rideability Index (developed by UMTRI) as a standard

method to compute a ride index.
The data collected for the LTPP program are stored in the NIMS database. A description

of the data that are available in the NIMS is presented in the Long Term Pavement Performance

Information Management System Data User's GUideY 1) The data available in the NIMS can be

divided into the following categories: inventory, maintenance, climatic, monitoring, traffic,

materials testing, and rehabilitation. A brief description of the data elements contained in each

category follows.

Il}ventory Data: Inventory tables contain information related to the location of the section and

material characteristics of the pavement obtained from State transportation agency archives.

Inventory data collection sheets were filled out by the State highway agencies for all GPS

sections, and these data are included in the inventory tables in the database.

Maintenance Data: Data tables in this category record maintenance activities performed on the

GPS test sections after the sections were included in the LTPP program.

Climatic Data: The climatic data available are derived from weather data originally collected by

the National Oceanic and Atmospheric Administration and the Canadian Climatic Center (for

Canadian test sections). Data collected from five weather stations that are close to each GPS

section are available in these data tables.

Monitoring Data: Data tables in this category contain data recorded by monitoring equipment.

These include profile data, deflection data, friction data, surface distress data, and transverse

profile data.

Traffic Data: Traffic data tables contain historical traffic estimates provided by State highway

agencies for each GPS section. Some limited section-specific monitored data collected by weigh

in motion (WIM) equipment are also available.

Materials Testing: Extensive laboratory testing of materials performed on samples obtained just

outside the 152-m- (500-ft-) long GPS section to characterize pavement layer and sub grade
properties. Laboratory test data for pavement and subgrade materials are contained in the tables

that fall into this category.

Rehabilitation Data: Major improvements to a test section after it is included in the LTPP

program are documented in the data tables that are in this category.

A goal in this study was to develop models to predict the development of roughness

from the data available in the NIMS. The first step in the study was to identify variables that

are available in the LTPP database that could be related to the development of roughness. A

previous research effort to develop models to predict pavement roughness using LTPP data was

performed by Simpson et al.(12) This report documents data elements that were identified by an

expert panel as significant factors that affect the development of roughness. Models to predict

roughness from data obtained from a study performed in Brazil is presented by Paterson. (13)

These two reports were reviewed to identify data elements that have been related to roughness

development in previous studies. The data elements that were available in the LTPP data tables

were reviewed, and engineering judgment as well as information obtained from previous

studies were used to identify data elements that may be relevant in this analysis. The tables

containing the relevant data elements were obtained from the sixth informational release of

NIMS, and are described later in this chapter. A review ofthe obtained data indicated that

some of the desired information was not available. In these cases, the information was

obtained from RIMS through the regional contractors.

One of the most time-co,nsuming portions of this study was the manipulation of the data

files obtained from the NIMS and RIMS. Statistical analysis software requires databases that

are row structured, where a dependent variable and the associated independent variables are

contained in one row. The data files obtained from NIMS and RIMS were manipulated in order

to develop such a structure for each GPS section.

Separate data tables were obtained for each GPS experiment from the NIMS. Each data

table contains specific information by each GPS section, and the data element of interest for the

analysis had to be extracted from the data file. Each data file obtained from NIMS was

imported into a spreadsheet and the data elements that were to be used in the analysis were

extracted. This resulted in separate spreadsheet files for each data element that contained the

GPS identification number and the data element of interest in each row. Thereafter, an analysis
file was created for each GPS section. This file contained all data elements organized into a

single row for each GPS section. This process was performed by importing the spreadsheets

containing the data elements into a relational database, and then linking the tables to create a

data table. All GPS sections, including the sections that had inconsistent IRI trends (see

section 2.2.4) were included in the analysis. Prior to assembling the analysis database, certain

manipulations had to be performed on several data elements, as described in section 4.3. A

climatic database developed in a previous study was used to obtain the climatic data for this

study.(12) Table 4.2.1 summarizes the information compiled for each GPS section and also

presents the table name in the NIMS that contains this data element.

The data files obtained from NIMS are structured in a row format, where the first

column contains the GPS identification number, and the other columns contain the data

associated with the section. However, there are typically many rows of data for the same GPS

section within each data file. For example, where laboratory test results are available for

multiple locations for a GPS section, each test result is contained in a separate row. The

pavement layer thicknesses for GPS sections are contained in multiple rows, with each row

containing data for one layer. The data file for historical traffic information can contain up to

25 rows of traffic information per GPS section, where each row contains the estimated traffic

parameters for the GPS section for one year The data files must then be processed and

arranged such that all the relevant information for each section is contained in one row before

being imported to the relational database to create the analysis file. This process was generally

performed in spreadsheets with the use of macros. The following sections describe examples

of data manipulations that were performed on the data obtained from the NIMS.

For each GPS section, laboratory test data such as percentage passing the 75-J.lm (No.

200) sieve for subgrade, or PCC compressive strength, are available for multiple locations.

These data elements were averaged to obtain a single representative value for each section.

The NIMS tables for layer thickness for a GPS section consist of a separate row for

each layer. Even for AC, separate rows contain the layer thickness for the surface and leveling
Data Element Applicable

Experiment NIMS Table

Section Data

Four-digit GPS section number GPS 1 to 9 -

Two-digit State identification number GPS 1 to 9 -

LTPP geographic region GPS 1 to 9 -

LTPP GPS experiment number GPS 1 to 9 EXPERIMENT_SECTION

Construction date GPS 1 to 9 INV AGE

PronIe Data

Age of pavement at time of profile measurement GPS 1 to 9 Computed

IRI values GPS 1 to 9 MON PROFILE MASTER

Pavement Layer Data

AC Thickness from core data GPS 1,2,6 TST L05B

PCC thickness GPS 3,4,5,7,9 TST L05B

Overlay thickness GPS 6,7,9 RHB IMP, INV_MAJOR IMP

Overlay date GPS 6,7,9 RHBIMP

Overlay code GPS6,7,9 RHBIMP

Base thickness GPS 1 to 9 TST L05B

LTPP material code for the base GPS 1 to 9 TST L05B

Approximate structural number GPS 1, 2, 6 Computed

Sub grade Data

Subgrade liquid limit GPS 1 to 9 TST UG04 SS03

Subgrade plastic limit GPS 1 to 9 TST UG04 SS03

Subgrade plasticity index GPS 1 to 9 TST UG04 SS03

Subgrade percent passing No.4 sieve GPS 1 to 9 TST SSOI UGOI UG02

Subgrade percent passing No. 200 sieve GPS 1 to 9 TST SSOI UGOI UG02

Subgrade moisture content GPS 1 to 9 TST SSOI UGOl_UG02

Subgrade liquidity index GPS 1 to 9 Computed

LTPP subgrade material code GPS 1 to 9 TST L05B

Overburden pressure on subgrade from pavement system GPS 1 to 9 Computed

Climatic Data

Annual precipitation GPS 1 to 9 Previous study (reference 12)

Number of days below 3rF/yr GPS 1 to 9 Previous study (reference 12)

Number of days above 9O°F/yr GPS 1 to 9 Previous study (reference 12)

Number of wet days/yr GPS 1 to 9 Previous study (reference 12)

Number of days with> 0.5 in. precipitation per/yr GPS 1 to 9 Previous study (reference 12)

Number of freeze thaw cycles/yr GPS 1 to 9 Previous study (reference 12)

Freeze index GPS 1 to 9 Previous study (reference 12)
Traffic Data

Historical Equivalent Single Axle Load (ESAL) data GPS I to 9 TRF EST ANL TOT GPS_LN

Growth rate for KESAL (1ooo's of ESAL) GPS I to 9 Computed

Average KESAL per year from historical data GPS I to 9 Computed

Initial KESAL for section GPS I to 9 Computed

Estimated truck factor GPS I to 9 Computed

Estimated cumulative traffic GPS I to 9 Computed

PeC Test Data

PCC compressive strength GPS 3,4,5,7,9 TST PCOI

PCC Poissons ratio GPS 3, 4, 5, 7, 9 TST PC04

PCC unit weight GPS 3,4,5,7,9 TST PC05

PCC elastic modulus GPS3,4,5,7,9 TST PC04

PCC split tensile strength GPS 3,4,5,7,9 TST_PC02

Shoulder Data

Total width of outside shoulder GPS I to 9 INV SHOULDER

Paved width of outside shoulder GPS I to 9 INV SHOULDER

Thickness of outside shoulder GPS I to 9 INV_SHOULDER

Thickness of shoulder base GPS I to 9 INV SHOULDER

Drainage

Subdrainge information GPS I to 9 INV GENERAL

LTPP code for drain type GPS I to 9 INV GENERAL

LTPP code for drain location GPS I to 9 INV GENERAL

PeC Mix Data

Water to cement ratio GPS 3, 4, 5 INV PCC MIXTURE

Air content GPS 3,4,5 INV PCC MIXTURE

Weight of coarse aggregate in mix GPS 3, 4, 5 INV PCC MIXTURE

Weight of fine aggregate in mix GPS 3, 4, 5 INV_PCC MIXTURE

Weight of cement in mix GPS 3,4,5 INV PCC MIXTURE

Weight of water in mix' GPS 3, 4, 5 INV PCC MIXTURE

PeC Steel Information

Percent longitudinal steel by area GPS4,5 INV PCC STEEL

PeC Joint Information

Average contraction joint spacing, ft GPS 3, 4 INV_PCC JOINT

LTPP code for type of joint GPS 3,4 INV PCC' JOINT
courses. The number of layers for the pavement structure for a GPS section can vary between

two and six layers. For cases where the number oflayers is six, the data are contained in six

rows in the data file. Macro programs were used in the spreadsheet to reduce all these data to

one row of information for each section. The structural number of AC-surfaced sections is not

available in a data table in the NIMS, so the estimated structural number was computed from

the layer thickness and by assigning structural coefficients to each layer on the basis of layer

type. The overburden pressure on the top of subgrade was also computed on the basis of layer

thickness and estimated density of the layers.

WIM scales and Automatic Vehicle Classification systems have been installed at

numerous GPS sections. At some GPS sections, portable equipment is used to collect data. The

collected data are uploaded to the Central Traffic Database. The Monitored Traffic Data Status

Report. published in October 20, 1995, provides a summary of monitored traffic data available at

GPS sections.(14) A limited amount of annual Equivalent Single Axle Loads (ESALs) estimated

from the monitored traffic data is available in the NIMS. This data table was reviewed to

determine data availability. The number of days for which WIM data has been collected at a

section ranged from 2 to 365, and the estimated annual ESALs by year have been computed from

the available WIM data.

A review of the monitored traffic data indicated that at least 1 year of estimated ESALs

was available at the following percentage of sections for GPS experiments 1 through 5: GPS-l,

43 percent; GPS-2, 37 percent; GPS-3, 43 percent; GPS-4, 76 percent; and GPS-5, 40 percent. A

review of the data indicated that, for a specific section, the annual ESAL estimates vary

significantly from 1 year to the next. For example, at GPS section 364018, the estimated annual

Kilo Equivalent Axle Loads (KESALs) from WIM data was 249 in 1992, while the estimate for

1993 was 9. At GPS section 531006, the estimated KESALs from WIM data for 1992 was 34,

while the estimate for 1993 was 72. The number of days ofWIM data used to obtain the

KESALs for the 2 years for both cases were different. One explanation for the variability seen

in the ESAL data at a particular section is the varying number of days for which WIM data were

collected from one year to the next. Because ofthe large variations in estimated ESALs that

were noted at many sections, an accurate ESAL estimate could not be obtained for a GPS

section from the WIM data. Therefore, it was decided to use historical traffic data that are

available for the sections to estimate cumulative traffic loading applied at each section. The

historical traffic data contain both truck counts and estimated ESAL counts for each year of
data. On the basis of these parameters, an estimate of truck factors (number of ESALs per truck)

can also be computed from these data.

The cumulative traffic at any given profile date was desired for the analysis. The number

of years for which historical traffic data exists is also variable. For some sections, historical

traffic data began with the construction date, while for other sections, the historical traffic data

were sporadic or did not begin at the date of construction. There were some sections for which

traffic data were available for only 1 year. Figure 4.3.1 presents the traffic trends for several

GPS-3 sections.

2500

Reported KESAL/yr: Selected GPS-3 Sections

~6-300S -e-6-3030

13-3019 -*-13-3024

2000 _6-3024 -+-8-7776

-+-12-3804 -12-3811

S

1500

~

r:1 1000

500

)~

~

)( )( )( )( )( )(

0

1975 1978 1981 1984 1987 1990

Yr Estimated

Three approaches were used to compute cumulative traffic on the basis of the number of

years of available traffic data. For sections that had 4 or more years of traffic data, cumulative

traffic was computed based on an exponential growth type model, while for sections that had less

than 4 years of traffic data, a linear projection that was based on a growth slope was used. For

sections with only 1 year of traffic information, traffic was assumed to be constant at that rate

over the life of the section. A brief description of the models that were used to compute

cumulative traffic is presented next.
Exponential Growth: Ifthe traffic growth rate is known along with the initial traffic rate, then

the cumulative traffic at any point in time is defined by:

CumTraff = KESAL (1+i)n - 1

o In(l + i)

the sum of the historical KESAL,

the first KESAL/yr noted in the historical data,

= annual growth rate for KESAL/yr, and

= number of years between first and last traffic date.

CumTraff =

KESALo

0= - CumTraff (In(l + i» + «1 + i)n -1)

KESALo

A macro was developed in a spreadsheet to iterate and obtain a solution for the growth

rate for the above equation for each GPS section that had more than 4 years of traffic data. On

the basis of the growth rate, the cumulative ESALs corresponding to each profile date were

computed. Figure 4.3.2 graphically presents the approach used in this model.

300 4 +Yr of Traffic Data

••• Estirmte initialKESAL

~

200 ani growth rate, i. ~

-<

rI.l

100

~

• •

0

1973 1979 1984 1990 1995

Figure 4.3.2. Graphical representation of traffic analysis scheme used for sections with more

than 4 years of traffic data.

Linear Slope Type Growth: Traffic generally exhibits exponential growth over time.

However, ifthe KESAL/yr were to increase linearly over time at a section, the cumulative

traffic at any time can be defined by:
t

CumTrajJ = J(KESALo + bt)dt

o

where:

CumTraff = cumulative traffic (KESAL),

KESALo = the first KESAL/yr noted in the historical data,

b = linear traffic growth rate, and

t = time in yr between first and last traffic date.

b = 2[CumTraff - KESALot]

t

2

An algorithm was developed to find the slope (b), which satisfies this relationship for each

GPS section that had less than 4 years of traffic data. Figure 4.3.3 graphically presents the

approach used in this model.

•

Estimate KESAL slope, b (b =0 for 1 point only)

150

1986

Figure 4.3.3. Graphical representation of traffic analysis scheme used for sections with less

than 4 years of traffic data.

Most sections had data over only a portion of the pavement's life, and the traffic data

for the initial years or the later years of the project were not available. In such cases, the above

equations were used to estimate the traffic at the section immediately after construction by

backcasti'ng the growth rate. T~e cumulative traffic at each profile date was then computed on

the basis of the initial traffic volume and the growth rate.
After the databases for each GPS experiment were assembled, the availability of each

data element was evaluated. Generally, laboratory test data for GPS experiments 1 through 5

were available for the data elements that were considered in the analysis, except for resilient

modulus test data, where data was limited. Some inventory data were missing for GPS

experiments 1 through 5. An example is the load transfer type (doweled or aggregate) for the

GPS-3 experiment, where the data were missing for 24 percent of the test sections. Laboratory

test data for GPS experiments 6 through 9 were less complete compared with those of GPS

experiments 1 through 5.

In the LTPP program, the GPS test sections were established on in-service pavements of

varying ages. The initial IRI of these test sections were not available in the LTPP database. The

researchers who performed an earlier analysis on roughness development(12) estimated the initial

PSI of the test sections with the use of data from various sources. These initial PSI values were

obtained and reviewed, and the review indicated that initial PSI estimates for 541 sections were

available. The researchers(12) had used one of the following procedures to estimate the PSI of

the sections: (1) estimate PSI on the basis of roughness measurement obtained at the test section

soon after construction (148 sections); (2) extrapolate a roughness measurement made several

years after construction to estimate the initial roughness and then estimate PSI (8 sections); (3)

use multiple roughness measurements made over time and extrapolate back to estimate the initial

roughness and estimate PSI from this value (122 sections); (4) tap the memory of an individual

closely associated with the project during construction to get an estimate of PSI (18 sections);

and (5) use general experience in initial PSI from research studies (248 sections). For cases

where the PSI was estimated from roughness measurements, the data appeared to be from Mays

meter measurements, and the roughness measurement was most likely to be for a segment length

that included the 152-m- (500-ft-) long GPS section. Figure 4.5.1 presents the frequency

distribution of the estimated PSI values. A review of the data indicated obvious biases. For

example, Colorado had 16 GPS sections (comprising GPS-l, -2, -3, -6A, -7A, and-9

experiments) and all had an estimated PSI of 4.20. Arkansas had 14 GPS sections, where all the

GPS-l, -2, and -7 experiments had an initial PSI of 4.20 while the 7 GPS-2 experiments had a

PSI of 4.70. Because of these biases it was decided not to use these PSI data to estimate the

initial IRI of the test sections.
180

160

140

a- 120

i 100

:J

~ 80

II. 60

40

20

o

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

_Frequency

_Cumulative %

3 U U M M 4 ~ « U U 5

PSI Value

Once the analysis databases containing the data elements that were to be analyzed were

assembled, statistical procedures such as univariate analysis and bivarate analysis were performed

on the data. The univariate analysis consisted of an investigation of the distribution of each data

element that was selected for analysis, and was carried out separately for each GPS experiment.

Histograms were developed to study the frequency distribution of the data elements. The

histograms indicated that some biases existed for some of the data elements that were analyzed.

For example, the study indicated that the distribution of the test sections for the GPS experiments

have some bias related to the States. For example, 39 percent of the GPS-1 test sections are

located in four states (Florida and North Carolina - 6 percent each, Arizona - 8 percent, and Texas -

19 percent). For the GPS-4 experiment, 37 percent of the test sections are located in three states

(Missouri - 15 percent, Minnesota - 12 percent, and Arkansas - 10 percent).

Bivariate analysis consisted of computing the Pearson's correlation coefficient between

the data elements selected for analysis and plotting two-way scatter plots. This correlation

coefficient has a value between 1 and -1, with values approaching 1 indicating a strong positive

correlation, values near zero indicating no correlation, and values approaching -1 indicating an

inverse relation between parameters. As it is possible to have a strong correlation between two

variables simply because of biases in the data distribution and influential observations, analyses

of scatter plots were used to obtain insight into the relationship between two parameters to

identify the true trends. For each GPS experiment, correlation coefficients were determined for
geographic regions, environmental regions, soil type, and many other parameters. The results of

these analyses are described in chapter 5 of this report.
CHAPTER 5: MODELS TO PREDICT ROUGHNESS DEVELOPMENT AT GPS

SECTIONS

The average IRI was used for all analyses described in this chapter. The average IRI is

the mean of the IRI of the left and the right wheel paths. When the IRI ofa section is referred to

in this chapter, the average IRI is implied.

The first step in investigating general trends in IRI was to study plots of IRI versus

pavement age for each GPS experiment. The general trends in IRI development that were noted

for each GPS experiment are presented in this section.

Figure 5.1.1 shows the general trends for IRI development for the GPS-1 through GPS-5

pavement types. It can be seen that each pavement type has different roughness characteristics

and rate of change. For example, the GPS-1 sections (flexible pavements on granular bases)

generally show a higher rate of change of IRI than the GPS-5 sections, which consist of

continuously reinforced concrete pavements (CRCP). Also, the GPS-3 sections, which are

jointed plain concrete pavements (JPCP), exhibit only slight increases in IRI, but have much

more variation in IRI over time. Figure 5.1.2 shows the general behavior oflRI for overlaid

pavements (GPS-6, GPS-7, and GPS-9). The following four general behavioral patterns were

observed in figures 5.1.1 and 5.1.2 for the GPS sections: (1) the IRI remained relatively stable

over time, (2) IRI values were variable between the years with no clear overall trend for the

section, (3) the IRI increased with time, and (4) pavements with an IRI in excess of2.37 mIkm

(150 in/mi) exhibited larger increases in IRI over time.

A linear regression analysis was performed between the time sequence IRI values and the

pavement age at each GPS section to identify the sections for which a linear relationship exists

between these two parameters. All GPS sections that had 3 or more years ofIRI data were

subjected to the analysis. In the linear regression analysis, the IRI was the dependent variable,

while the pavement age was the independent variable. The following statistical test was

performed on the slope (B 1) obtained from the regression to see if a positive linear relationship

exists between the IRI and the pavement age:
250

•• 200

El :e ISO

...:'100

=' - 50

o

o

250

•• 200

j 150

...:'100

=' - 50

o

o

250

~~:

...:'100

=' - 50

o

o

•• 200

Ell50 :s

--100 ='

- 50

o

o

250

•• 200

El1S0 :s

...:'100

='

- 50

o

o

IS Age, yr

IS Age, yr

15 Age, yr

--==--:::::::- -

- ::::=--- ---

Figure 5.1.1 Trends in IRI development for GPS-l through GPS-S sections

(each line segment is one LTPP section)
250

200

J

no

.

- ---

g -- ..

100

SO
Ho:BI=O

Ha: BI> 0

It was noted that some sections exhibited a reduction in IRI with increase in pavement

age. Another statistical test was performed at the GPS sections to determine if a negative linear

relationship existed between the IRI and the pavement age. The following statistical test was

performed on the slope (B1) to examine if this condition existed:

Ho:BI=O

Ha:BI<O

The percentage of sections for which a positive or negative linear relationship existed

between the IRI and the pavement age classified according to the GPS experiment are presented

in table 5.1.1. The following information obtained from the linear regression analysis is

presented for each GPS section in appendix B: the intercept, the slope, the coefficient of

determination (R2), and if the t-test determined a linear relationship existed between IRI and

pavement age. The time-sequence IRI data that are available at each site are also presented in

appendix B. For nonoverlaid pavements, a higher percentage of AC pavements showed a

statistically significant relationship between IRI and pavement age compared with the PCC

pavements. A higher percentage of GPS-6 sections showed a statistically significant relationship

between IRI and pavement age when compared with GPS-7 sections.

The frequency distribution of GPS sections according to the slope obtained from the linear

regression analysis is presented in table 5.1.2. The slope from regression represents the rate of

increase oflRI at the section. The percentage distribution ofGPS sections according to the range

of slopes is presented in table 5.1.3. It should be noted that the R

2

associated with these slopes

ranged from zero to one. Therefore, the slopes that correspond to low R

2

values may not

represent the actual rate of increase of IRI at the section. Table 5.1.4 presents the percentage

distribution ofGPS sections according to the range ofR2values obtained from the linear

regression. As seen from this table, for each GPS experiment a wide distribution ofR2existed.
Table 5.1.1. Percentage of GPS sections showing statistically significant linear relationship

between IRI and pavement age.

Table 5.1.2. Distribution of sections according to rate of increase ofIRI obtained from

linear regression.
Table 5.1.3. Percentage distribution of sections according to rate of change ofIRI obtained from

linear regression.

Table 5.104. Percentage distribution of sections classified according to coefficient of

determination (R2) obtained from linear regression.

Range of Percentage at ~ectlons (Ufo)

Coefficient of GPS Experiment

Determination , :l 3 4 5 6A 66 7A 76 g

<0 3 2 8 7 5 2 4 3 0 4

0-0.25 20 20 32 39 36 18 26 27 25 33

0.25 - 0.50 16 16 26 16 30 18 26 13 13 21

0.50 - 0.75 20 19 19 14 19 14 11 30 19 17

0.75 -1.00 41 44 16 23 10 49 33 27 44 25

, TOTAL 100 100 100 100 100 100 100 100 100 100

Table 5.1.3 shows that, for each GPS experiment, a large number of sections had either a

negative rate for the increase ofIRI or a low rate that was between zero and 0.03 m/km/yr (2

in/mi/yr). The available IRI data indicated that the GPS sections have been profiled an average of

four times. 'A rate of increase ofIRI of 0.03 m/km/yr (2 in/mi/yr) corresponds to a change of 0.13

mIkm (8 in/mi). This change represents a small value when considering the changes in IRI that

can occur between the years due to environmental effects and variability in the profiled path. For

nonoverlaid PCC pavements (GPS-3 to GPS-5), the percentage of sections that had a rate of

increase ofIRI less than 0.03 m/km/yr (2 in/mile/yr) or a negative rate were: GPS-3: 72 percent,

GPS-4: 73 percent, and GPS-5: 91 percent. For nonoverlaid AC pavements (GPS-1 and GPS-2

sections), the percentage of sections exhibiting a similar behavior were 48 percent and 55 percent,

respectively. GPS-6B and GPS-7B are overlaid pavements, and virtually all ofthese are less than
5 years old; 71 percent of GPS-6B and 81 percent of GPS-7B sections exhibited a growth rate that

was negative or less than 0.03 m/km (2 in/mi/yr).

In this study, for GPS experiments 1 through 4, considerable effort was placed on

developing models that back predict the initial roughness on the basis of time-sequence IRI data

and also predict the actual IRI value at any point in time. In the modeling effort, the sections that

had relatively stable IRI values as well as sections that show an increase in IRI were considered.

A growth rate parameter was also modeled such that the key parameters affecting both the initial

IRI and growth of the IRI (including zero growth) could be determined. For the GPS-5 sections

that showed virtually no increase in IRI, multiple linear regression was used to develop a model.

For the overlaid sections, a rate of increase of 00 was determined by linear regression, and

attempts were made to relate this parameter to factors that influence the increase in 00. These

concepts are discussed in greater detail in the following sections. All GPS sections that had a

complete data set of the parameters that were evaluated for each model were included in the

model development.

The first step in model building consisted of observing the plots of 00 development over

time. The observed shape ofthe development curves gives an indication of how the growth rate

of the IRI changes over time. The IRI trends observed in the data were generally similar to

exponential growth for GPS-1 and GPS-2 sections, while the GPS-3 and GPS-4 sections

generally showed a logistic growth pattern.

Figure 5.1.3 shows examples of logistic and exponential growth rates and the resulting

growth curves. In this figure, case 2 shows a constant growth rate while case 4 shows a growth

rate that increases with time. For modeling ofGPS-1, GPS-2, and GPS-4 sections, the growth

rates that were used corresponded to case 4. The growth rate was characterized as follows:

tAl r(t)= ro( IB)

where:

r(t) = growth rate value at any time t,
'0 = growth rate constant,

A = constant between 0 and 2,

B = constant, and

t = time.

350

300

250

E 200

! 150

100

50

o

o

Development Trends

-- Case l(Logiltic, Rigid Pvt)

- - - - Case 2(Expone1Uia1)

o 0 0 • 0 • 0 Case 3(Logiltic-like)

- o. _. Case 4(Expone1Uial-Like. Flexible Pvt.)

15 20

Pavement Age, yr

0.18

0.16

~~ 0.14 i 0.12

i 0.1

o 0.08

~ 0.06 _._0_

j ~:~r--

o

o

Growth Rates Corresponding to Above Trends

__ Case 1(Decreasing)

____ Case 2(ColIStant)

.00 •• 00 Case 3(Pu1se)

_ 0 • _. Case 4(1n:reasing)

15 20

Pavement Age, yr

1 in/mi = 0.0158 m/km

Figure 5.1.3. Plots showing how different growth rate functions affect the development of an

arbitrary distress type.

Logistic-like growth is characterized by a decreasing or single-pulse growth rate as a

function of time that results in a somewhat stable value after some time period as the growth rate

approaches zero. GPS-3 pavements were modeled using a logistic growth approach.
In reality, the growth rates in pavements are likely to oscillate seasonally, and are perhaps

discontinuous in time, changing as the pavement condition changes and having a pulse-like

increase during the passing of each load. A logistic trend appears similar to an exponential trend

in early stages, but is characterized by a change in sign of the curvature in the resulting trend at

later stages as the growth rate approaches zero. It is believed that all pavements would

eventually exhibit logistic growth of IRI if measured for very long periods of time (i.e., IRI

would not go to infinity but would eventually stabilize at some very high value) provided that the

pavement system was not eroded away into an entirely impassable state.

One of the most challenging problems in modeling was the fact that the initial 00 values

for the GPS sections were unknown. The modeling approach used in this study is shown

graphically in figure 5.1.4. The initial 00 is typically back predicted as a nonlinear function of

the structural properties of the pavement and subgrade properties. The growth rate is modeled as

a nonlinear function of time, structural properties of the pavement, sub grade properties, climate

parameters, 'and traffic loading. The results of the analyses indicate that initial 00 can generally

be back predicted from the data within the LTPP IMS, yet most error in the models appear to be

associated with back predicting initial 00. This is likely associated with differences in

construction practices, density specifications, and local sub grade variability and density.

's 150

:e

18100

1 in/mi = 0.0158 m/km

Figure 5.1.4. General description of the modeling technique used for 00 prediction.

Correlations were determined for LTPP geographic regions, environmental regions, soil

type, and many other parameters. The strongest correlations were typically observed when the

data were sorted by environmental regions (figure 5.1.5) and subsorted by soil type for the wet

regions. With the exception of the first model presented, only the 10 parameters that had the

highest correlation to 00 for each data set are shown in this report. The first model presented
~

Wd - No PIeezo

IIWd-Precz.e

IIDry - No PRczo

D Dry -PRczo

Figure 5.1.5. General distribution of the four climate classifications within the continental United States.
(GPS-1 dry freeze) shows an example of a correlation matrix and scatter plots of variables used

in the modeling process.

After reviewing the correlations and scatter plots between the 00 and the parameters

considered in the analysis, a model form for each data set was established using the parameters

that showed the strongest correlation to IRI. For nonlinear modeling, a nonlinear optimization

routine available in a commercial solver was used to vary the algebraic coefficients in the models

for each data set to minimize the errors between measured and predicted 00.

The modeling utilized a nonlinear optimization methodology developed by Leon Lasdon

of the University of Texas at Austin, and Allen Waren of Cleveland State University.(15) This

methodology is incorporated into what is referred to as the Generalized Reduced Gradient

Algorithm (GRG2), which is used in the commercial solver. The solver varies the model

coefficients slightly and monitors the partial derivatives of the dependent variables and the

constraint variables with respect to the causative variables. The coefficients are varied until the

partial derivatives are near zero, indicating a local minimum or optimal solution while

maintaining constraint or boundary conditions.

1. Range of Predicted Initial 00 = Estimated Range of Initial 00: This constraint was used to

hold the predicted initial 00 values within a reasonable range.

2. 00 Prediction Standard Deviation = IRI Measurement Standard Deviation: This constraint

was used so that the standard deviation of the predicted 00 would fall within the standard

deviation of 00 that was observed in the data set.

3. Range of Predicted 00 =Range of Estimated IRI: This constraint was used to so that the

predicted IRI values would fall within a reasonable range of IRI values.

4. 00 Growth Rate ~ 0: This constraint was used so that the 00 would not decrease over time.

5. Traffic Effects on 00 Growth Rate ~ 0: Traffic had to be constrained as shown because of

the tendency for thick, high traffic routes to have lower initial roughness and to develop

roughness more slowly over time than thin, low traffic sections. In flexible pavement

modeling, the ratio of traffic raised to some power divided by structural number to some

power was used extensively in the growth rate constant estimate that generally produced

good results and tended to result in positive trends for traffic effects.
The nonlinear modeling requires an initial estimate based on judgment for the

coefficients. The optimization algorithm systematically varies the coefficients, minimizing the

sum of errors between measured and predicted IRI. The results are sensitive to the initial

estimate used for the coefficients.

In the optimization process, different strategies were used in the computation of errors

that were used in the minimization process. The following methods were used to compute errors.

1. Sum of the absolute values ofthe average error per site: For each site, at each time-sequence

IRI data point, the difference between the predicted and measured IRI values was computed.

The absolute value of the differences were then averaged for each site. Then the average

values at each site were summed.

2. Sum of the squared values ofthe average error per site: For each site, at each time-sequence

IRI data point, the difference between the predicted and measured IRI values was computed.

The squared values of the differences were summed and then averaged for each site. Then

the average values at each site were summed.

3. Sum of the absolute values of errors: For each time-sequence IRI data point, the error was

computed as the difference between the measured and the predicted IRI value. The absolute

value of the error at each data point was then summed (for all time-sequence IRI data points

for all sections).

4. Sum of squares of errors: For each time-sequence IRI value, the error was computed as the

difference between the measured and the predicted IRI value. The errors were then squared

and then summed (for all time-sequence IRI data points for all sections).

The initial iterations of the optimization were generally set up to minimize the sum of

squares of errors. Some or all of the strategies that were described were then used in iterations

until convergence. After a given solution converged, based on the initial estimate of the

coefficients, it was reviewed to see if it gave realistic results and conformed with common sense.

If not, the coefficients would be varied and the optimization run again. Using the average error

per GPS site has the effect of weighting each site equally whether there was one IRI value or six

IRI time-sequence values for the site. Using the error associated with each data point would

weigh each measurement equally, weighting sites with more data more heavily in the resulting

model.
When each model is described, the error for each model is defined as the average of the

average errors per site for the sites used in the model. The average error for a site was computed

as presented below:

Average error per site = Ej= .!.:t Abs (Predicted IRIj- Measured IRIj)

n i=l

where: n ~ number of time-sequence IRI measurements at a site.

A

£". d I _1~ E

J

'. verage error lor mo e = LJ

mj=l

where: m = number of GPS sites used in model.

For each model developed for GPS-l through GPS-4 experiments, the standard deviation

of the errors are also presented. The standard deviation of the error for a model provides an

indication on the dispersion of the errors.

It should be noted that the models are based on a data set that on average had four timesequence IRI values that were obtained over a 4-year period. The models reflect the behavior

pattern observed for the analyzed data set, and could reflect biases that were present in the data

set. For the GPS sections used in the analysis, the applicability of these models can be evaluated

as more time-sequence data become available. These models can be 'used to gain an insight into

factors that affect the IRI development. The models are sensitive to input parameters, and care

should be taken to use input parameters that are neither unrealistic nor beyond the range of data

from which the model was developed.

The modeling of IRI for the GPS-5 pavements was performed by using a step-wise

multiple regression analysis that related the IRI of the pavement to a set of independent variables

that could possibly affect the growth of IRI.

For the overlaid pavements, a linear regression analysis was performed between the timesequence IRI data and the pavement age to obtain the rate of increase of IRI, which is the slope

of the regression equation. Correlation coefficients between the slope and parameters that could
affect the increase in IRI were computed to identify parameters that have strong correlations.

Multiple regression analysis was then performed between the slope and these parameters to

develop a relationship to predict the slope.

In sections 5.2 through 5.5, models for predicting IRI for GPS experiments 1 through 4

are presented. The resulting models are based on fitting exponential relations to parameters

within the models. Some of the resulting exponents are large, and the models are sensitive to

some parameters that are at extremes or are out of the range of data used to establish the models.

For example, if the percentage passing the 75-~m (No. 200) sieve for subgrade (P200) value of

90 is placed into a model that was developed for subgrade having a P200 content less than 50,

unrealistic predictions may result. Each ofthe models presented in sections 5.2 through 5.5 is

followed by a figure showing the sensitivity of the model to various parameters. This figure can

be used as a check when programming the model algorithm. These figures also show the range

of each parameter encountered in the database. Table 5.1.5 provides. the definitions of the

parameters used in model building and used in the discussions presented in the following

sections.

Climatic factors were found to have a strong influence on roughness development.

Separate models were often developed for different climates for a given pavement type. Figure

5.1.5 shows the area included in each of the LTPP climatic zones. For the modeling conducted

in this study, the boundary between wet and dry regions was taken as 508 mm (20 in) of annual

precipitation, and the boundary between freezing and nonfreezing zones was taken as an annual

freeze index of 89°C days (160°F days).

The following discussions present the terms used to describe PCC strength and elastic

modulus. Concrete data were compiled for all GPS-3, GPS-4, and GPS-5 sections such that the

average PCC properties could be determined. The average PCC properties for the entire GPS

concrete pavement data (GPS-3 through GPS-5) are as follows:

1. Average split tensile strength = 4.44 MPa (643 psi)

2. Average elastic modulus = 31573 MPa (4570 ksi)

3. Average water to cement ratio = 0.48

4. Average compressive strength = 52.4 MPa (7584 psi)

5. Average unit weight = 87 kg/m3(147 pct)
Parameter Definition

%ACinSN Percent of SN contributed by AC thickness

%Sand P#4-P200, %

%Steel Percent longitudinal steel by area, %

ACThick AC thickness from core data, in

Age Age of pavement, yr

air PCC air content, %

AnnPrecip Annual precipitation, in

BaseThick Base thickness, in

c Weight of coarse aggregate in PCC, Ib/yd3

elf Weight ratio of coarse to fine aggregate in PCC mix

cement Weight of cement in mix, Ib/yd3

Cum Traf Estimated cumulative traffic, KESAL

Comp PCC compressive strength, psi

Days32- Number of days below 32°F per yr

Days90+ Number of days above 90°F per yr

Days.5"+ Number of days with >0.5" precipitation per yr

DaysWetlyr Number of days with precipitation per yr

EMOD PCC elastic modulus, psi

f Weight of fine aggregate in PCC, Ib/yd3

FrzThwCyc Number of freeze-thaw cycles per yr

FZI Freeze Index, OFdays per yr

Initial KESAUyr Estimated initial traffic level

JointSpace Joint spacing, ft

KESAUyr Average KESAL per yr from historical;data

LI Subgrade liquidity index

LL Subgrade liquid limit, %

P200 Subgrade percent passing No. 200 sieve

P#4 SUbgrade percent passing No.4 sieve

PCCth PCC thickness, in

PI Subgrade plasticity index, w%

PL Subgrade plastic limit, w%

Po Subgrade overburden pressure, psf

Poissons Ratio PCC Poissons ratio

SN Estimated Structural Number

SplitTensile PCC split tensile strength, psi

t Age of pavement at time of profile measurement

unitwt PCC unit weight. Ib/ft3

w% Subgrade moisture content, %

w/c Water to cement ratio for PCC mix

water Weight of water in PCC, Ib/yd3
The split tensile strength and the elastic modulus ofPCC can be related to the compressive

strength (fe' ) ofPCC by the following American Concrete Institute (ACI) relations:

SplitTensile = 7.4.[l

ElasticModulus = 57000.[l

where:

SplitTensile

ElasticModulus

fe'

= split tensile strength ofPCC in psi,

= elastic modulus of PCC in psi,

= compressive strength ofPCC in psi.

The average values presented previously show good agreement with these two formulas.

On the basis of the above two formulas, the ratio between the split tensile strength and the elastic

modulus can be computed as shown below, where 10,000 is a scaling factor:

SplitTensile(lO,OOO) = 7.4(10,000) = 13

ElasticModulus 57,000

In the following discussions, a PCC that is referred to as higher strength-lower modulus is

a PCC that has a ratio greater than 1.3 as computed from the above formula. For example, a PCC

mixture that has higher split tensile strength with a low modulus is described qualitatively as a

higher strength lower modulus mixture. The terms higher and lower are used in a relative sense.

A mix with a low ratio is a stiff concrete that fails or cracks at low stress levels (more brittle

behavior). A mix with a high ratio can experience more stress before failure or cracking (less

brittle behavior). The above ratio at the various sites varies from 0.9 (E = 40590 MPa (5875 ksi),

split tensile = 3.7 MPa (538 psi» to 3.0 (E = 20900 MPa (3025 ksi), split tensile = 6.32 MPa

(915 psi», with an average of 1.43.

The time-sequence IRI values for each GPS-l site were compiled and plotted against

pavement age. Figure 5.2.1 shows the development ofIRI for the GPS-l sections classified

according to LTPP regions. Trends in IRI observed in the different environmental regions and for
250

200

·s 150 :5

gloo

50

o

o

250

200

~ 150

gl00

50

o

o

250

200

·s 150 :5

gl00

50

200

~ 150.

gloo

50

~ .--

~~gr&~~

o

o

o

o

Figure 5.2.1 IRI development over time for GPS-l sections classified

according to LTPP regions
different soil types are discussed later in the sections of the report that describe the models.

Many of the GPS-l sections show little change in 00 over time. The GPS-l sections that do

show an increase in IRI generally appear to have exponential growth of the 00. A model based

on exponential growth of the 00 can capture the general curve shapes observed. The model

form that was used for flexible pavements is described below and shown graphically in figure

5.2.2:

r.tA/

IRI(t) = IRIoe 0 /B

where:

fRIo =

=

70 =

=

t =

e =

A andB =

estimated initial 00 (after some initial traffic loading),

f(the pavement structure and subgrade properties),

growth rate constant,

f(climate, traffic, subgrade, layer properties of the pavement),

time in years,

the exponential function, and

modeling constants.

Description of Modeling Technique

o

o 10 15

Pavement Age, yr

1 in/mi = 0.0158 m/km

Figure 5.2.2. Modeling technique used for GPS-l sections.

In this model, fRIo represents the 00 value of the pavement after it has been opened to

traffic and has been subjected to some initial traffic loading. In the initial phases of model

building, the GPS-l data were sorted in many ways, and correlation coefficients and scatter plots

were generated and reviewed. The strongest correlations were observed when the data sets were

divided ir~toenvironmental regions and then again by the subgrade percent by weight passing the
75-J.1m(No. 200) sieve (P200) for the wet regions. The wet-freeze and wet-no freeze

environments were split into three soil subgroups: (l) P200 > 50 percent, (2) P200 between 20

and 50 percent, and (3) P200 < 20 percent. These divisions appeared to divide the data on the

basis of trends observed and resulted in relatively equal sizes of the data sets. Separate models

were then developed for each data set. Scatter plots and statistical correlation matrices were used

extensively during the model-building process. However, they are only presented for the first

model discussed so that the methodology used during model building is understood. Table 5.2.1

shows the 10 parameters that had the highest Pearson correlation coefficients to IRI for the data

sets used in GPS-l model building.

In the following sections, each model is described along with a discussion on which types

of pavements were showing high and low IRI values within each data set. The best and worst

performers in each data set were roughly evaluated using the AASHTO pavement design

equation (without considering serviceability loss due to climatic factors). This analysis provides

an indication of what level of performance would be expected at the various sites that can be

compared with the observed IRI values. By comparing the AASHTO-based anticipated life with

the actual IRI behavior, sections that were performing well or sections that were failing

prematurely could be better identified. In this evaluation, the cumulative traffic volume

estimates at each section and an estimate of the sub grade modulus at each section (on the basis of

laboratory test data for the subgrade) were used to obtain the structural number (SN) that is

required for that section. This SN was then compared with the SN estimated for the test section

from the pavement layer thickness. If any conclusions could be drawn from this analysis, they

are indicated in the discussions.

The dry freeze environment is defined for this study as a region that has less than 508 mm

(20 in) of annual precipitation and a freezing index greater than 89°C days (160 OFdays). The

pavements in this region are very sensitive to the amount of precipitation, freezing effects, and

sub grade parameters. Higher fines content (P200) in the subgrade, coupled with higher

precipitation and freezing, result in higher IRI. The effect of traffic could only be detected for

very small SN values.

Figure 5.2.3 shows the model form developed for IRI prediction for the dry freeze

region. This model was based on 124 IRI measurements from 26 sections and had an average
Wet-Freeze Dry-Freeze Dry-No Freeze

P200<20% 20% < P200 < SO% P200>SO% All Soils All Soils

P200(+.30) %Sand(-.50) PI(+.44) AnnPrecip( + .48) SN(-.31)

w%(+.28) Po(-.45) DaysWet/yr(-.42) Age(+.47) ACThick(-.27)

Age(+.24) FrzThwCyc( + .42) LL(+.38) DaysWet/yr( + .44) Age(+.19)

Days.5" +( + .21) SN(-.39) FZI(+.31) Days.5" +( + .35) Days90+(+.14)

FrzThwCyc( -.19) AnnPrecip( -.35) w%(+.29) P200(+.33) KESAL/yr(-.14)

Po(+.19) P200(+.34) ACThick( + .26) Days32-(+.31) Cum Traf(-.13)

,

DaysWet/yr(-.18) Days.5" +(-.33) Age(+.25) FZI(+.28) FZI(+.ll)

FZI(+.16) KESALlyr( -.31) PL(+.25) w%(+.26) Po(-.09)

AnnPrecip( + .14) Truck Factor( -.23) P200(+.23) Cum Traf( + .24) Days32-( + .08)

SN(+.ll) Age(+.23) AnnPrecip(-.22) %Sand(-.24) P#4(+.08)

Wet-No Freeze

P200<20% 20% < P200 < SO% P200>SO%

Days90+(+.59) DaysWet/yr(-.40) AnnPrecip(-.53)

ACThick( -.59) KESALlyr(-.30) Days.5" +(-.52)

SN(-.34) Po(-.27) Days90+(+.37)

w%(-.51) SN(-.25) Age(+.35)

AnnPrecip( -.46) w%(+.24) Po(+.32)

Days32-(-.39) Days90 +( + .22) PI(+.31)

%Sand(+ .22) FZI(-.21) P200(+.31)

Age(+.21) ACThick(-.16) P#4(+.28)

P200(-.18) P200(-.14) LL(+.27)

KESAL/yr( + .15) AnnPrecip(-.13) FrzThwCyc(-.24)

error per section of 0.19 m/km (12.1 in/mi); the standard deviation of errors at the sections was

0.10 m/km (6.6 in/mi). Also included in the figure is a plot of measured versus predicted IRI and

a plot ofIRI versus pavement age for the measured and predicted IRI values from the data set.

The measured and predicted values for a section can be identified by drawing a vertical line from

the age axis through a white square, and then connecting it to a black triangle on the graph.

Figures 5.2.4 and 5.2.5 show the correlation matrices and scatter plots, respectively, for

some of the key parameters related to GPS-l dry freeze IRI development. There are no strong

correlations with any ofthe variables, and several variables are correlated with each other. Many

multi-variable clusters were calculated and reviewed in an attempt to find strong prediction

variables to use in models. For example, one of the clusters used for freezing environments was:
's ISO

l

;100

-

I(KESALI yr/ 0/.1 P '.IP Q(FZI· P200 .W%)R

rO = L + I•..•nn reclp/ +

K~N) Po

A= 250.1471 F= 5.5503 K= 300 R= 1.5855

B= -0.0287 G= 203.8493 L=4 S= 1.(952

C= -517.4179 H= 0.05983 0= O.<XXX> T= 0.1879

D= -0.06407 1= 0.005 p= 0.67~

E= -2847:&10 J= 1.7 Q= 6.84:&00

's:91SO

;

-

100 ISO

MelL'lured IRI, inImi

81·1804 • Predicted IRI

• o Measured lRI

~

0•

0

Qo

0 00 IiJ

• • o IiJ a 0

•• 0

lIP 8 lS'=I 0

9-1017

16·1010

5 7.5 10 12.5 15 17.5 20 22.5 2S 27.5

Pltvement Age,yr

1 in/mi = 0.0158 m/km

o

o 2.5
Dry-Freeze (FZI>160, Ann Precip < 20)

lRf A,. PII4 P200 w" SN Po ACl7ticf: AlIIIPrtcip Da,;sJ2· 0-90+ DarrWttlyr 0-.$'+ F~11twCl'C FZI. KEW.Iyr

JRI 1.000

Ace 0.414 1.000

PI4 0.163 0.073 1.000

P2IlO 0.325 .0.098 0.491 1.000

•.." 0.261 .o.em 0.399 0:793 1.000

SN 0.089 0.194 0.396 0.2U 0.199 1.000

Po 0.163 .0.234 0.3U 0 •• 0.596 0.361 1.000

ACThick .o.O!1 0.216 0.:»5 .0.1211 .0.129 0.831 -0.155 1.000

AmPn:cip 0.411 0.261 -0.052 0.124 0.250 .0.015 0.183 .0.286 1.000

o.yo32- 0.308 0.130 0.126 -0.111 .o.OS2 0.015 0.048 0.010 0.058 1.000

o.yo90.;- -o.2llS -0.051 0.140 0.263 0.139 .0.112 -0.110 .o.on .0.215 .0.696 1.000

o.yowcl!yr 0.442 0.211 .0.121 -0.101 -o.07ll 0.188 0.245 -0.001 0.618 0.531 .0.116 1.000

o.yo.5' + 0.355 0.161 0.119 0.230 0.359 .0.169 0.128 -0.354 0.901 -0.140 0.0211 0.216 1.000

FrzTh •.Cyc 0.018 0.063 0.052 0.018 0.233 0.241 0.061 0.253 . .0.380 0.312 -0.034 .0.183 .o.4S3 1.000

FZI 0.283 0.011 0.163 .0.090 .o.U2 .0.114 0.034 .o.2OIl 0.299 0.185 .0.668 0.619 0.116 -0.266 1.000

KESAUyr -o.Oll8 -0.086 .o.m .0.181 .0.210 0.105 -O.on 0.2111 O.UO .0.104 .0.146 0.259 0.041 -0.119 -0.066

Dry-Freeze (FZI>160, Ann Precip<20)

fRf wi,KESAL Tnd: F c••••T S.V·JI(KESAIhr "AO..sN 0-90+*lL40nSN FZ/"p2OO"w'J.IPo Annprtd"..F~11twCyclPo p1Q(}/po ""owl o-J2·"F~11twCl'C (KESAfJ>/rf.f (KESNhrf.$

IRJ 1.000

inilial KESAUYr -0.149 1.000

TNCkFaetot 0.056 0.361 1.000 -....l

0'1 CumTnC 0.238 0.369 0.191 1.000

SN'3/(KESAUYr) .0.161 .0.361 -0.442 -0.235 1.000

"ACinSN -0.145 0.128 0.085 0.440 0.111 1.000

(Ooyo90+ ,.."ACinSN -0.184 .0.042 -0.146 0.054 0.051 0.461 1.000

FZl"p:!OO"w"lPo 0.364 .0.333 -0.111 .0.226 0.673 -0.239 -o.2ll3 1.000

AnlIp=ip*FrzThwCyclPo 0.211 0.185 0.2119 0.161 0.OS3 0.2llS -0.022 -0.042 1.000

p2lllllPo 0.316 .0.164 .o.U3 -0.018 0.342 .0.188 0.211 0.622 .0.004 1.000

"sand -0.242 .0.048 0.264 0.045 -0.109 0.390 0.039 .0.384 0.082 .0.624 1.000

Ooyo32·*FrzThwCyc O.ISO -0.100 0.265 -o.0J6 0.G2S 0.131 -o.3S6 0.173 0.200 .0.102 0.138 1.000

(KESAUyr)" .1 0.011 0.153 0.545 0.583 -0.149 0.161 -0.058 .o.S48 .0.023 .o.29S 0.096 .o.09S 1.000

(KESAUy,)" .5 -0.045 0.852 0.S11 0.621 -0.541 0.220 -0.063 -0.413 0.011 .0.238 0.068 .0.119 0.955 1.000

Figure 5.2.4. Correlation matrices used for review of GPS-l dry-freeze data and model building.
GPS-l Dry-Freeze Scatter Plots

2SO 2SO 2SO

200 •

200 200

_ISO •

:, ; ISO _ISO

CI:

~

• • llC

-100 • .ft -100 -100

SO • ,. SO SO

0

• • 0

0 3) 4Onoo 60 ., 100 ~~KESAm.. 200 2SO 0 S

~

15

~j 2SO 2SO

200 • 200 •200 •

• • • ISO I • ·

ISO •

•

_ISO •

$100

~

;

llC

-100 -100

so+ :1 so

01

0 100

~

300 400 500 1000 15OO

FZ1

:lOOO 2SOO 3000 3500

0 100 KESAU)T 200 300

....:I 2SO 2SO ....:I 2SO

200 • 200 200 • ,

ISO _ISO •

• ISO

,

$

CI: • ;

100 -100 -100 •

SO •

SO SO •

3 4 5 6

40 "sand 60 ., 5 10 d 15 100 SN

2SO 2SO 2SO

200 # 200 •• 200

,

• •

••

•

ISO •

_ISO _ISO •

; CI: • • •

• •

CI:

-100 ••

-100 -100 .

•••

• • • • • SO SO •

SO

0

10 3) 30

500 1000 1500 :lOOO 2SOO 3000 3500 0 SO 100 ISO 200

Aae,yr Cum Tnt I'ZOO'"w'I·FZU(lOOOOOPo)

Figure 5.2.5. Scatter plots used for analysis of GPS-I data and model building.
Th~s parameter is thought to give an indication of the frost susceptibility of a given

pavement system. This parameter shows strong correlation to the IRI values in freezing

environments and is typically stronger than any of the individual parameters within the cluster.

Figure 5.2.6 shows the sensitivity of the dry freeze model when the subgrade conditions

vary from a sandy gravel to soft clay. These models are very sensitive to the input parameters.

Input parameters must be realistic and within typical ranges for dry freeze conditions, and within

the range for parameters contained in the data set used for model building. Shown in parentheses

next to the parameter name is the range of the parameter encountered in the LTPP data used to

build the model.

Traffic rates for this data set varied from 2 to 250 KESAL/yr with an average of74

KESAL/yr. The pavements in this subset that were poor performers generally existed on highmoisture-content clays, often with in-situ moisture contents that were higher than the plastic limit

of the subgrade soil. The pavements in this subset that performed well generally rested on

granular soils that contained 10 to 30 percent ofP200 material and had relatively high AC layer

thickness. Pavements with sub grades having P200 less than 10 percent appeared to have higher

initial IRI values.

Sections 16-1010 and 49-1017, shown in figure 5.2.3, are considered to be the smoothest

and oldest pavements in the data set. These sections had P200 values of 10 percent and 16

percent, respectively, sub grade moisture contents of 5 percent and 9 percent, respectively, traffic

levels of60 and 75 KESAL/yr, respectively, and annual precipitation values of300 mm (12 in)

and 225 mm (9 in), respectively. A preliminary evaluation of section 16-1010 based on the

AASHTO equation indicates that the cumulative traffic volumes were below those expected to

cause a terminal serviceability level. Section 49-1017 has a low estimated SN of 2.98; the

subgrade is a clayey gravel with sand, with 56 percent passing the No.4 sieve. This 28-year-old

yet very smooth section is estimated to be near the end of its useful service life according to the

AASHTO equation. Good subgrade conditions and low precipitation are factors that likely

resulted in this section remaining smooth for over 25 years.

The poor performers generally existed on high-moisture-content clays, often with in-situ

moisture contents that were higher than the plastic limit of the sub grade. Section 81-1804

(identified in figure 5.2.3) is less than 10 years old and has a rapidly increasing 00. A preliminary

analysis of this section with the AASHTO pavement design equation indicated that this section

was at the end of its service life. This section has higher values for precipitation (475 mm (19
Parameter (Data Rane.e) Case 1 Case 2 Case 3 Case 4 Case 5

•• ACinSN (15-85) 20 20 20 20 20

KESAL/yr (1-150) 225 225 225 225 225

1'100 (7-95) 10 25 45 65 95

w•• (1-10) 4 8 12 15 20

•• Sand (3·90) 60 65 60 33 4

Po, ps' (110-360) 200 200 200 200 200

SN (1.1-5.9) 4 4 4 4 4

ADnPrecip, in (3.10) 10 10 10 10 10

FZI, deg rdays (160-3400) 1700 1700 1700 1700 1700

INITIAL IRI, inImi = 49 41 39 38 36

Growth Rate Constant, r = 0.0035 0.0044 0.0078 0.0146 0.0356

:;Dry Freeze IRI Model /,-

/

.' ./

./ .' '"

-'-"Cue 1

--Caae2

·······Caae3

-"-'Case 4

·······CueS

's 200 :5

.: ISO e:

lloo

e'.0:\

~ SO

O-t-------r-------t-------+-------t--------l

o 10 IS

Pavement Age, yr

Figure 5.2.6. Sensitivity of the dry freeze IRI prediction model to

changes in subgrade conditions.
in)), freeze index (1339 °C days (2411 of Days)), P200 (89 percent), and subgrade moisture

content (14.5 percent), and has an estimated SN of 4 and is subjected to 74 KESAL/yr.

The dry-no freeze environment is defined as a region that has less than 508 mm (20 in) of

annual precipitation and a freezing index ofless than 89°C days (160 OFdays). The roughness

behavior of the pavements in this region appears to be controlled by the structural properties of

the pavement, layer thickness, traffic, and subgrade parameters. Traffic rates for this data set

varied from 30 KESAL/yr to 360 KESAL/yr, with an average of 110 KESAL/yr. Figure 5.2.7

shows the model developed to predict IRI for these pavements. This model was based on 79 IRI .

measurements from 22 sections and had an average error per section of 0.19 m/km (11.9 in/mi);

the standard deviation of errors at the sections was 0.14 m/km (8.6 in/mi). Figure 5.2.8 shows

the sensitivity of the resulting model to changes in traffic conditions. Shown in parentheses next

to the parameter name is the range of the parameter encountered in the LTPP data used to build

the model.

As shown in figure 5.2.7, the best long-term performers were sections 6-8535 (estimated

SN = 5.5) and 6-8534 (estimated SN = 4.2). These sections are subject to very hot and dry

conditions and high traffic (KESAL/yr = 360 and 240, days over 32°C (90°F) = 167). These

sections are located on relatively high-P200-content but lower-moisture-content subgrades, but

they utilize thick base and subbase layers such that the overburden pressures on the sub grade are

greater than 14 kPa (300 Ib/ft2). Annual precipitation in the area where these sections are located

is 75 mm (3 in) per year. The AASHTO equation predicts that 6-8535 is still within its

anticipated service life. Section 6-8534, however, is estimated to be at the end of its service life

according to the AASHTO equation, but has remained very smooth.

The section with the highest IRI in the data set was section 4-1002 (see figure 5.2.7). A

preliminary evaluation of this section based on the AASHTO design equation indicated that this

section was near the end of its estimated design life. This section has an AC thickness of 260 mm

(10.4 in) and rests on a sub grade that is very dry and has a low percentage ofP200 material. This

site was the least predictable section in the data set. Another poorly performing section is section

4-1037 (see figure 5.2.7). A preliminary evaluation of this section based on the AASHTO

equation indicated that it has exceeded its estimated design life. This site is located in a very hot

region that has 180 days over 32°C (90°F); the pavement section has an estimated SN of3.4.
r

o

= [I(KESAL /yr/ + M(AnnPrecip x FZThWCyC)N + O(FZljP + Q(P200 / pOjR] X _1_

K(SNi Po 1000

A= 70.163738 F= -1.0523933 K=I90 P = 3.6002489

B= 0.0719436 G= -309.87143 L= 3.5799804 0= 2.7983612

C= -180.49308 H= -1.2513266 M= 8.032E-05 R= -0.9909045

D= -3.5455528 1= 130 N = 5.4828793 s= 1.0568

E= 370.09218 J= 1.65 0= 3.838E-06 T= 10

100 ISO

Meas •••• d !RI, iDhJi

250

200

·1 1.50

:5a 100

.50

0

0 2.5

GPS-l Actual vs Predicted IRI

4-1002

•

4-1037

D

•

0

0 • •

• •

D 0 0 0

~

oS 7.5 10 12.5 15

Pavemed Age, yr
Parameter (Data RaDKe) Case 1 Case 2 Case 3 Case 4 Case 5

KESAUyr (30-360) 30 100 170 265 360

SN (2.2-6.7) 2.70 2.70 2.70 2.70 2.70

ACThick, in (2.4-13) 5 5 5 5 5

P200 (4-70) 70 70 70 70 70

Po, psr (120-370) 140 140 140 140 140

FZI (0 to 145) 140 140 140 140 140

AnnPrecip, in (3-20) 20 20 20 20 20

FrzThwCyc (3-150) 110 110 110 110 110

INITIAL IRI, in/mi= 45 45 45 45 45

Growth Rate Con.~tant, r = 0.506 0.540 0.595 0.697 0.826

250

·s 200

]

ef 150

."

~ 100

.§

~ 50

,0

0

Dry-No Freeze GPSI IRI Mode~/

'" .' '" ."

.'"

/

,,'

~

.' ""

--Case 1

---'Case 2

······Case3

_._. Case4

_ .. - Case5

10 15

Pavement Age, yr

Figure 5.2.8. Sensitivity of the dry-no freeze IRI prediction model to changes in

traffic volume, holding all other parameters constant.
The wet freeze environment is characterized by an annual precipitation greater than 508

mm (20 in) and a freezing index greater than 89°C days (160 of days). The data for this region

were divided into three categories on the basis of the percent passing the 75-Jlm (No. 200) sieve

(P200) for subgrade. The first model is for fine-grained soils (P200 > 50 percent), the second

model is for coarse soils with P200 between 20 and 50 percent, and the third model is for coarsegrained soils with P200 less than 20 percent. The boundary ofP200 equal to 50 percent is a

standard boundary in the Unified Soil Classification System to differentiate between fine- and

coarse- grained soil. The boundary ofP200 equal to 20 percent was somewhat arbitrary, but

coarse-grained soils with P200 greater than 20 percent often behave much differently than clean

granular soils. This boundary also divided the data set somewhat equally.

The data appear to indicate that fine-grained soils are sensitive to the climate, subgrade

conditions, and layer thickness. No strong correlation was noted with hot weather conditions for

the wet freeze climate. Sub grade parameters and the severity of freezing are in general the

controlling factors for roughness development in these conditions.

Figure 5.2.9 shows the model developed for the wet freeze climate for pavements with

sub grade having a P200 greater than 50 percent. The least predictable sections with this model

were thick AC sections that have very little base or subbase. This model was based on 96 IRI

measurements from 19 sections and had an average error per section of 0.24 mIkm (15.2 in/mi);

the standard deviation of the errors at the sections was 0.19 mIkm (11.9 in/mi). These pavements

are sensitive to the Atterberg limits of the subgrade, moisture content of the subgrade, and the

freezing index. Figure 5.2.10 shows the sensitivity of the resulting model to variations in the SN.

Shown in parentheses next to the parameter name is the range of the parameter encountered in

the LTPP data used to build the model. Traffic rates varied from 16 to 1600 KESAL/yr with an

average or 160 KESAL/yr for this data set.

The best long-term IRI performer within this data set was section 21-1034 (see figure

5.2.9). A preliminary analysis of this section with the AASHTO equation indicated that this

section is still within its service life. This section consists of a very thick AC surface and a low
200H

°1 ISO

l

c;ilOO

...

fRIo = A(P200)8 + C(1 + pl)D + E(ACThickt + G(PO)H

r. = [f(KESAL / yr)J + M(1 +Pf)N +O«w% x P#4 x FZI)/ ,P +Q(P200)R] x _1_

o K(SN)L 7p(Y 1000

A=-9.014E-06 F=-O.l725499 K=180 P=2.3882482

B= 3.4906533 G=8.361E-Hl L=4 Q= 2.062E-08

C= 44.459606 H=-10.537378 M=0.OOOI832 R= 5.9060862

D=0.142 1=3000 N=4.8077516 5=1.078564

E=56.917593 J=I.9 0=0.0465496 T=200

ElTOr Distribution for Wet-Freeze Model

250 Su!lgnule P200 >50%

so 100 ISO 200

Measured IRI, ioJmi

.M_utodJRJ

D PRdided JRI I

D•

8 8

10 12.5 15

l"nve •••••1It Age, yr
.§ 200

:s

g ISO

'il .j 100

~ SO

Parameter (Data Ranle) Ca.~ I Case 1 Case 3 Case 4 CaseS

KESAL/yr (10-1600) 250 250 250 250 250

SN (1.8-10.3) 4.32 3.98 3.64 3.3 2.96

ACThick, in (1.1-16) 8 7 6 5 4

Po, psr (0-34) 180 180 180 180 180

PI (0-34) IS 15 IS IS IS

P200 (50-100) 66 66 66 66 66

w~ (1.5-17) 16 16 16 16 16

FZI (160-1730) 1400 1400 1400 1400 1400

INITIAL IRI, in/mi = 85 86 87 89 90

Growth Rate Constant, r = 7.67 8.34 9.37 11.01 13.77

--Case 1

---Case2

..... Case3

-·-·Case4

-"-Case5

o

o 10 IS

Pavement Age, yr

Figure 5.2.10. Sensitivity of the wet-freeze IRI prediction model to changes

in estimated SN, by varying AC thickness.
base thickness resting on a very high-water-content clay (P200 = 83 percent, w% = 27 percent).

This section has low traffic (30 KESAL/yr), a low freeze index, and high precipitation.

The worst performers in this data set were sections 27-1085 and 20-1005 (see figure

5.2.9). These sections consist of thick AC layers with very little base or subbase. These sections

are subjected to high freezing indices, and the frost-related effects appeared to be the cause of the

high IRI values. Section 20-1005 is virtually identical in all respects to section 21-1034, which

is identified as a good performer. The primary difference is that 20-1005 has a much higher

freeze index. Section 27-1085 has even lower traffic but has a higher freeze index and has a

higher IRI at an even earlier age.

Soils of this type are frost susceptible. The correlation coefficients determined from these

data fit th~ expected frost-related trends. The more sand present in the sub grade and the greater

the overburden pressure on the sUbgrade, the lower IRI values tended to be. Surprisingly, a

negative correlation to annual precipitation is noted. This may relate to the variation of

precipitation over the year. Figure 5.2~11 shows the model developed for the wet freeze climate.

This model was based on 88 IRI measurements from 15 sections and had an average error per

section of 0.18 m/km (11.4 in/mi). The standard deviation of the errors at the sections was 0.10

m/km (6.9 in/mi). Traffic rates varied from 10 to 1030 KESAL/yr with an average of230

KESAL/yr for this data set. Figure 5.2.12 shows the sensitivity of the resulting model to

variations in the freeze-thaw cycles. Shown in parentheses next to the parameter name is the

range of the parameter encountered in the LTPP data used to build the model.

The best long-term performer within this data set was section 37-1801 (see figure 5.2.11).

This section is a fairly high-traffic (220 KESAL/yr) section with a low freeze index (91°C

days/yr (164 OFdays/yr)) and a relatively thick AC surface (180 mm (7.2 in)) located on a

relatively wet, higher P200 sub grade (w% = 19.5 percent, P200 = 43 percent). This section was

at the margin between wet-no freeze and wet freeze conditions. It is ,believed that this section

would not have done well in high freeze index conditions. According to the AASHTO equation,

this very smooth section should be near the end of its service life.

One ofthe smoothest pavements in the data set is section 23-1012. This 8-year-old

section experiences very high traffic (600 KESAL/yr) and has a high pavement thickness (SN =

7.3, Po = 22 kPa (460 lb/ft?), AC thickness = 233 mm (9.3 in)) with a medium freeze index
fRIo = A(P200/ PO)B +C(%Sand)D +E(SN( +G(%ACinSH,10O>H

[

I(KESAL / yr)J ] 1

'0 = L + M(FrzThwCyc)N + O(%Sand)P + Q(PO)R x--

K(SN) 1000

A= 61.978361 F= 1.08049 K= 70 p= 6.1837864

B= 0.321181 G= 0.0730219 L= 4.5 Q= 9. 175E+ 12

C= 576.6484 H= -7.2190131 M= 2.576E-06 R= -4.9485028

D= -0.9128525 1= 240 N = 3.8083072 s= 1.1603307

E= 1.1868639 J= 1.352654 0= 5.238E-I0 T= 10.265499

ElTOr Distribution for Wet Freeze Model

200 20~ cP200 <SO~

80 120

Measured JRI, inlni

.M __ IRI

I o_...IRI I

51-1002 P

•

a

•

a

o u' ..

• •• • •

•

• • a •

I•• ~ OH • •••• • •

Ie a ~ a

•• a

"

a a

i i ,.0 !J~.~ a .0 \ .

iO •• •

u

a

a -a i ~

~

e D a

• , a

i

I

•

IP DB ~ •• • • .1 •• • - •

.

37-1801 23-1012

12.5

)'avement Age, yr
Parameter (Data RanKf» Case 1 Case 2 Case 3 Case 4 CaseS

KESAL/yr (16-1020) 350 350 350 350 350

SN (3.6-7.8) 5 5 5 5 5

"ACinSN (0.47-1.0) 0.66 0.66 0.66 0.66 0.66

Po, psr (125-460) 210 210 210 210 210

P200 (26-50) 20 20 20 20 20

"Sand (l8- 72) 70 70 70 70 70

FrzThwCye (71-132) 70 85 100 115 130

INITIAL IRI, in/mi = 49 49 49 49 49

Growth Rate Constant, r = 0.198 0.228 0.277 0.352 0.460

's 200

:s

;150

-

--Case 1

--- Case2

······Casc3

-'-'Case4

-"-CaseS

.•.•.

.......

,. .- . .' .

..",. .." .,..... ....-.-. --

.....•...-. ...-.--- -------

.- .---- ------

......::: :.=-.-:."-i;:. ~:-:.=::.; ~-::.-;..-:.~:-;.::"":": :..::.... _ ..-

0+-------+.------+-------+------+---------1

o S 10 15

Pavement Age, yr

1 in/mi = 0.0158 m/km

Figure 5.2.12. Sensitivity of the wet-freeze model to changes in number of

freeze-thaw cycles/yr.
(FZI=549 °C days/yr (988 OFdays/yr». The combination of good subgrade (P200 = 20%, w% =

12%) with high overburden pressures and thick AC layers likely resulted in this section having a

very low initial 00 values.

The worst performer observed was section 51-1002. A preliminary analysis of this

section with the AASHTO equation indicated that this section has exceeded its estimated design

life.

Pavements existing within these conditions do not appear to be as susceptible to freezing

effects compared with the higher P200 subgrades in the same climate. The number of days/yr

with greater than 12 mm (0.5 in) of precipitation is the strongest climate factor that is correlated

with 00. Figure 5.2.13 shows the model developed for the wet freeze climate with a subgrade

P200 less than 20 percent. This model was based on 15700 measurements from 32 sections and

had an average error per section of 0.24 m/km (15.6 in/mi). The st~dard deviation ofthe errors

at the sections was 0.19 m/km (11.9 in/mi). Pavement that had low SN values or pavements

located in areas of high freeze indices had the highest errors for the developed model. For this

data set, the traffic rates varied from 15 to 350 KESAL/yr with an average of 110 KESAL/yr.

Figure 5.2.14 shows the sensitivity ofthe resulting model to changes in layer thickness for a

constant SN. Shown in parentheses next to the parameter name is the range of the parameter

encountered in the LTPP data used to build the model.

The best long-term performer in this data set was section 26-1001 (see figure 5.2.13).

This is a very low-traffic (16 KESAL/yr) and low-SN (2.5) section resting on dry, clean sand

(p200 = 4 percent, w% = 4.5 percent). The AC thickness was 55 mm (2.2 in) and the overburden

pressure on the sub grade was 7.2 kPa (150 Ib/ft2). Another very smooth pavement is section 23-

1009 (see figure 5.2.13). This 24-year-old section rests on similar sub grade conditions and

utilizes thick base and subbase layers (Po = 19.2 kPa (400 Ib/ft2». A preliminary evaluation of

these two sections with the AASHTO equation indicated that the pavement sections are still

within their estimated service life.

The worst long-term performers in the data set are sections 89-1127 and 89-1125 (see

figure 5.2.13). Both of these are very thick sections (po > 23 kPa (480 Ib/ft2» located in a high

freeze index environment (FI > 1167 °c days/yr (2100 OFdays/yr». Traffic levels are very

different (25 and 350 KESAL/yr, respectively); however, their 00 behavior is similar. The only

factor that explained the high 00 values for these sections was the high freezing indices,
2SO

200

'il 150

l

8100

50

0

0 2.5

1RIo = A(P200 x PO)' + C(%ACinS~Oo>D + E(ACThick)F + G(W%)H

ro = [I(KESAL / ;)J + M(FZI /1(0)N + O(FZI/p)' + Q(Days.5',+))R + S(ACThiCk)T] X _1_

K(SN) /fo 1000

A= 163.38286 F= -1.6724454 K=3.7680662 Q=3.004E-09

B= -0.0839446 0=-1922.9695 L=6.3120233 R=8.0280584

C=0.0130354 H=-5.7582451 M=0.0259789 S= -0.0008525

D=-8.4850338 1=31378.88 N= 3.8343596 T=6.3945824

E=-329.31446 J= 1.4836653 0= 1559.7432 U= 1.0489556

p= -2.4342743 V=200

Error Distribution for Wet Freeze Model

250 Subgrade })200 < 20%

200

·s

I:

._~ 150

;2

-'a

~100

E 50

0

0 50 100 /50 200 250

Mellsured IR , inlml

Actual vs Predicted JRI

89-1125 •

•

10 12.5 IS

J'ovCIIIClIl Agc, yr
·s 200

]

;2 ISO

-

Parameter (Data Ranae) Case 1 Case 2 Case 3 Case 4 Case 5

KESAL/yr (t 5-340) 120 120 120 120 120

SN (2.2-7.4) 3.0 3.0 3.0 3.0 3.0

ACThic:k (2.2-10.5) 5.6 4.8 4.1 3.3 2.5

%ACinSN (0.35-.95) 0.83 0.71 0.60 0.48 0.37

Po, psf (95-480) 120 ISO 180 210 240

P200 (1-15) IS IS IS IS IS

w% (2-11) 8 8 8 8 8

Days.5" + (5-35) 25 25 2S 25 25

FZI (160-2740) 1400 1400 1400 1400 1400

INITIAL IRI, in/mi= 69 62 53 44 73

Growth Rate Constant, r= 11.59 11.74 11.S0 11.63 11.38

..'

...... ..••..

...... ..-.

................. _.-......- ...••••.... --Case 1

--- Case2

······Case3

_._. Case 4

_ .. - CaseS

o

o 10 IS

Pavement Age, yr

1 in/mi = 0.0158 m/km

Figure 5.2.14. Sensitivity of the wet-freeze model (P200 < 20 %) to changes in layer

properties at constant SN.
indicating that the environment is a controlling factor for these very thick sections in a severe

climate. On the basis of AASHTO structural equation, these rough sections are estimated to be

at the very beginning of their estimated service lives, indicating the strong effects of the severe

climate on IRI and serviceability.

The wet-no freeze environment is characterized by annual precipitation greater than 508

mm (20 in) and an annual freeze index ofless than 89°C days/yr (160 OFdays/yr). The data for

these sections were divided into three categories based on the percent passing the 75-Jlm (No.

200) sieve (P200) in the subgrade soils. The first model is for fine-grained soils (P200 > 50

percent), the second is for coarse-grained soils with P200 between 20 and 50 percent, and the

third is for coarse-grained soils with P200 less than 20 percent. The boundary ofP200 equal to

50 percent is a standard boundary in the Unified Soil Classification System to differentiate

between fine-grained and coarse-grained soil. The boundary of P200 equal to 20 percent was

somewhat arbitrary, but coarse soils with P200 greater than 20 percent often behave much

differently than clean, granular soils. This boundary also divided the data set somewhat equally.

The pavements in this region are sensitive to the number of days above 32°C (90 OF),

which indicates that roughness is related to the softening of the asphalt concrete. Surprisingly,

all subgroups show significant negative correlation to annual precipitation, which may indicate

I

either a cooling effect from precipitation resulting in a higher AC stiffness over the year, or that

areas with greater temperature and moisture variations over the year may have higher pavement

roughness than more uniformly moist/uniform temperature climates.

Figure 5.2.15 shows the model developed for the wet-no freeze environment with finegrained soils (P200 > 50 percent). This model was based on 97 IRI measurements from 24

sections and had an average error per section of 0.23 m/km (14.6 in/mi). The standard deviation

of the errors at the sections was 0.17 m/km (10.5 in/mi). Traffic rates varied from 15 to 625

KESAL/yr with an average of 170 KESAL/yr for this data set. Traffic is not a strong factor in

the IRI behavior of these pavements. Subgrade parameters such as plasticity index (PI), moisture

content (w%), P200, and hot weather effects are the strongest factors that were correlated with

IRI for these pavements. There is a strong negative correlation to precipitation, which is not
IRIo

= A(PO)B +C(P200)D + E(w% x %sand x Po( + G(ACThicyPO)H

[

l(KESAL I yr)J LI+.OOOOI ] 1

To = L + M(O.5+PI)N +O(---)p +Q(FrzThwCyc X Days90+)R+S(DaysWet I yr)T x-

~~) ~ 1000

A=3069.239 F=0.0073774 K= 1.8153442 Q=4.333E-07

B= -0.0076966 G=-443.09587 L=3.0510484 R=2.5926674

C= -5034.3994 H=0.0419734 M= -2384835.6 S ~ 8.035E+l7

D=-0.0292012 1=0.0029165 N= 7.464E-05 T=-7.8133062

E= 1794.5335 J=3 0=2388175.3 U= 1.4938206

P = 7.636E-05 V=2oo

250 Eiror Distribution for Wet·No Freeze Model

Subgrade PlOO >50~

• •

48-1178

48-1130

•

D

D ~ • MealUlTA I

D D Predi:tcd

• .. . II I!l ••

D

• ~ D D

~~

DC D

•

"

• .,D.

'-D~

~ ~ D D-'I.~.,'W" I!l _"DC D D

• •

D D

D • _ II ;;.J 3D- - ••• I!l_ •• • • • •

. lII!l~ - - - ~. D

8-3865

D

9

D D

~ III!ID . 9

37-1008

10 12.5 15

PlIve1l1enl Ace, yr
clearly understood except from the perspective of reducing hot weather damage or reduced

shrink-swell of soils from being wet more of the time. Figure 5.2.16 shows the sensitivity of the

resulting model to varying SN. Shown in parentheses next to the parameter name is the range of

the parameter encountered in the LTPP data used to build the model.

The best long-term performer in the data set was section 48-3865 (see figure 5.2.15).

This section has a P200 of 57 percent, a moisture content of 11.5 percent, was at the lower end of

precipitation (675 mm/yr (27 in/yr», is subjected to low traffic (65 KESAL/yr), and has a thin

AC layer (58 mm (2.3 in» with overburden pressures on the sub grade of 10.5 kPa (220 lb/ft?).

This section has exceeded the AASHTO estimated traffic volumes that would cause terminal

serviceability .

One ofthe smoothest pavements in the data set is section 37-1008 (see figure 5.2.15).

Tills is a 12-year-old, high-traffic route (515 KESAL/yr), resting on a wet sub grade (p200 = 50

percent, w% = 31 percent), which experiences very little hot weather. The AC thickness is 233

mm (9.3 in) with a sub grade overburden pressure of 10 kPa (210 lb/ft?). This section is

estimated to be within its service life on the basis ofthe AASHTO equation.

The worst long-term performer in the data set was section 48-1130 (see figure 5.2.15),

which was subjected to a low traffic level of 44 KESAL/yr. This section rests on a highplasticity clay (LL = 53 percent, PI = 30 percent, P200 = 82 percent, w% = 26 percent) with the

in-situ moisture content greater than the plastic limit of the soil (LI = 0.115). This section has an

AC thickness of 68 mm (2.7 in), with the overburden pressure on the subgrade being

approximately 15.3 kPa (320 Ib/ft2). The AASHTO equation indicated that this section is at the

early stages of its service life.

Figure 5.2.17 shows the model developed for IRI prediction of pavements in wet-no

freeze climates that have coarse soils with a lower P200 content (20 percent<P200<50 percent).

This model was based on 96 IRI measurements from 21 sections and had an average error per

section of 0.15 mIkm (9.6 in/mi), The standard deviation of the errors at the sections was 0.17

mIkm (10.6 in/mi). Traffic rates varied from 1 to 1180 KESAL/yr with an average of 180

KESAL/yr for this data set. These pavements are the best overall performers of all GPS-1

sections and are most sensitive to cross-section properties and warm weather effects. Figure

5.2.18 shows the sensitivity of the resulting model to variations in traffic volumes. Shown in
Parameter(Data RaDKe)Case 1 Case 2 Case 3 Case 4 Case 5

KESAL/yr (15-630) 300 300 300 300 300

SN (1.9-6.8) 2.5 3 3.5 4 4.5

ACTbick, in (1.5-11) 3.5 3.5 3.5 3.5 3.5

Po, psf (126-330) 120 187 249 312 330

LI (6-0.3) 0.1 0.1 0.1 0.1 0.1

PI (6-30) 15 15 15 15 15

w•• (11-31) 15 15 15 15 15

noo (56-97) 66 66 66 66 66

••Sand (2.5-50) 27 27 27 27 27

DaysWetlyr (69-145) 85 85 85 85 85

Day590+ (1-130) 35 35 35 35 35

FrzThwCyc (4-86) 55 55 55 55 55

INITIAL IRI, inImi= 65 68 70 72 72

Growth Rate Constant, r= 5.03 3.82 3.19 2.83 2.63

Wet-No Freeze IRI Model: P200 > 50%

/ .' ~~'

, ".*",. "".- " ..' ,. ,

",(11' ...,," ., •••

,*' , .".~ .

". ...;,.~

,,,,,,,, ...'•...... ::;"" .

.,/~ ..:~..;:::'""'...

, -•....

•...-:;;: ~t-ii ::-::= .

.,...".$.~r••.•·

--Case 1

---Case2

······Case3

-·_·Case4

-··-Case5

10 15

Pavement Age, yr

Figure 5.2.16. Sensitivity of the wet-no freeze IRI prediction model (P200>50%) to

changes in estimated SN, holding AC thickness constant.
IRIo = A(W%)B + C(SN)D + E(P200)F + O(PO)H

I(KESAL I yr)J .

ro = L + M(ACthICk)N + O(Days90+l + Q(DaysWet I yr)R

K(SN)

A= 3.~E-01 F= -o.4D K= 832.46al p= 0.0414

B= 1.7353 G= -6.7049 L= 11.3978 Q= -0.00344

C= -0.3749 H= 0.29413 M= 0.0740552 R= 0.4553

D= -0.27133 1= 0.0573665 N= -6.6522 S= 1.7173

E= 312.61 J= 2.8032 0= 0.029425 T= 3.m6

ElTOr DiUrilmtion for Wet-No Freeze Model

10,. <SubImde P100 <SO,.

50 100 ISO 200

Mealured JRI, inhN

•

45·1025

~

• M •••••• JRI

o_ ••IRI

29-1008 •

i

• 8 • • "

• ~.. •

•• • IU.! 0 .00 i .". 0

0 • 0

83 • • 3

o • 00 rP •

0

a

0 0 0 0 0 0

eo ~ • o. •

f· ~~ .. ~ .,. ~ ~

~a!••• • • •• .~

0

0 • • •

• • •

12·4103 37·1814

1

:s 100

g

10 12.5 15

J'IonmontAae.yr
Parameter (Data Range) Case 1 Case 1 Case 3 Case 4 CaseS

KESAL/yr (2-1100) 200 400 600 800 1000

SN (1.3-8.4) 3.2 3.2 3.2 3.2 3.2

Po, pst (75-430) 110 110 110 110 110

ACTbick, in (1-13) 8 8 8 8 8

w% (1-18) 10 10 10 10 10

P100 (20-50) 30 30 30 30 30

Days90+ (8-103) 100 100 100 100 100

DaysWetlyr (80-165) 80 80 80 80 80

INITIAL IRI, In/mi = 64 64 64 64 64

Growth Rate CoDStant, r= 0.011 0.013 0.018 0.027 0.041

--Case 1

---Case2

..... Case3

-·_·Case4

_··-Cases

". .",,"" .' .'" .-,,- ,,--'

/ / ..

.,.. / .

" ,"" .-- , ..,.. ...,.. --- ...,... _.......... .-.- ---

.. ..,... _.- .-....._-------

.·-:.;::.:.-.·.7:::.··· ...····· --- -:.-::._-

10 15

Pavemeot Age, yr

Figure 5.2.18. Sensitivity of the wet-no freeze IRI prediction model (20% <P200 <50%)

to changes in traffic volume/yr, holding all other parameters constant.
parentheses next to the parameter name is the range of the parameter encountered in the LTPP

data used to build the model.

The best long-term performer in this data set was section 37-1814 (see figure 5.2.17).

This 24-year-old, 85 KESAL/yr section is characterized by high precipitation (1350 mm/yr (54

inlyr)) and very little hot weather (10 days over 32°C (90°F)). This smooth section is estimated

to be approaching the end of its useful service life according to the AASHTO equation.

A very smooth pavement in this data set is section 12-4103 (see figure 5.2.17). This 12-

year-old, very-high-traffic route (1126 KESAL/yr) has high precipitation (1425 mm/yr (57

inlyr)), moderate hot weather (55 days over 32°C (90°F)/yr), and has a high overburden pressure

of 16.3 kPa (340 lb/fl?) on a coarse-grained subgrade (P200 = 24 percent, w% = 7.5 percent).

This section is estimated to be within its service life according to the AASHTO equation.

A poor performer in this data set was section 45-1025 (see figure 5.2.17). A preliminary

analysis of this section with the AASHTO design equation indicated that this section should be

approaching the end of its service life. This section has an estimated 5 KESAL/yr and has a SN

of 1.65, and rests on a subgrade having a high moisture content and a high P200. Another rough

pavement noted is section 29-1008 (see figure 5.2.17). This section utilizes a thick AC section of

285 mm (11.4 in) with low subgrade overburden pressure (po = 5.6 kPa (178 lb/fl?)). It is

unclear why this section has such high 00 values early in life; perhaps construction related

issues affected roughness.

Figure 5.2.19 shows the model developed for the wet-no freeze environment with clean,

coarse-grained soils (P200<20 percent). This model was based on 77 00 measurements from 21

sections and had an average error per section of 0.19 m/km (12 inlmi). The standard deviation of

the errors at the sections was 0.15 m/km (9.5 inlmi). Traffic rates varied from 36 to 660

KESAL/yr, with an average of 180 KESAL/yr for this data set. There are indications that for

subgrades within this range, higher precipitation, higher P200, and higher moisture content

reduce the 00. Again, correlation oflRI to hot weather is very strong. Figure 5.2.20 shows the

sensitivity of the resulting model to changes in traffic volume. Shown in parentheses next to the

parameter name is the range of the parameter encountered in the LTPP data used to build the

model.
fRIo = A(W%)B +C(SNl +E(P200l +G(ACThick)H

I(KESAL / yr)J ACthick X Po '0 = [ L + M( )N + O(Days90+)P + Q(DaysWet / yr)R] x)(OOO

K(SN) w% x KESAL / yr

A=31.966912 F=6.9990895 K=1 P=6.6583095

B= 0.2839958 0=59.02204 L=77.998698 Q=2.471E+l2

C=-9.857E-07 H= -1.6203565 M=33801.58 R=-4.6876717

D=9.3738374 1=2.5 N=-17.172621 S = 1.0356619

E=-4.042E-08 J= 1.2 0= 1.595E-I0 T=200

2jO ElTOr Distribution for Wet-No Freeze Model

Subgmde P200 <20"

• I I

so 100 ISO 200

Measured 00, in/11K

,

12-4136

I • MeaIlRd IR1 I

I D PKdicted lR1 I g

-.-

48-3739 • ••• ••

•

•

•

D D cD ••••

D

D

•

,

D

• •

D P. • ~ • ... -- D

D

. • .0 _ i· It ~D D g

~- ~ IcD •

~

,... • D.a

D ~.~ • ft~ •• • ~ ft - -

•

IS • • 48-1122 D D D D

12-1060 12-3996

10 12.5 15

l>avelllelt Ace, yr
Parameter (Data Range) Case 1 Case 2 Case 3 Case 4 Case 5

KESAL/yr (36-660) 350 400 450 500 550

SN (1.8-6.0) 3.5 3.5 3.5 3.5 3.5

ACThick (1.3-9.4) 6 6 6 6 6

Po, psf (100-370) 120 120 120 120 120

w% (1-13) 13 13 13 13 13

noo (0-20) 15 15 15 15 15

DaY590+ (4-117) 90 90 90 90 90

DaY5Wetlyr (82~191) 85 85 85 85 85

INITIAL 00, in/mi = 62 62 62 62 62

Growth Rate Constant, r= 3.88 4.00 4.83 9.71 33.86

's 200

:s

B150

1

100

50

--Case 1

--- Case 2

·····-Case3

----Case4

-··-Case5

..•.• ....• ....• ••...... .•..

- ...-

...- ...-...

...,.. ......- ..-.

--..-----

.--------.--- .-_ .

..-..--.. .--" .. .. .

....

",'

..

.",,""

0+-------+--------+--------+--------1--------1

o 10 15

Pavement Age, yr

Figure 5.2.20. Sensitivity of the wet-no freeze IRI prediction model (P200<20%)

to changes in traffic volume/yr, holding all other parameters constant.
The best long-term performer in this data set was section 48-1122 (see figure 5.2.19).

This 23-year-old pavement has an estimated SN of 4.3. A preliminary evaluation of this section

with the AASHTO design equation indicated that the pavement section should still be within its

service life. This section uses thick base and subbase layers, resulting in a subgrade overburden

pressure of approximately 14.8 kPa (310 lblft?). Subgrade conditions for this section are good,

with a P200 content of 19.3 percent and a moisture content of 8.5 percent.

A poor performer noted in the data set is section 48-3739 (see figure 5.2.19). A

preliminary evaluation of this section with the AASHTO design equation indicated that this

section should still be within its service life. This section has an SN of3.5, with a thin, 45-mm

(1.8-in), AC surface, and is subjected to 155 KESAL/yr. The sub grade has a P200 of 5 percent

and a moisture content of 4 percent. It is believed that the thin AC layer was the cause of the

high IRI values at this section.

The models developed for the GPS-1 sections predict the development of IRI over time

for flexible pavements according to the concept of exponential growth ofIRI. The models

predict two parameters: the initial IRI and the growth rate. The initial IRI is predicted on the

basis of the structural properties of pavement layers and subgrade parameters. The growth rate

is an exponentially increasing function of time and is a function of structural properties of

pavement layers, sub grade and climate parameters, traffic loadings and time. The various

models have average per site errors between 0.14 and 0.25 mIkm (9 and 16 in/mi). Pavements in

wet freeze environments resting on clean sands and gravel (P200 <20 percent) were the least

predictable type of GPS-1 pavement.

Structural factors such as SN and thickness of the AC layers have a larger role than

climatic factors in roughness prediction for pavements in the no 'freeze environments.

Performance of pavements over fine-grained soils is strongly related to the P200 and Atterberg

limits of the subgrade soils. In hot climates, roughness is related to days above 32°C (90° F). In

cold climates, roughness is related to freeze index and freeze-thaw cycles. In all data sets, the

least predictable pavement types were pavements with thick AC layers and very thin base and/or

subbase layers. These pavement types can perform either very well or very poorly but are

obviously more sensitive to sub grade and climate conditions and construction procedures than

pavements with thicker base and subbase layers. Higher overburden pressures on the subgrade,

which reduce frost heave effects, appear to be critical for wet freeze environments and frost-
susceptible sub grades. In the wet regions, clean, coarse-grained soils with low P200, generally

less than 5 percent by weight, had higher and more variable 00 than coarse-grained soils with

P200 that was between 5 and 50 percent.

The time-sequence IRI values for each GPS-2 site were compiled and plotted against

pavement age. Figure 5.3.1 shows the development of 00 for the GPS- 2 sections classified

according to LTPP regions. The general trend observed for roughness development for these

sections was exponential growth. On the basis of observation of these plots, it was decided to

pursue the development of prediction models based on exponential growth of 00, as described in

the previous section on GPS-l pavements.

There are several base treatment types in the GPS-2 experiment. Table 5.3.1 shows the

base types encountered in the GPS-2 database and the number of pavement sections for each base

type. It was decided to build separate models for the different base types, except for the lean

concrete base that did not have sufficient sections for model building. Table 5.3.2 indicates the

10 parameters that had the highest Pearson correlation coefficients to 00, presented separately

for the base types used in GPS-2 model building.

There are indications that, for the cement-treated aggregate bases, higher moisture in the

subgrade and environment results in smoother conditions over time. Higher base thickness for

the cement-treated aggregate bases indicate higher 00 values. T~e pavements with soil-cement

bases showed a trend toward a lower IRI for increasing base thickness.

Traffic levels were not significant in the correlation analysis. The study of GPS-l

sections indicated that traffic effects on the 00 were generally detectable only for low SNs.

Most of the GPS-2 sections have higher SN estimates, which may partially explain why traffic

parameters did not correlate to 00 measurements for the GPS-2 sections.

Each model is described in the following sections along with a description of which

pavement sections were showing good and poor performance within each data set. As described

in section 5.2, the best and worst performers in each data set were evaluated with use of the

MSHTO pavement design equation to determine if the best performers were within the service

life and if the worst performers were at the end of their design life.
250 GPS-2, North Central

200

·s ISO

~

:e

a

100 ----- SO

0 -.- -.

0 S 10 IS 20 25 30

Age, yr

250 GPS-2, North Atlantic

200

·s

"----

:e ISO

~C::~~I':: a

100

•

SO ~

0

0 S 10 IS 20 25 30

Age, yr

2S0 GPS-2, Western

200 .•

~

ISO

~

a

100

SO .:·~~~~C

0

0 S 10 IS 20 25 30

Age, yr

2S0 GPS-2, Southern

·s

200

ISO

V

:e / .... ..... .----

a

100

.:-:~i2!#ff§ ::;.~.::::::::

SO

•

, 0 ,

0 S 10 IS 20 25 30 Age, yr

--

Figure 5.3.1. Plots showing the development of roughness for the GPS-2

sections within each LTPP geographical region.
Base Type Number of Sections

Cement Aggregate Mixture 39

Hot Mix AC (HMAC) 27

Asphalt-Treated Mixture 18

Soil-Cement 14

Lean Concrete 6

Table 5.3.2. Parameters with highest correlation to IRI for the

GPS-2 data sets (separated by base type).

AC Treated HMAC

P200 (+.60) SN (-.54)

P#4 (+.42) P#4 (-.51)

PI (+.40) Days32. (+ .47)

Cum Traf (-.30) AnnPrecip (-.46)

w% (+.22) ACThick (-.46)

ACThick (+ .17) P200 (-.36)

SN (+.15) BaseThick (+ .24)

Days32- (+.14) Po (-.21)

Po (-.08) w% (+.15)

BaseThick (+ .06) LL (-.10)

Cement-Agg Soil Cement

ACThick (-.30) FZI (+.67)

ACThick/BaseThick (+ .30) Days32- (+ .66)

%ACinSN (-.29) BaseThick (-.56)

BaseThick (+.19) AnnPrecip (+ .39)

Days90+ (+ .17) Days90+ (-.39)

PL (-.16) SN (-.36)

w% (-.10) P#4 (-.32)

P200 (+.07) Po (-.31)

- P200 (+.29)

- ACThick (-.23)

Figure 5.3.2 shows the model form developed for this data set along with plots of

measured versus predicted IRI shown as a line plot, and versus pavement age. This model was

based on 59 IRI measurements from 15 sections and had an average error per section of 0.14

mIkm (8.8 inlmi). The standard deviation of the errors at the sections was 0.11 mIkm (6.7 inlmi).

Traffic rates varied from 36 to 1010 KESAL/yr with an average of230 KESALlyr for this data
[

I(KESAL / yr)J ] 1

To = L + M(Days32-)N + 0(0.1 + P/)p + Q(ACThick + BaseThick)R x--

~~ l~

A-5.375E-+t2 F= 1.647102 K=2 P=5.973454

B=-16.515056 G=3.7768623 L=3 Q=35645608

C= 1.405E-+t4 H= 1.4903745 M= 3.0725968 R= -5.2694928

D=-5.925719 1=800 N= 1.4380862 S= 1.01

E=-0.2137858 J= 1.123564 O=3.333E-Q5 T=200

Enor Distribution for GPS-2 ACTreated Base Model

i ISO

g 100

J

] so

l:loo

SO 100 ISO

Measured IRJ, inlmi

I •Mtund IRI I

" Predi:1:d JRI 8-2008 0

~ .

88-1646

.

S4-1640 II .~ •

•

• n • liI .~ .i

. " 8 a • w

• - . ~ " ~"~

~\1!l.8 SO-1681

•1 ~

•• .~ •

I!l. • -I!l

o

0.0 10.0 12.5 15.0 17.5 20.0 22.5 15.0 27.5 30.0

Pavement Age, yr
set. Figure 5.3.3 shows the sensitivity of the resulting model to changes in sub grade conditions

ranging from clean sand to fat clay. Shown in parentheses next to the parameter name is the

range of the parameter encountered in the LTPP data used to build the model.

The best long-term performer is section 50-1681 (see figure 5.3.2). This 26-year-old, 70

KESAL/yr section is located in the wet freeze environment and has an estimated SN of 4.5. This

section has a relatively thin AC surface layer of 58 mm (2.3 in), an asphalt-treated base layer of

85 mm (3.4 in), and a thick subbase. The subgrade is a clayey gravel with sand. A preliminary

evaluation of this section with the AASHTO design equation indicated that the pavement section

is still within its service life.

Section 8-2008 is a poor performer in this data set (see figure 5.3.2). A preliminary

evaluation of this section with the AASHTO design equation indicated that this section is at the

end of its design life. This section is located in the dry freeze region and has a AC surface of 93

mm (3.7 in) and an asphalt-treated base layer of 185 mm (7.4 in) thickness.

The other two rougher pavements noted in figure 5.3.2 are sections 88-1646 and 54-1640.

Each section has approximately 500 mm (20 in) of asphalt-treated base plus AC thickness resting

directly on the subgrade, and are located in the wet freeze environment. Both sections are

subjected to low traffic, and a preliminary evaluation of these two sections did not indicate that

they were underdesigned for the amount of traffic.

Figure 5.3.4 shows the model form developed for this data set along with plots of

measured versus predicted IRI shown as a line plot, and versus pavement age. This model was

based on 90 IRI measurements from 22 sections and had an average error per section of 0.22

m/km (11.8 in/mi). The standard deviation of the errors at the sections was 0.14 m/km (8.7

in/mi). Traffic rates varied from 40 to 1400 KESAL/yr with an average of 335 KESAL/yr for

this data set. Figure 5.3.5 shows the sensitivity of the resulting model to changes in traffic rate

for a pavement having a SN of 3, that is located in a wet environment. Shown in parentheses

next to the parameter name is the range of the parameter encountered in the LTPP data used to

build the model.

The best long-term performer in the data set is section 12-4100 (see figure 5.3.4), which is

located in the wet-no freeze environment. This section is subjected to low traffic (46 KESAL/yr)
Parameter (Data Range) Case 1 Ca.~Z Case 3 Case 4 CaseS

KESAUyr (36-1010) 200 200 200 200 200

SN (3.8-8.4) 3.87 3.87 3.87 3.87 3.87

ACthick, in (1015) S.S S.S S.S S.S S.S

Base1bick, in (z..14) S S S S S

Po, psI (118-508) 123 123 123 123 123

PZOO(5-69) S 20 33 SS 69

PI (6-Z6) 0 3 12 22 26

Days3z.. (15-194) ISO ISO ISO ISO 180

INmAL IRI, inlmi = 103 86 7S S3 42

Growth Rate Constant, r= 8.19 8.19 8.29 11.77 17.86

---:;

---,.', .... ". -- .

"". ...,,""'" ,. .

•. ".:,...,.". --- •••••.••C""'.~ ••.•

..,~ - •• , .::;1""

.,.,.....--:;- ::... .

----...- -- "...-:"'"..- .. -:'-.. ..""

------:.- ....~.:._..:...~' _ •._ .. _.

........ --

.... ...t:::·-

...-..... .-""

••••• .=.:::=::JI ••••

..•-.--=:=. -.-'"

--Case I

---Case2

..... Case3

_. - .Case4

-"-CaseS

10 IS

Pavement Age, yr

Figure 5.3.3. Sensitivity of the AC-treated base GPS-2 model to changes in

subgrade conditions.
·e ISO

.5-_~ 100

Cl:

-

IRlo = A(ACThick)B + C(Base1hick)D + E(P#4)F + G(SN)H

r

o

= I(KESALI/rl + M(ACThick)N + O(Days32- f + Q(AnnPrecipf

K(SN)

A= 40.8887 F= -0.8682 K= 20.7016 p= 2.3060

B= -0.5110 G= 40.0891 L= 12.00 0= 9.70E-05

C= 1953.287 H= -0.75037 M= 2.ooE-04 R= 0.1813

D= -2.95004 1= 101.57589 N= -0.2060 s= 1.2379

E= 349.64172 J= 0.6117952 0= 3.12E-09 T= 0.0226

Error Distribution for GPS-2

250 HMAC Base Model

"200

"8

d

·-:150

el

'0

~100

...

]

~ so

I D PlOdicled IR I I

I • M •• urod IRI I

D

SO •

o

0.0

I

SO 100 ISO 200

Measured IRI, in/mi

34-1033

D. r} i 8 D

D ._

• • •

.: • 8

~ •.••- u

Dill.. ••

• • •

D. Ii

... :

10.0 12.5 15.0

)avement Age, yr
Parameter (Data Range) Calle 1 Calle 2 Case 3 Case 4 Case 5

KESAUyr (37-1400) 50 200 500 900 1400

SN (2.9-6.4) 3 3 3 3 3

ACthick, in (1-6) 4 4 4 4 4

BaseThick, in (4.5-12.7) 5 5 5 5 5

PI4 (50-100) 75 75 75 75 75

Days32- (16-190) 125 125 125 125 125

AnnPrecip, in (15-70) 24 24 24 24 24

INITIAL IRI, in/mi = 63 63 63 63 63

Growth Rate Constant, r = 0.0006 0.0008 0.0010 0.0011 0.0013

HMAC Base GPS2 IRI Model

~.. .~ .... ",' .",- ,," , ,,,,, ." .,,'., ~/

• *" .' .- -*" *" ..; /- .. ~ --

,,,.",..",. -*",....

." .. ".. .. ..

./:.:. ""'':# ~..,.,.,-

..~,:{.:t.~~~::'~:::.::"- -

--Case 1

---'Case2

...... Case3

-'-' Case4

-"- CaseS

o

o 10 15

Pavement Age, yr

Figure 5.3.5. Sensitivity of the HMAC base GPS-2 IRI model to changes in traffic

volume, holding all other parameters constant.
and has a SN of approximately 4.7. The preliminary evaluation of this section with the

AASHTO design equation indicated the pavement section is still within its service life. The

annual precipitation at this location is very high with 1750 mm/yr (70 in/yr) but the section rests

on a clean sand subgrade (P200 = 7 percent, w% = 3 percent). The section consists of73 mm

(2.9 in) of AC, 190 mm (7.6 in) ofHMAC base, and approximately 300 mm (12 in) of subbase.

The section with the highest IRI is section 34-1033 (see figure 5.3.4), which is located in

a wet freeze environment on a frost-susceptible subgrade (P200 = 23 percent, w% = 8 percent,

FZI = 219°C days/yr (395 of days/yr) , precipitation = 1200 mm/yr (48 in/yr)). This section is

subjected to a low traffic rate of37 KESAL/yr, and the pavement section consists of30 mm (1.2

in) of AC over 155 mm (6.2 in) of base.

Figure 5.3.6 shows the model form developed for this data set along with plots of

measured versus predicted IRI shown as a line plot, and versus pavement age. This model was

based on 161 IRI measurements from 35 sections and had an average error per section of

0.21m1km (13 in/mi). The standard deviation ofthe errors at the sections was 0.17 mIkm (13

in/mi). Traffic rates varied from 4 to 370 KESAL/yr with an average of 107 KESAL/yr for this

data set. Figure 5.3.7 shows the sensitivity of the resulting model to changes in layer thickness,

holding all other parameters constant for a dry environment. Shown in parentheses next to the

parameter name is the range of the parameter encountered in the LTPP data used to build the

model.

The best long-term performer in this data set is section 6-8149 (see figure 5.3.6). This

22- year-old section is located in a very hot and dry-no freeze environment, which has an annual

precipitation of 175 mm (7 in) and 149 days above 32°C (90°F). This is a very-high-traffic (720

KESAL/yr) section with thin pavement layers (AC = 133 mm (5.3 in), Base = 125 mm (5 in)).

The dry climate together with the good subgrade conditions (P200 =20 percent, w% = 5 percent)

may have been the primary factors that contributed to the excellent long-term performance.

The worst performer in the data set is section 5-2042 (see figure 5.3.6). A preliminary

evaluation of this section with the AASHTO design equation indicated that it is approaching the

end of its service life. This section is located in a wet no-freeze environment on a high P200 and

high-water-content subgrade. Section 48-3679 (see figure 5.3.6) is showing a rapid increase in
fRIo = A(ACThick)B + C(W%)D + E(P200( + G(Pot

r

o

= [I(KESAL/ ;l + M(A""Precip)N +O(Days90 %CTlIickJ' +Q(P200· w% ~FrzThwCYC)~ +S(ACThiCkl] x _1_

K(SN) AC17l1ck • Po 1000

250

200

'81.50

.51 -

':100 CII:

-

SO

0

0.0 2.5

A= 175.72255 F=-0.0412061 K=0.0885887 Q=0.2

B=-2.7528761 G=-829.64201 L=30.697898 R= 1.3

C=-302.51141 H=-0.5603349 M=2.557E-08 5=4000

D = -1.9703234 1=20.989881 N= 6.5330335 T=-0.15

E= 123.57391 J =6.6289015 O=3.788E-08 U=l.OI

P=6.7857038 V=200

En'Or Dishibutlon rOl' GPS-2 Cement

250 Aggl'egate Mix Base Model

E

~150

ii

-~IOO

1ll. 50

50 100 150 200

Measured IRI. hllllli

12.5 1S.0 17.5 20.0 22.S 2S.0 27.5

I'avement Age, yr
Paramt'ter (Data Ranae) Case 1 Case 2 Case 3 Case 4 Case 5

KESAL/yr (13-1057) 250 250 250 250 250

SN (3.5-7.5) 5.79 5.80 5.79 5.79 5.79

ACThick, in (1.6-9.9) 3 3.5 4 4.5 5

Po, psf (102-520) 220 217 213 210 206 w" (3·39) 9 9 9 9 9

P200 (9-94) 25 25 25 25 25

AnnPrecip, in (6-60) 10 10 10 10 10

FrzThwCyc (4-158) 12 12 12 12 12

Days90+ (0-159) 155 155 155 155 155

INITIAL IRI, in/mi = 72 69 67 66 64

Growth Rate Constant, r = 19.38 8.93 5.52 4.21 3.64

Cement-Aggregate Mixh e Base GPS2 IRI Model

.-------,

--Case 1

--- Case2

······Case3

_. _. Case4

_ .. - CaseS

o

o

I

10 15

Pavement Age, yr

Figure 5.3.7. Sensitivity of the cement-aggregate base model to changes

in layer thickness, holding all other parameters constant.
IRI at a very early age. This section has a low AC thickness of 40 rom (1.6 in) over a thick base

(210 rom (8.4 in)). The subgrade has a high P200 (66 percent) and a high plasticity index.

Figure 5.3.8 shows the model form developed for this data set along with plots of

measured versus predicted IRI shown as a line plot, and versus pavement age. This model was

based on 47 00 measurements from 14 sections and had an average error per section of 0.13

m/km (8 in/mi). The standard deviation of the errors at the sections was 0.10 mJkm (6.6 in/mi).

Figure 5.3.9 shows the sensitivity of the resulting model to changes in sub grade conditions,

holding all other parameters constant, for a wet-no freeze environment. Shown in parentheses

next to the parameter name is the range of the parameter encountered in the LTPP data used to

build the model. There are very few sections in this data set, and little variation in behavior is

seen between the pavements in this data set.

The best long-term performer in the data set is section 28-3090 (see figure 5.3.8). This

section has extremely low traffic (5 KESAL/yr) with a fairly thick section (SN = 4.6, AC = 63

rom (2.5 in), base = 135 rom (5.4 in)). A preliminary evaluation of this section with the

AASHTO design equation indicated that the pavement section is still within its service life.

The stabilized base types that are in the GPS-2 experiment are cement-aggregate

mixtures, hot mix AC, asphalt-treated mixtures, soil-cement, and lean concrete. Models for

predicting 00 were developed for all these base types except for lean concrete, which did not

have sufficient sections for model building. The models that were developed predict the

development ofIRI over time according to the concept of exponential growth of 00. The

models predict two parameters: the initial 00 and the growth rate. The initial 00 is predicted on

the basis of the structural properties of pavement layers and subgrade parameters. The growth

rate is an exponentially increasing function of time and is a function of structural properties of

pavement layers, subgrade and climate parameters, traffic loadings, and time.

The general trends for GPS-2 sections were similar to the trends observed for GPS-1

sections. Generally, poor performance was observed for GPS-2 sections that had a low total

pavement thickness and were located in the freeze regions and also in cases where they were

utilized over high-moisture-content subgrade. A reduction in the total pavement thickness can be
_[I(KESAL/yrl urWOA)xAnnpreC;pxFZl% ~N 0/" P . ,.1' Q(100.P200.W%)R] 1

ro - L +.IV.l1 10 P + (.-ann reclp/ + x--

K(SN) x Po 1000

A=-38.971342 F=-0.OO15447 K=2 P=-0.3137206

B=0.0927331 0=-14362.11 L=3.5 Q=0.1

C=6.135E-05 H=-0.OOO1067 M=77325.96 R=1

D=4.150747 1=2812.9537 N=0.OO19983 8=1.01

E= 14572.344 J = 1.2901464 0=-257547.04 T=200

ElTOr Distribution for GPS-l SoD

2SO Cement Base Model

• 200

E

l ISO

g

) 100

~ SO

O-f---+----+----f----+-----i

o SO 100 ISO 200

Measured IRI, Inhnl

--1 •MculJledIRI I

a Pled~IRI I

a •

~

•

• 3 a i

a

118 a 08

II •

II .~.~ •

28-3090

aa a

~ "'?

a

-- ~ . •

e ~_ a

:::it • •

". .. •
Parameter (Data Ranle) Case 1 Case 2 Case 3 Case 4 Case 5

KESAL/yr (4-370) 120 120 120 120 120

SN (2.2-9) 3 3 3 3 3

Po, psI (70-300) 130 130 130 130 130

PI (0-11) 0 0 0 4 11

noo (1.4-84) 1.4 20 SO 70 84

wCJ, (4-19) 4 8 12 16 19

AnnPrecip, in (44-63) 48 48 48 48 48

FZI (4-241) ISO ISO 150 ISO ISO

INITIAL IRI, in/mi - 55 60 62 61 64

Growth Rate Constant, r = 15.83 15.95 16.04 16.13 16.19

--Case 1

---Case2

..... Case3

-._oCase4

_ .. -Case 5

'£1200

:5

~150 i

"ir:

10 15

Pavement Age, yr

Figure 5.3.9. Sensitivity of the GPS-2 soil cement base model to changes

in subgrade conditions.
achieved by utilizing a stabilized base as opposed to a granular base or subbase layers. However,

this results in a low overburden pressure on the subgrade, and was considered to be a primary

cause for the poor performance that was described.

The time-sequence IRI values for each GPS-3 site were compiled and plotted against

pavement age. Figure 5.4.1 shows the development ofIRI for the GPS-3 sections classified

according to the LTPP regions. The general trend observed for roughness development over the

typical range of IRI observed is either logistic or exponential growth. Logistic growth is

characterized by a leveling off of the IRI development as the pavement ages. This is often

referred to as S-curve type development. A logistic growth model appears similar to exponential

growth in the early stages but then levels off over time. The general model form used for the

GPS-3 pavements was:

where:

fRio =

fRImax =

=

ro =

=

t =

e

estimated initial IRI (after some initial traffic loading),

f (design properties of pavement and subgrade properties),

estimated long-term IRI,

f (design properties of pavement and subgrade properties),

growth rate constant,

f (climate, traffic, subgrade, and design properties of pavement),

time in years, and

the exponential function.

In this model, fRio represents the IRI value of the pavement after it has been opened to

traffic and has been subjected to some initial traffic loading. GPS-3 sections consist of sections

that have doweled as well as undoweled joints. It is well known that doweled pavements behave

differently than undoweled pavements. In wet regions (annual precipitation greater than 500 rom

(20 in», the data were divided according to the joint type. In dry regions (annual precipitation
250

ZOO

Os

1.50 ]

;- 100

- 50

0

0

250

200

Os

150 ]

;- 100

- 50

0

0

250

200

Os

.] 150

- 100 &:lll

- 50

0

0

250

200

Os

150 :a~ •

-

100 &:lll - • 50

0

0

GPS-3 North Central 4- ~/

~.~~ ..-..:

15 20 Age, yr

GPs-3North Atlantic

~

--' ...•

• ...--........ ~ =6 ..-.

~o~ .:..-:.!_.•..'-~ ~ 0 ••••• ",... • 0.. _ ••..•

....~. • :. ===-- • . . ..•

15 Ag 20

e, yr
less than 500 mm (20 in)), 69 percent of the pavements were undoweled, 13 percent were

doweled, and the joint type was not known for 18 percent of the sections. Sufficient sections

were not available in the dry regions to build a separate model for the doweled sections:

therefore, it was decided to build one model for dry regions by considering all sections. Fairly

good models were obtained for the undoweled pavements in the wet regions and for the

pavements in the dry regions. Modeling attempts were not as successful for the doweled

pavements in the wet regions. The correlations shown in table 5.4.1 reflect these trends.

Wet Regions Wet Regions Dry Regions

Doweled GPS-3 Undoweled GPS-3 (All Joint Types)

Age (+.31) EMOD (+.53) Age(+,41)

Days90+ (-.21) KESAL/yr (-.52) PCCth (+.37)

FrzThwCyc (+ .19) Poissons Ratio (+ .39) JointSpace (+.31)

f, Ib/cyd (+ .18) Comp (+.34) Poissons Ratio (-.25)

cement (+.17) Days90+ (-.34) w% (+.23)

Days.5" + (-.16) P200 (+.33) FrzThwCyc (+ .22)

AnnPrecip (-.14) c, Ib/cyd (+ .33) Po (+.21)

air, % (-.13) w/c (-.31) f, Ib/cyd (-.21)

SplitTensile (+ .12) DaysWet/yr (+ .30) BaseThick (+ .15)

unitwt (-.12) PCCth (+ .27) PL (-.13)

The correlations for the doweled sections located in wet regions, although rather

insignificant, seem to indicate that pavements existing in cooler climates, with higher freezing,

which utilize higher cement content and more fine aggregate will be rougher pavements. This

can be explained in the context of brittleness and frost heave. Higher freezing, of course, means

higher frost heave potential. Mixes with higher cement content and fine aggregate will generally

have more brittle behavior and will suffer more shrinkage resulting in greater joint openings.

This would result in higher dowel-bearing stresses. The joint spacings for the doweled GPS-3

pavements vary from 4.3 m (14 ft) to 9.1 m (30 ft), with an average of 5.8 m (19 ft). Most of

these pavements are shorter than the critical slab length of 6fJ2, where f is the radius of

relative stiffuess ofthe pavement system. For a 229-mm- (9-in-) thick slab on a typical granular

subgrade, this critical slab length would be approximately 8 m (26 ft). As slab length approaches

the critical slab length, bending stresses and deflections from combined curling, warping, and

loads reach a maximum. There is a slight trend toward increasing IRI for increasing slab length

(correlation coefficient of +.11) for the doweled GPS- 3 sections.
The correlations for the undoweled sections show a strong negative correlation to traffic

rates. This is due to the presence of several early-age sections that are very rough after very little

cumulative traffic. These sections will be discussed in more detail later. In general, the trends

show that higher precipitation, with more fines and water in the subgrade, tends to result in

higher IRI values. This is intuitively correct as these trends in climate and subgrade conditions

would tend to increase erosion/faulting potential. There is a slight trend for decreased IRI for

increased joint spacing. The joint spacings for the GPS-3 pavements vary from 4.3 m (14 ft) to

9.1 m (30 ft), with an average of 5.8 m (19 ft). Most of these pavements are shorter than the

cri~ical slab length. Curling and warping effects should increase as slab length increases.

However, it is believed that pavements with very short joint spacing would be more susceptible

to slab rocking from settlement as the aggregate interlock diminishes over time at the joints. The

PCC mix parameters are also strong factors relating to IRI. The trends seem to indicate that

higher water to cement ratios with lower cement contents are resulting in smoother pavements.

This can be explained in the context of workability and early-age curling and warping ofthe

slabs. A very low water to cement ratio mix with very little cement and water would likely not

be a workable mix. Low water to cement ratio mixes with high cement and water contents would

likely generate more heat during hydration causing more extreme temperature gradients at set.

This mix would also gain more strength at a faster rate, perhaps allowing more early-age curling

effects because of less relaxation of stresses and strains during early temperature swings.

However, this type of mix would be expected to be more durable over the long term.

The correlations for the dry regions seem to indicate that pavements that are thicker with

longer joint spacing will have increased roughness. Dry environments would be more likely to

develop significant moisture gradients through the thickness of the slab, perhaps developing

more warping within the slabs. Higher subgrade moisture contents and higher freeze-thaw cycles

per year indicate increased roughness.

Figure 5.4.2 shows the model that was developed for prediction ofIRI for doweled GPS-3

sections in wet climates where annual precipitation is greater than 508 mm (20 in). This model was

based on 136 IRI measurements from 30 sections and had an average error per section of 0.25 mIkm

(16.1 in/mi). The standard deviation ofthe errors at the sections was 0.21 mIkm (13.6 in/mi).

Traffic rates varied from 20 to 950 KESAL/yr with an average of 280 KESAL/yr for this
fRIo = A(W~ x 100l +C(P;::';V+E(JointSpacef +G(W%)H

fRImax = W(PCCth)X+Y(W~ x 100l + AA(P200 xw%lB +CC(JointSpace)DD

r

o

= [I(PCCth)J + K(SpliIT."SI1XO)L + M(EMODAOOOOO)N + O(FrzThwCyc)P + Q(cement)R

+S(Days Wet / yr)T + U(KESAL / yr)"] x ){oooo

A= -6.554E+11 H= 0.08403437 0= 0.45640911

B= -6.8246703 1= 126625.341 P= 1.26188093

c= -8.470891 J= -3.7821891 Q= 0.1663822

D= -0.0022624 K= -252.30118 R= 0.57651795

E= 0.01120562 L= 0.33965894 S= -4.8230202

F= 1.1341688 M= 223.618991 T= 0.96836712

G= 68.9381288 N= 0.53868136 U= -93734.541

v= -1.9594221

w= 1512892.36

X= -5.0282439

y= -0.0001979

Z= 3.21849506

M= 0.00077479

nB= 1.74421201

cc= 7.4137E-05

DD=5.4931273

Error Di.~triblltioll AIr the GPSJ Wet

2.'!O Climate Doweled Model

o

o .50 100 1.50 200 2.50

Meuured IRI, inlmi

250

200

411-3010

] 150 42-3044

.5 • •

-~100

c-= - 50

0

0 5 10 IS 20

Pavement Age, yr
data set. Most of the sections in this data set are less than 20 years old and have IRI values less than

2.37 m/km (150 in/mi). Figure 5.4.3 shows the sensitivity of the doweled GPS-3 model to changes

in climate conditions. Shown in parentheses next to the parameter name is the range of the

parameter encountered in the LTPP data used to build the model.

There was a very large variation in IRI values for sections less than 10 years old. For

example, two sections in Florida, sections 12-4109 and 12-4059 (see figure 5.4.2), have virtually

identical parameters but very different 00. There are thick pavements at both extremes in IRI.

There are pavements that are performing quite well as well as pavements that are performing

poorly that are both located on poor subgrades. There are also sections of lower than average

and higher than average split tensile strength on each extreme. All of these observations shed

light on the reasons why correlations were small and modeling attempts were not as successful

for these pavements as they were for the undoweled sections and the sections in the dry regions.

The model is capturing the general trends but is not predicting the IRI values very accurately.

The following discussion presents a qualitative analysis of the doweled GPS-3 pavement

sections located in wet environments that exist at the extremes in performance. The analysis

looks at high IRI pavements, low 00 pavements, and young pavements that had very different

IRI values. The sections discussed are indicated in the bottom portion of figure 5.4.2.

The two smoothest pavements, which are more than 15 years old are sections 29-5393

and 55-3019. Section 29-5393 was approximately 33 years old at the time it was last profiled,

and is subjected to a very low traffic level of28 KESAL/yr. The section has 9-m (30-ft) joint

spacing, 203-mm (8-in) slab thickness, and 32-mm (1.25-in) dowels resting on a thin granular

base. The subgrade consisted of a wet clay (P200 = 70 percent, w% = 25 percent). The section

is located in the wet freeze environment (precipitation = 950 mmlyr (38 in/yr), FZI=301 °C

days/yr (542 OFdays/yr)), with over 30 days above 32°C (90°F). Section 55-3019 is also a low

traffic section (estimated 62 KESAL/yr), is 19 years old, and has an IRI of 1.1 m/km (72 in/mi).

This section has a 4.6-m (15.1-ft) joint spacing, 224-mm (8.8-in) slab thickness, 25.4-mm (I-in)

dowels, a granular base, and rests on a granular subgrade (P200 = 8.6 percent, w% = 5 percent).

The section is located in the wet freeze environment (precipitation = 825 mmlyr (33 in/yr),

FZI=1266 °C days/yr (2278 OFdays/yr)). The low traffic volumes at these two sections may

have been one reason for the low roughness.
200

~ 150

el~100

Parameter (Data Range) Case 1 Case 2 Case 3 Case 4 Case 5

PCCth, in (6 to 13) 9 9 9 9 9

SplitTensile, psi (486 to 738) 730 730 730 730 730

MOD, psi (3,350,000 to 5,675,000) SSOOOOO SSOOOOO SSOOOOO SSOOOOO SSOOOOO

w/c (0.31 to 0.7) 0.4 0.4 0.4 0.4 0.4

cement, lb (450 to 5(0) SSO SSO SSO SSO SSO

Po, psr (100 to 440) 180 180 180 180 180

noo (1 to 97) 9S 9S 9S 9S 9S

w~ (4 to 28) 2S 25 2S 25 2S

DaysWet/yr (96 to 199) 96 110 130 150 170

FrzThwCyc (2 to 108)= 28 48 68 88 108

Kesal/yr (20 to 950) 650 650 650 650 650

JointSpace, ft (14.5 to 30) 20 20 20 20 20

IRI@2Syr 244 230 208 189 172

INITIAL IRI, inlmi = 75 75 75 75 75

Growth Rate constant = 0.OS2 0.049 0.045 0.040 0.036

JRI(max) = 1634 1634 1634 1634 1634

GPS-3 Wet Regions, Doweled IRI !,9del ..

,,/' .... ...

--Case 1 .",./ ."..... _.--'"

---Case2 ,,/',,/' .., _.... _.-

.•..• Case3 - •.••... _ _.-.-

-·-·Case4 ;..--"':, -.- _.-'

-..~.::=.,,,~~; ~~ ~:~:~:::::. _._.

....--.

o

o 15 20

Pavement Age, yr

Figure 5.4.3. Sensitivity of the GPS-3 (doweled) wet regions IRI model to

changing climate conditions.
Sections 12-4109 and 12-4059 are both located in Florida and have similar parameters

except for the IRI values and base type. These two sections had IRI measurements taken

between the ages of 1 and 5 years. Both sections have low PCC thickness (180 mm (7.1 in) and

162 mm (6.4 in)), respectively, with medium traffic volume (169 KESAL/Yr). The smoother

section (12-4059) has a base that consists of a thin AC layer over a cement-aggregate mixture

whereas the rougher section has only a cement-aggregate mixture. The PCC parameters reported

indicate that the sections used a lower cement content of301 kg/m3(508 lb/yd\ but these values

were higher than all of the other GPS-3 sections in Florida. With the exception of different base

types, it is unclear as to why these similar sections have such different IRI at early ages.

The pavement that shows the highest IRI value in the data set is section 19-3006. This

section consists of a 226-mm (8.9-in) thick slab with 6.1-m (20-ft) joint spacing resting on a 109-

mm- (4.3-in-) thick cement-aggregate mixture base. Although this section has a low PCC split

tensile strength 3403 kPa (494 psi), it has a relatively high elastic modulus and compressive

strength.

Figure 5.4.4 shows the IRI prediction model developed for undoweled pavements in the

wet environment. This model was based on 104 IRI measurements from 24 sections and had an

average error per section of 0.25 mIkm (15.7 in/mi). The standard deviation of the errors at the

sections was 0.18 mIkm (11. 7 in/mi). Traffic rates varied from 25 to 1490 KESAL/Yr with an

average of 410 KESAL/Yr for this data set. The largest errors are associated with the two

sections (55-3009 and 46-3012) that have very rapid IRI development. The model does predict

these sections to have very high IRI growth rates but not quite as high as those observed from the

IRI measurements. Figure 5.4.5 shows the sensitivity ofthe model to changes in traffic rates.

Shown in parentheses next to the parameter name is the range of the parameter encountered in

the LTPP data used to build the model.

The following discussion presents a qualitative analysis of undo we led GPS-3 pavement

sections located in wet environments that exist at the extremes in performance. The analysis

looks at pavements that have remained very smooth over time, pavements that have become very
IR/(t) = ero! [ IR/max x IR/o ]

IRl (ero! - 1) + IR/ o max

fRio = A(Wh x 100l + C(P;~ ;V +E(JointSpacef + G(w%j!

IRImax = W(PCCth)x + Y(JOO x w / c;Z + AA(p200 X W%lB + CC(JointSpace)DD

ro= [I(PCCth)J + KeplitT.nsiIXo)L + M(EMODAooooo)N+ o(FZI)P + Q(Days32-)R

+S(DaysWet / yr)T + U(KESAL / yr(] x ){oooo

A= -0.2480371

B= 1.33362197

c= -826.4683

D= 0.01704857

E= 2.1071304

F= 0.00805606

G= 808.915445

0= -4.0215953

p= 0.76147578

= 0.01759138

R= 2.33383558

s= 0.02570815

T= 1.95067852

U= -3834.3217

v= -0.3173752

w= 7313.61265

X= -2.476341

y= 0.00012248

Z= 3.29949886

AA= 2.8769925

BB= 0.52868067

cc- 0.02633191

DD- 3.01229593

H= 0.0525586

1= 4403.71574

J= -3.5134802

K= -0.0132394

L= 2.38567734

= 0.4798878

N = 1.91694438

EmIr DIstribution ror Ibe GPS-3 Wet

300 RegIons, Vndoweled Model

o

o ~ 100 I~ :lOCI uo 300

Meannd IRI, IaImI

• MeasumI IRI

a Pledi:lied IRI

Oil

i ISO

B 100

a

• I, • i : %. aa a a

• ••••••S3·3812

a

g •

IS

Pavement Age, yr
Parameter (Data Ranae) Ca~e 1 Ca~e2 Case 3 Case 4 CaseS

PCCth, in (8 to 14) 9 9 9 9 9

SpiitTensile, psi (416 to 1014) 5SO 550 550 550 550

EMOD (1000ooo to 6650000) 350ססoo 350ססoo 3SOOOOO 350ססoo 350ססoo

wIt (0.27 to 0.57) 0.51 0.51 0.51 0.51 0.51

Po, psr (120 to 680) 220 220 220 220 220

1"200 (3 to 88) 88 88 88 88 88

wfJ, (3 to 23) 12 12 12 12 12

DaysWet1yr (75 to 180) 80 80 80 80 80

FZl (1 to 1500) 1500 1500 1500 1500 1500

Days32· (8 to 160) 160 160 160 160 160

Kesallyr (30 to 1400) 30 100 500 1000 1400

JointSpace, ft (11 to 20) 20 20 20 20 20

OOO2Syr 155 261 335 351 356

INITIAL 00, Inlml = 63 63 63 63 63

Growth Rate constant, r = 0.048 0.089 0.125 0.135 0.140

IRI(max) = 417 417 417 417 417

250

200

·s 150 :s

8 100

50

0

0

GPS-3 Wet Regions, Undow!l~d .~~I Model __

.:,....0-' .. - _-,.,...

.. ..,::.::::,: .-" .. .. ..•. .,...-..-

.;,:::.. .... ...--

~. .. ...- •.~:~... _ ... ""

:~ .... -,.,... I"..:=s:··· _---

~.~... --- ~!...... --_ ... ~I.. _-

-ct"\.- -:.---

....•.•.•. ~--- _'f:!:--

I

10 15

Pavement Age, yr

Case 1

Case 2

..... Case 3

-·-·Case4

_ .• -Case 5

I

25

Figure 5.4.5. Sensitivity of the GPS-3 (undoweled) wet regions IRI

model to increasing traffic rates.
rough, and pavements that are developing roughness very quickly. The sections discussed are

indicated in the bottom portion of figure 5.4.4.

Two pavements that are old but have low IRI values are sections 13-3018 and 53-3812.

They have traffic levels of280 and 710 KESAL/yr, respectively. Section 53-3812 has a split

tensile strength that is near the upper limit of the split tensile strength data for the GPS-3 data set,

whereas section 13-3018 has a split tensile strength that is near the lower limit ofthe split tensile

strength data for the GPS-3 data set. Section 13-3018 used a low cement content of307 kg/m3

(5171b/yd3) with an estimated water to cement ratio of 0.51. Section 53-3812 used a low water to

cement ratio (0.39) with typical cement content of332 kg/m3(560 Ib/yd3), resulting in very high

split tensile strength. The PCC mix for both sections used granite as a coarse aggregate with a

low fine aggregate content.

Section 55-3009 shows rapid gain in IRI prior to reaching the age of 10 years. This

section is located in the wet freeze environment on a high P200 sub grade that has a PI value just

greater than 10. This section, which has a PCC thickness of215 mm (8.6 in), is subjected to a

traffic level of 312 KESAL/yr. The PCC for this section had a low split tensile strength and a

high modulus, and the brittle nature of this mix may have contributed to pavement distress that

resulted in a high IRI value.

There are two older pavements, sections 39-3013 and 6-3005, which were noted to have

high IRI values. Section 6-3005 is a high traffic route (1101 KESAL/yr) located in a hot, wet-no

freeze environment. This section has a 205-mm (8.2-in) slab, with 4.6-m (l5-ft) joint spacing,

and rests on a relatively high-moisture-content clay (P200 = 51 percent, w% = 17 percent, PI =

21 percent). A preliminary analysis ofthis section using the AASHTO equation indicated that it

is likely to have reached the end of its design life. Section 39-3013 is a low traffic route (l00

KESAL/yr) located in a mild wet freeze environment. This section has a 211-mm- (8.3-in-) thick

slab, 5.2-m (l7-ft) joint spacing, and a soil-cement type base. The sub grade is a high-moisturec~ntent clay (P200 = 83 percent, w% = 21 percent). The PCC offthis section had a low split

tensile strength and a high modulus value. The brittle behavior of the PCC as well as the high

moisture content of the sub grade may have been contributing factors to the high IRI of this

section.
Figure 5.4.6 shows the IRI prediction model developed for GPS-3 sections in the dry

environments. This model was based on 135 IRI measurements from 26 sections and had an

average error per section of 0.21 m/km (13.2 in/mi). The standard deviation of the errors at the

sections was 0.11 m/km (7.1 in/mi). Traffic rates varied from 14 to 1610 KESALlyr, with an

average of 400 KESAL/yr for this data set. Figure 5.4.7 shows the sensitivity of the model to

changes in climate conditions from very dry and hot to very cold and to the upper limit ofthe dry

climatic range. Shown in parentheses next to the parameter name is the range of the parameter

encountered in the LTPP data used to build the model.

The following discussion presents a qualitative analysis of the GPS-3 pavement sections

located in dry environments that exist at the extremes in performance. The analysis looks at

pavements that have remained very smooth over time and pavements that are developing

roughness very quickly. The sections discussed are indicated in the bottom portion of figure

5.4.6.

One notable good performer is section 6-3042. This section has carried an average of

16JO KESAL/yr is 15 years old, and still had an IRI value of 0.95 m/km (60 in/mi). The slab

thickness is 224 mm (8.8 in) and rests on a subgrade with 64 percent P200 and 15 percent

moisture content. The PCC used a cement content of279 kg/m3(470 Ib/yd3) with a water to

cement ratio of 0.61. These parameters combined with high coarse aggregate contents, resulted

in a PCC mix that had a lower modulus (E = 24181 MPa (3500 ksi)) and a higher split tensile

strength (split tensile strength = 5077 kPa (737 psi)).

Section 53-3013 has remained smooth over a long period. This section is located in the

dry freeze environment, rests on a frost-susceptible subgrade (P200 = 37 percent, w% = 12

percent), and has a PCC thickness of 205 mm (8.2 in). This section is subjected to a low traffic

level of 101 KESAL/yr. The PCC is a higher strength, lower modulus type concrete having a split

tensile strength of 6079 kPa (894 psi). The low traffic levels, thick base and subbase layers that
fRIo = A(JointSpacel + C(PCCth)D + E(Pof + G(P200)H

fRImax = S(Jo;ntSpacel + u(P2oof + W{PCCth)x

ro = [I(Days32-l + K(AnnPrec;pl + M(Jo;ntSpace)N + O(C / ./Y + Q(KESAL / yrr J x !1oooo

A= 1.57701354 G= 172.548119 M= 16.4129241 s= 9903.86557

B= 1.3932107 H= -0.7108349 N= 0.11943774 T= -1

c= 0.05750755 1= 1.3816E-05 0= -234.52103 U= 0.00713641

D= 2.96098324 J = 3.38379753 p= 0.11918564 V= 2.73962869

E= -76.984327 K= 218.96547 Q = -7986.4536 W= -124.32398

F= -0.0728376 L= 0.2534601 R=-1.002 x= 0.65113559

Distribution of Errors for the

Dry Regions, GPS-J Model

·s ISO

:s

~ 100

lll. 50

50 100 150

Me •• ured 1RJ.lnhni

• •

•

, a

a

56-3027

1I!I-3802 • S

a

"

a • •

a 0

•

• ••

• •

• o 0

•

~ te~a a a

_:.. g

a _ a aa 0

.: •••:0 ~ • ft II a

a

g o • ~. 0

o ~J j, a~ ~. ~ .• ~ •• a

I!.!. •• • ••• • • •

ii)

• 0 aD

a a~ "ita o oJ q.a. a ~a'" a

II ~ a a. ~8 S3-3013

S idl p" J: •. .-a 0 o o. •

6-30012 I

o Mea.ured JRI

a PJedicted JRI

·5

]100

g

o

0.0 10.0 12.5 15.0

I'avement Age, yr
250

200

Os ISO :s

;- 100

- ..-:

SO

,

0

0

Parameter (Data Ram!e) ClL'Ie1 Case 2 Case 3 Case 4 Case 5

PCCth, In (8 to 115) 9 9 9 9 9

elI,lbllb (1.13 to 2.17) 1.65 1.65 1.65 1.65 1.65

Po, ,., (110 to 590) ISO 150 150 150 150

P200 (6 to 93) 50 50 50 50 50

AnnPredp, In (4 to 20) 4 8 12 16 20

DlI)'132- (0 to 203). 1 50 100 150 200

JoIntS.- •• ft (I 1.5 to 15) 13.5 13.5 13.5 13.5 13.5

DSAL1)'1o (14 to lAG) 100 100 100 100 100

IRI015yr 56 64 82 140 313

INITIAL IRI, inlml = 55 55 55 55 55

Growth Rate constant, r = = 0.000 0.007 0.018 0.045 0.100

IRI(max) = 536 536 536 536 536

--CaIOI

----CaI02

·······CaI03

-'-'-CaI04

-..-. r•.••'1

GPS-3 Dry Regions. IRI Model

,.oil'"

, •• t1"

",.. . .

,..",. -''-.-'-'-'-

,.

.. ," " _.-._..-.-"

,..,... '-'-'-'--

" .-.-.- ...",.. ..,.. _._"- , _._._._.-.. . .

.-:;:~:::-.:::".:::::....':.::.::.::::::.::.::::::::'::::::::: :::.::::::::::'':':~''::.~------------------------

IS 20

Pavement Age, yr

Figure 5.4.7. Sensitivity of the GPS-3 dry regions IRI model to changes

in climate, holding all other parameters constant.
minimize frost heave effects, and the good PCC properties may have contributed to the excellent

behavior of this section.

Section 56-3027 is shown to have rapidly developing IRI. This section has been

subjected to 505 KESAL/yr, and the sub grade is very similar to that of a section discussed

previously (53-3013). However, the freezing index at this location is much higher.

One pavement section that is apparently on a rapid course toward failure is section 89-

3802. The IRI for this section has increased from 1.89 m/km (120 in/mi) at age 4 years to 2.84

mflan (180 in/mi) at age 8 years. This section is located in a severe dry freeze environment

(precipitation = 463 mm/yr (18.5 in/yr), FZI=1887 °C days/yr (3396 OFdays/yr), days

wet=106». This section is located on a very wet clay subgrade (P200 = 91 percent, w% = 34

percent, LL = 74 percent, PI = 41 percent). The section has a 249-mm (9.8-in) thick slab, 4.6-m

(15-ft) joint spacing, and is subjected to 115 KESAL/yr. The PCC used limestone coarse

aggregate with a relatively high fine aggregate content, and the cement content was 326 kg/m3

(550 Ib/yd3) with a water to cement ratio of 0.4. The severe environment coupled with the

subgrade type may be a cause of the high IRI at this section.

The models developed for the GPS-3 sections predict the development ofIRI over time

according to the concept oflogistic growth ofIRI. The models predict three parameters: the

initial IRI as a function of design parameter of the pavement and subgrade parameters, a

maximum long-term stable value of IRI as a function of design parameters of the pavement and

subgrade parameters, and the growth rate constant that is a function of the design parameters of

the pavement, subgrade, climate, and traffic parameters. The doweled and undoweled GPS-3

models for the wet region both have a mean error per GPS-3 section of 0.25 m/km (16 in/mi).

The GPS-3 model for the dry regions has a mean error per GPS-3 section of 0.21 m/km (13

in/mi). Figure 5.4.8 shows the strong relation between IRI and the total amount of faulting

observed at the undoweled GPS-3 sites. There are some indications that the relative values of the

split tensile strength and elastic modulus in a PCC mix may have an effect on the IRI

development of the pavement. However, this phenomenon needs further investigation.
:t GPS-3 Undowelled Sections

• •

.... • •

~

150 • • • •

•

• I

....

100 • • g

• .~.~. • y = 0.6221x+ 80.351 • • • 50 ,. • R2 =0.4TI5

o -I I I I I I I I I I

0 20 40 60 80 100 120 140 160 180

Total fauhing per LTPP section, rnm

1 inlmi = 0.0158 mIkm

Figure 5.4.8. Relationship between IRI and total faulting for undoweled GPS-3 sections.

The time-sequence IRI values for each GPS-4 site were compiled and plotted against

pavement age. Figure 5.5.1 shows the development ofIRI for the GPS-4 sections classified

according to the LTPP regions. There are no GPS-4 sections in the western region. The general

trend observed for roughness development for GPS-4 sections is exponential growth. The

general model form used for the GPS-4 pavements is described below:

where:

fRIo =

=

ro =

=

t =

e =

A andB =

estimated initial IRI (after some initial traffic loading),

f(design parameters of pavement and subgrade properties),

growth rate constant,

f(climate, traffic, sub grade properties, design parameters of the pavement),

time in years,

the exponential function,

modeling constants.
l ISO

Bl00 ~.

~

~---..--:=-- ~. -~

1 ISO

:s

Bl00

·iI ISO

:s

B 100

•..- -~..'~- ~

•• " •. ,I ~ .• A...-f"Y--. --"'--=~. . _ ~.- I ~

250

200

oil ISO :s

B

100 .

.

SO

0

0

-- North Central

-- North AUantic

/ ~ --Southern

~~ ~ -- .

~p~.~ ....

1 in/mi = 0.0158 mlkm

Figure 5.5.1. Trends in IRI development for the GPS-4 sections

according to LTPP regions.
In this model, fRIo represents the IRI value of the pavement after it has been opened to

traffic and has been subjected to some initial traffic loading. Because of the relatively small

number of GPS-4 sections, only one model was generated for the entire experiment. There were

a total of 68 pavement sections with a total of 285 IRI values. Traffic rates at GPS-4 sections

varied from 34 to 1610 KESAL/yr with an average of350 KESAL/yr. Table 5.5.1 shows the ten

parameters that had the highest correlation coefficients with 00.

Parameter Correlation

Coefficient

water -0.51

AnnPrecip 0.38

Days32- -0.36

.PCCth 0.34

cement -0.33

FZI -0.32

JointSpace 0.26

w/c -0.24

w% 0.23

EMOD -0.22

The correlations, although rather insignificant, seem to indicate that higher precipitation

with more fines and water in the subgrade tend to result in higher IRI values. This is intuitively

correct, as these trends in climate and subgrade conditions would tend to increase

erosion/faulting potential. There is a slight trend for higher IRI for increased joint spacing. An

increased joint spacing would likely result in a greater proportion of the transverse

discontinuities (cracks and joints). This could increase the faulting potential over time.

The joint spacings for the GPS-4 pavements vary from 4.6 m (15 ft) to 30.5 m (100 ft),

with an average of 15 m (49 ft). Most of these pavements are longer than the critical slab length

of 6f.J2, where f. is the radius of relative stiffness of the pavement system. For a 229-mm- (9-

in-) thick slab on a typical granular subgrade, this critical slab length would be approximately 9

m (30 ft). As slab length approaches the critical slab length, bending stresses and deflections due

to combined curling, warping, and loads reach a maximum.
The PCC mix parameters are among the stronger factors relating to IRI. The trends seem to

indicate that higher water to cement ratios with higher cement and water contents are resulting in

smoother pavements. This can be explained in the context of workability and early- age curling

and warping of the slabs. A concrete mix with a very low water to cement ratio and very little

water would likely have workability problems. Low water to cement ratio mixes with high cement

and water contents would likely generate more heat during hydration causing more extreme

temperature gradients at set. This mix would also gain more strength at a faster rate, perhaps

allowing more early-age curling effects because of less relaxation of stresses and strains during

early temperature swings. However, this mix would be expected to be more durable over the long

term.

Figure 5.5.2 shows the model developed for IRI prediction for the GPS-4 sections. The

average error per section for the model was 0.16 m/km (10.3 in/mi). The standard deviation of the

errors at the sections was 0.11 m/km (10.3 in/mi). Figure 5.5.3 shows the sensitivity of the

resulting model to changes in slab thickness, holding all other input parameters constant. The

sensitivity analysis indicates that, as slab thickness increases, the initial value ofIRI increases but

deterioration rates decrease. This indicates an optimum thickness in the 229 to 254 mm (9 to 10

in) range for the climate and traffic conditions shown. Shown in parentheses next to the parameter

name is the range of the parameter encountered in the LTPP data used to build the model.

The following discussion presents a qualitative analysis of the pavement sections that exist

at the extremes in performance. The analysis looks at high IRI pavements, low IRI pavements, and

old pavements that have carried very high traffic volumes and still have acceptable IRI values.

The sections discussed are indicated in the bottom portion of figure 5.5.2.

Two sections, 54-4004 and 48-4152, are identified as having developed high IRI values at a

relatively early age. Section 54-4004 is located in the wet freeze zone, and experiences 192 days

with precipitation per year, which is the highest for the data set. The pec slab thickness is 251

mm (9.9 in) and the slab rests on a granular base. This section has a high traffic volume (717

KESALlyr); a preliminary analysis of this section with the AASHTO pavement design equation

indicated that it is likely to have exceeded its design life. Section 48-4152 is located in the wet-no

freeze zone, and is subjected to an annual precipitation of 1500 mm (60 in), with 34 days/yr
fRio = A(PCCthl +C(JointSpacel + E(J+ PLf +G(PO)H+frw!cl +K(w%l

r. = M(PCCth)N + O(AnnPrecip~' + Q(W/)R + S(P200 x w% f + U(KESAL / yr / )v + W(BMOP)X + y(C •••,)Z

o /c 100 /10 J()()()(J() J()O()

A= 1.056E-06 H= -1.68577 0= 6.32E-I0 v= -0.8

B= 7.2505499 1= -997.43269 p= 7.638408 W= -2.309E-05

c= 1038.5954 J= 0.09401 Q= 118140340 x= 5.00354

D= -0.01149 K= 0.04133 R= 12.46117 Y= -1.91067

E= -0.00179 L= 1.99116 s= -19329.546 Z= 1.678449

F= 2.73034 M= 14669026 T= -0.0906444 AA= 0.51907

G= -52333.3 N= -2.49805 u= -50000 BB= 43.131

Error Distribution for GI»S-4

IRIModel

50 100 150

Measured JRI, inlmi

's 150

:s

~ 100

~

~

~ 50

250

• Measured IRJ

200 D Pred.,ted IRJ

A A

•

• D

'E! 150

.51 - D •

;- 100 • __ D_~~~~.

-

• • ••. w:

SO

0

0

5·3074

39-4031

15

Pavement Age, yr
EM

Compressl

Parameter(Data Ranae) Case 1 Case 1 Case 3 Case 4 Case 5

KESAUyr (34 to 1600) 250 250 250 250 250

PL (0 to 14) 0 0 0 0 0

noo (1 to 98) 3 3 3 3 3

~ (1 to 18) 3 3 3 3 3

Po, pst' (135 to 335) 100 100 100 100 100

JolDtSpace, It (15 to (1) 20 20 20 20 20

PCCtb, ID(7.' to 11.4> 7 I 9 10 11

ADDPredp (16 to (0) 50 50 50 50 50

w/c (.1' to .71) 0.55 0.55 0.55 0.55 0.55

OD, psi (3.5e+06 to 6.35e+06> 4500000 4500000 4500000 4500000 4500000

ve Streaatb, psi (4000 to 11000) 6000 6000 6000 6000 6000

INrnAL IRI, IDIml- 40 42 47 57 76

Growth Rate CoDStaDt,r - 15.43 12.21 10.13 1.73 7.74

-_::

--::=- •.• -

----:.•.-- --- .•..•..•. ~~~-

-------- ...----- -~~~~~~

--- ---- =::::::::;;;;;~~~~:-::-::-:._-

.::::=--: - ---- .

- -:= ••-:-:.... -..-

- -- ~~:::~:-::-=. .... -

.•.-- ...-",.. ...•...•.•.. •..- ..".,. ...

--Cue 1

---Cue2

..--_·Cue3

----Cue4

----Cue5

o

o 15

Pavement Age, yr

1 inlmi = 0.0158 mIlan

Fig 5.5.3. Sensitivity of GPS-4 IRI model to changes in PCC thickness,

holding all other parameters constant.
having a precipitation greater than 12.5 mm (0.5 in). This section has low traffic (79

KESAL/yr), a cement-aggregate mixture base, and a slab thickness of290 mm (11.4 in).

The two smoothest sections noted in the GPS-4 data are sections 27-4055 and 29-5473.

Section 27-4055 is a 226-mm- (8.9-in-) thick slab with a thin granular base resting on a

coarse-grained subgrade (P200 = 10 percent, w% = 5.5 percent). The section experiences a

medium traffic level (577 KESAL/yr) and has 8.2-m (27-ft) joint spacing. The annual

precipitation is 737 mm (29 in), with a very high freeze index (1151 °C days/yr (2071 of

days/yr». The PCC mix had a high coarse aggregate content and lower than average cement

content which resulted in a relatively low modulus and high split tensile strength mix that was

very dense. The combination of good subgrade and good PCC may have been one factor that

resulted in exceptional performance of this section. Section 29-5473 is a 201-mm (7.9-in) thick

slab with relatively high traffic (749 KESAL/yr). This section has a thin granular base and has a

18.7-m (61.5-ft) joint spacing. The sub grade has a P200 of70 percent and a moisture content of

15 percent.

Sections 5-3074 and 39-4031 were identified as having supported heavy traffic volumes

while maintaining low IRI values. Section 39-4031 is located in the wet freeze zone, and has

carried an estimated 1600 KESAL/yr over its 30-year life. The section is a 234-mm- (9.2-in-)

thick slab with 18.3-m (60-ft) joint spacing with a granular base and a subbase. The sub grade

has a P200 of 64 percent and a moisture content of 12 percent. The PCC mix had a cement

content of362 kg/m3(611Ib/yd3) and a water to cement ratio of 0.51. Laboratory test data for

the PCC are not available at this section. Section 5-3074 is located in the wet-no freeze zone, and

has carried an average of 1100 KESAL/yr over it's 25-year life. The section consists of a 259-

mm-(1O.2-in-) thick slab with 13.7-m (45-ft) joints and third point hinge joints at 4.5 m (15 ft).

A 172-mm- (6.8-in-) thick cement-aggregate mixture base was used for this section. The

subgrade consists of silt at 22 percent moisture content and 76 percent P200, which exists in a

wet-no freeze environment. The PCC mix has a split tensile strength of 5385 kPa (805 psi),

which is in the upper 5 percent ofthe GPS-4 data set, but had a relatively low elastic modulus.

The models developed for the GPS-4 sections predict the development of IRI over time

for jointed reinforced concrete pavements according to the concept of exponential growth ofIRI.

The models predict two parameters: the initial IRI as a function of design parameters of the

pavement and subgrade parameters, and the growth rate constant, which is a function of the
design parameters of the pavement, subgrade properties, climate parameters, and traffic loadings.

The model has an average error per GPS-4 section of 0.16 m/km (10.3 in/mi). Generally, most

of the sections that were performing well were resting on granular bases.

The IRI versus pavement age plots for continuously reinforced concrete pavements are

shown in figure 5.6.1. These plots show that IRI remained relatively constant over time for most

sections. Some sections showed some variability in IRI over the years, with the IRI for a specific'

year being either higher or lower than the previous and subsequent years. The pavements that

were young (less than 5 years) as well as pavements that were old (over 20 years) showed the

same IRI behavior pattern, which is a relatively constant IRI over time. This observation

indicates that the CRC pavements appear to generally maintain their initial IRI over a long period

oftime.

To analyze the relationship between the current IRI and parameters that could affect

roughness, the GPS-5 sections were divided into three data sets based on the environmental

regions: dry (10 sections), wet-no freeze (36 sections), and wet freeze (39 sections). As only a

few sections were in the dry freeze and dry-no freeze regions, they were combined into one

category. Table 5.6.1 shows correlation coefficients between IRI and selected parameters for the

different environmental regions. Consistent trends in correlations were observed for all three

regions for percentage steel, elastic modulus ofPCC and water to cement ratio. Low IRI values

were associated with high percentages of steel and high water to cement ratios. Pavements with

higher PCC elastic modulus had higher IRI values. The number of days above 32°C (90 OF) was

a s~rong climate factor for the wet-no freeze and dry regions that indicated higher IRI.

The analysis approach used in developing an IRI prediction model was multiple linear

regression. The model to predict IRI for GPS-5 sections was developed by considering 70

sections for which data were available. The model developed for GPS-5 sections is:

Adjusted R

2

= 0.14, standard error = 27 and n = 70
250 GPS-5, North Central

200

'E1S0

55-5040 18·5043

- • ..... :e ~

..-..

• • ..-----' 0

;100

•......•

...~If:!;;:§&?: '. .~, •

0 .----- ..• -

..... • • • •

50 • 31·5052 46-5020

X1-5076

0

,

,

0 5 10 15 20 25 30

Age, yr

250 GPS-5, North Atlantic

200

~50

51-5008

...-.::..... o••••• :E:+ __ : .•

;too -.....----. • • ••••• I .~

-

~l

.••••,~. ___.IOJ~

50 ~-.:t ~ I •

37-5037 51-2564

0 ,- .,

0 5 10 15 20 25 30

Age, yr

250 GPS-5, Western

200

]150

16-5025

.5 -

. I .-/

;100 • I

• •

•

- 50 --.-...

~'I

• .••• 0 • :~

41-5008

0 I I I I I

0 5 10 15 20 25 30

Age, yr

250 GPS-5, Southern

200

48·5024 1150

48-5284 .48-3779

~~o 0 o~

• • •

•.. i . . •

•

..---. .t. = ~-- ci100 • 0 • - :. ~

:

p,~, ,

• .7 - .:+=$# 0 0

• --.

50 •

0 • •

0

0 5 10 15 20 25 30

Age, yr

Figure 5.6.1. IRI development for the GPS-5 sections cJassifed

according to LTPP geographical regions.

139
Dry Wet-No Freeze Wet Freeze

Environments Environments Environments

water (-.80) w/c (-.71) EMOD (+.53)

w/c (-.65) Days90 + (+ .49) Comp (+.45)

elf (-.63) DaysWetlyr (-.41) w/c (-.41)

Cum traf (+ .62) FrzThwCyc (-.39) BaseThick (+ .38)

c (+.57) water (-.36) PL (+.36)

BaseThick (-.52) BaseThick (-.33) water (-.31)

Days90 + (+ .43) %Steel (-.33) cement (+ .28)

PCCth (-.41) Poissons Ratio (+ .27) w% (+.28)

%Steel (-.23) Comp (-.27) DaysWetlyr (+.21)

EMOD(+.08) EMOD (+.23) %Steel (-.21)

where:

IRI =

WCR =

CUMT =

PSTEEL =

current IRI (in/mi),

water to cement ratio,

cumulative KESAL (1000's ofESALs), and

percent longitudinal steel (%).

Attempts were made to develop separate models for each environmental region.

However, such attempts were not successful. The time versus IRI plots for the GPS-5 sections

show a nearly constant IRI over time for virtually all the GPS-5 sections. This behavior is seen

for pavements that are young (less than 5 years) as well as for sections that are old (over 20

years). These plots do not reveal any trends in IRI development for GPS-5 sections. It appears

that the GPS-5 sections maintain a relatively constant IRI over their service life. The initial IRI

of the pavement may be related to workability of the PCC, construction procedures, and the

weather conditions at the time of placement ofPCC. The cause of the low R

2

of the above

relationship is most likely because these factors were not considered in the regression analysis.

The characteristics of two pavements that have low IRI values and two pavements that

have high IRI values are described in this section. These sections are indicated in figure 5.6.1.

The average traffic rate for the GPS-5 data set was 456 KESAL/yr. There were several GPS-5

sections that were old (over 20 years) but still had low IRI values. Two such examples are

section 41-5008, which is 22 years old and has an IRI of 0.95 m/km (60 in/mi) and section 46-

5020, which is 20 years old and has an IRI of 0.98 m/km (62 in/mi). Section 41-5008 is located
in the dry freeze zone and is a high traffic route with an average of 566 KESAL/yr. The PCC

slab is 205-mm (8.1-in) thick and the base is a cement-treated base, with the sub grade having a

P200 of 11 percent and a moisture content of 10 percent. Section 46-5020 is also located in the

dry freeze zone, and is a low traffic route with an average of 43 KESAL/yr. The PCC slab is

200-mm (7.9- in) thick and the base is a hot mix asphalt, with the subgrade having a P200 of 21

percent and a moisture content of 9 percent.

Two sections that have high 00 values and are less than 12 years old are sections 48-

5284 and 48-5024. Both are located in Texas and are in the wet-no freeze zone. Both sections

are located in areas that have a hot climate with days above 32°C (90°F) of 89 and 111 for

sections 48-5284 and 48-5024, respectively. Section 48-5284 has an 00 of2.4 mIkm (153

in/mi) and is 7 years old, while section 48-5024 has an 00 of2.55 mIkm (162 in/mi) and is 11

years old. Section 48-5284 has a PCC slab thickness of 278 mm (11.1 in) resting on an asphalttreated base, and is located over a subgrade that has a P200 of 41 percent and a moisture content

of 16 percent. Section 48-5024 has a PCC slab that is 278-mm (11.1-in) thick that rests on an

AC-treated base and is located on a subgrade that has a P200 of 24 percent and a moisture

content of 12 percent. The traffic levels at sections 48-5284 and 48-5024 are 219 and 139

KESAL/yr, respectively.

A multiple regression analysis that was conducted on this data set developed a model for

predicting the current 00 of the pavement, but the model has a very low R

2

• Since all CRC

pavements are maintaining a relatively constant 00 over the observed period, the roughness

observed on these pavements is likely to be associated with the initial 00 of the pavement. The

initial 00 of the pavement is probably related to factors that are associated with the conditions at

the time of construction, such as workability of PCC, finishing procedures, and weather

conditions during placement. These factors were not included in the regression analysis, and are

likely to be the cause of the low R

2

value. The correlation analysis indicated that in all climatic

regions, the percentage oflongitudinal steel, water to cement ratio, and elastic modulus ofPCC

are related to the 00 of the pavement. Higher percentage of longitudinal steel and water to

cement ratios generally result in a lower 00, while higher PCC moduli result in a higher 00.
Experiments GPS-6A and B, GPS-7 A and B, and GPS-9 in the LTPP program involve

the study of overlaid pavements. Experiment GPS-6A and B is the study of AC overlays of AC

pavements, while GPS-7A and B is the study of AC overlays ofPCC pavements, and GPS-9 is

the study ofunbonded PCC overlays ofPCC pavements. Test sections for GPS experiments 6A,

7A, and 9, were established on in-service pavements, and the condition of the pavement prior to

the overlay as well as the IRI of the pavement immediately after the overlay is not known for

these sections. GPS-6B and GPS- 7B experiments include sections that were overlaid after the

beginning of the LTPP program, and the condition of the pavement prior to the overlay is known

for these sections. Laboratory test data for overlaid sections in the NIMS and RIMS were less

complete than the data that were available for GPS experiments 1 through 5. For the overlaid

sections, various laboratory test data were missing for several sections.

The roughness development at the overlaid sections will be affected by applied traffic

loading as well as climatic conditions. The condition of the pavement prior to the overlay as

well as the amount of repairs performed on these sections prior to the overlay are expected to

have a large influence on roughness development at these sections. However, the condition of

the pavement prior to the overlay for these sections is not known except for the GPS-6B and

GPS-7B sections. The increase in the IRI of a pavement after it is overlaid will be influenced by

traffic, structural condition of the pavement, subgrade conditions, and climatic conditions. The

current roughness of the pavement will depend on the initial roughness of the pavement and the

increase in roughness caused by the described factors. Attempts to relate the current roughness

of the pavement to the factors that would have an affect on the increase in roughness would not

result in accurate models, as these factors are only related to the change in roughness. Predicting

the initial IRI of overlaid pavements with use of an approach similar to that used for GPS-l

experiments will not provide realistic results for overlaid pavements. This is because the initial

IRI of an overlaid pavement can vary depending on construction practices, such as milling prior

to overlay, and the amount of repairs performed on the pavement prior to the overlay.

In the study of the overlaid pavements, the rate of increase of roughness at the sections

was determined by using the time-sequence IRI data and performing a least square linear

regression between IRI and time. The rate of increase ofIRI obtained from the regression was

then related to the possible factors that would cause an increase in IRI to see if significant factors
could be identified. The following sections describe the analysis that was carried out for GPS

experiments 6, 7, and 9.

The GPS-6 experiment is the study of AC pavements that are overlaid with AC. The

GPS-6 experiment is divided into two categories: GPS-6A and GPS-6B. The GPS-6A sections

were established on existing overlaid pavements that were at different periods of their service

life. The GPS-6B sections were overlaid after they were accepted to the LTPP program, and the

IRI of these sections immediately after the overlay is known.

There are 37 GPS-6B sections in the LTPP experiment for which profile data are

available. Nineteen of these sections were profiled prior to being overlaid, and the IRI before the

overlay are available for these sections. Figure 5.7.1 presents the IRI before the overlay, the IRI

after the overlay, and the overlay thickness for these 19 sections. In this figure, the overlay

thickness are arranged in ascending order. There are several cases, as shown in this figure, where

the IRI before and after the overlay are very close to each other. The overlay date for the

sections was determined by reviewing both the assign date (from data table experiment section in

NIMS) and the overlay date indicated in the rehabilitation file. This overlay date was compared

with the profile date for the section to determine if the profiling was performed before or after the

overlay. For the sections where the IRI values before and after the overlay are approximately

equal, there is a possibility that the overlay date in the database is incorrect and both these IRI

values were values that were obtained after the section was overlaid. The data shown in figure

5.7.1 indicate that, in some cases, a substantial reduction in IRI can be obtained by a thin AC

overlay.

As described before, there are 37 sections in the GPS-6B experiment, and the IRI before

and after the overlay is known for 19 of these sections; for the other 18 sections only the IRI after'

the overlay is known. Therefore, there is a total of 37 sections for which the IRI after the overlay

is known. The IRI after the overlay for these sections was analyzed to determine the distribution

of the IRI values after overlay. The IRI of these sections ranged from 0.42 to 1.80 m/km (27 to

114 in/mi), with an average value of 0.95 m/km (60 in/mi). The frequency distribution of the IRI
220

200

180

160

.- 140

~ 120

;. 100

i 80

60

40

20

o

III T

•

T ••

• • •• •

• J. III •

• i 1

..•.

• I I

- I

I

• Before Overlay

• After Overlay

q ~ ~ ~ ~ ~ 0 0 0 N • ~ ~

~ ~ ~ ~ ~ ~ N N N N N N N

Overlay Thlckne •• (In)

1 in/mi = 0.0158 m/km

Figure 5.7.1. 00 before and after overlay for GPS-6B sections.

of these sections after the overlay is shown in figure 5.7.2. As seen from this figure, the 00 of

the overlay varied within a wide band; however, 73 percent of the 00 values were less than 1.10

m/km (70 in/mi). The factors that influence the 00 value of an overl~d pavement include: (1)

the wavelength distribution in the pavement prior to the overlay, (2) the thickness of the overlay,

(3) construction procedures, and (4) capability of the contractor.

Figure 5.7.3 presents the 00 versus age of pavement (after overlay) plots per region for

the GPS-6B sections. This figure shows that virtually all sections maintain a relatively constant

00 over time. The age distribution of the sections shown in figure 5.7.3 indicate that 89 percent

of the sections were less than 5 years old, with 45 percent of the pavement sections being less

than 4 years old. Twenty-seven GPS-6B sections have been profiled three or more times. The

statistical analysis described in section 5.1 of this report indicated that there was a statistically

significant, positive linear relationship between 00 and age of overlay for only six sections. At

10 sections, the 00 at the last profile date was less than the 00 at the first profile date. Because

only a small number of sections showed an increase in 00, a meaningful analysis to statistically

quantify the change in 00 cannot be carried out yet for the GPS-6B sections.
10 90%

9 80%

8 70%

7

60%

~ 6

c 50% Q)

:;, 5

40% _Frequency

~ 4

u. 30% ___ Qmulative %

3

2

20%

1 10%

0 0%

~

0

~

~

0

~

8 8

0 0 0

~ ~

•.. C'l C'l

0 0 &1 •.. •.. •.. •..

C'l .., •... 0 8

0

A

0) •.. •.. •..

IRIAfter Overlay (inIm)

1 in/mi = 0.0158 m/km

Figure 5.7.2. Frequency distribution ofIRI after overlay for GPS-6B sections.

In the LTPP program, GPS-6A sections were established on in-service pavements, and

the IRI of the pavement immediately after the overlay is not known for these sections. The

overlay thickness for these sections are recorded in an inventory file in the NIMS database, but

were not available for many of the sections. Figure 5.7.4 presents the IRI versus pavement age

(after overlay) relationship for the GPS-6A sections. The date when a section was overlaid is

indicated by the assign date in the database file called experiment section. In the Western region

it was noted that all GPS-6A sections had a similar assign date. As the age of the pavement after

overlay was computed on the basis of this date, the pavement age shown in figure 5.7.4 for the

sections in the Western region may be incorrect.

As seen in figure 5.7.4, some sections showed a relatively constant IRI with time, but

some sections were exhibiting an increase in IRI with time. Fifty-seven GPS-6A sections had

been profiled three or more times, and the statistical analysis described in section 5.1 of this

report indicated that 24 sections showed a statistically significant linear increase in IRI with time.

The slope from the least square regression indicates the rate of increase of IRI for each section,

and the frequency distribution of the slopes for all GPS-6A sections are presented in table 5.7.1.
GPS-6B, North Central

200

'. 150

l 100 - g ;;;;S- o .

: J?' 0

50

i

I 4

0

0 1 2 3 4 5 6 7 8

Age After Overlay, yr

GPS-6B, North Atlantic

200 ---...-. .. .____a 0

'E1

150

I

.

l

0 I

0 I

100

;

- SO

-=::::::::::: I .?+ . -

0 I I I I I I

0 1 2 3 4 S 6 7 8

Age After Overlay, yr

GPS-6B, Southern

200

150

'E1

l 100 . - ; - 0

-

.

•••

.

SO I ·t . ,. . .

0

0 1 2 3 4 S 6 7 8

Age After Overlay, yr

200

GPS-6B, Western

150

's

:s 100 .

--llIl --' •

-

10 • . i .

SO .. . . •. -

0 I I I I I I

0 1 2 3 4 S 6 7 8

Age ARer Overlay, yr

Figure 5.7.3. Trends in IRI development for the GPS-6B sections.

]46

The correlation coefficients between the slope and a selected group of parameters that

could affect the rate of development ofIRI are presented in table 5.7.2. Correlations considering

all GPS-6A sections as well as considering those that have a slope greater than 2 in/mi/yr (0.03

m/km/yr) are shown in this table. When performing the correlation analysis for all sections, the

slope of the sections that had a negative slope was set to zero. Correlation coefficients are

affected by data distribution, and the cause of some illogical correlations in table 5.7.2 were

caused by such occurrences.

When all sections were considered, the highest correlation of the slope occurred for

minimum surface modulus (-0.32), which was obtained from deflection data. When the sections

that showed a slope greater than 0.03 m/km/yr (2 in/mi/yr) were considered, the highest

correlation coefficient (-0.49) was with the SN estimated from thickness of the pavement layers.

Multiple regression analysis was performed for these two cases separately by considering the

slope as the dependent variable. When all sections were considered in the analysis, the following

equation was obtained:

Slope = 5.26 + 0.03 * Frzthaw - 1.566 * 10 -4* Minsur (5.7.1)

R

2

= 0.30, Standard Error = 3.26, n = 44

where:

Slope = rate of increase ofIRI (in/mi/yr),

Frzthaw = number of freeze-thaw cycles/yr, and

Minsur = minimum surface modulus (psi).
nnua reclp a on

Average Annual Days Below 32 of

Average Annual Days Above 90 of

Average Annual Wet Days

Average Annual Freeze-Thaw Cycles

Average Annual Freeze Index

Percent Passing No. 200 Sieve for Subgrade

Subgrade Moisture Content

Liquid Limit of Subgrade

Plastic Limit of Subgrade

Plasticity Index of Subgrade

Annual KESALs

Base Thickness

Structural Number

AC Thickness

Minimum Surface Modulus

Dynamic Stiffness Modulus

Ions

Sections With

Slope> 2

in/mi/yr

0.27

0.07

-0.24

0.30

0.14

-0.14

-0.25

-0.19

-0.28

-0.09

-0.04

-0.09

-0.17

-0.13

-0.32

-0.38

0.07

0.07

-0.20

0.13

-0.02

-0.32

-0.39

-0.47

-0.48

-0.37

-0.26

-0.10

-0.49

-0.44

-0.30

-0.28

When only the sections that showed a slope greater than 0.03 m/km/yr (2 in/mi/yr) were

considered in the analysis, the following equation was obtained:

Slope = 11.15 - 0.96*SN

R

2= 0.19, standard error = 3.09, and n = 31

where:

SN = structural number.

Characteristics of two sections that have similar traffic levels but different behavior, as

well as two sections that have low IRI values are described in this section. These sections are

noted in figure 5.7.4.
There were two GPS-6A sections 1-6012 and 1-6019 that were noted to have carried an

estimated 1050 KESAL/yr over their 10 to 13 years of service after overlay but had different IRI

behavior (see figure 5.7.4). Both these sections are located in high precipitation environments

with very minimal freeze-thaw cycles, and have similar SN. The subgrade properties at the two

locations are likely to have been a major factor in the difference in their performance. The better

performer (1-6109) has approximately 305 mm (12 in) of AC, which is located on a welldraining sand subgrade with no drains. This section has an IRI of 0.71 mIkm (45 in/mi) 14 years

after the overlay. The pavement section for the poorer performer (1-6012) consisted of 127-mm

(5-in) AC thickness on 127-mm (5-in) of AC-treated base that is located on a silty sand subgrade

(P200 = 21 percent, w% = 11 percent) with continuous well-type drains. This section had an IRI

value of 1.91 mIkm (121 in/mi) 10 years after the overlay.

Section 84-6804 was noted to be performing well under high traffic (730 KESAL/yr),

high precipitation, and high freeze index conditions (see figure 5.7.4). This is a very thick

section (SN = 6.3) consisting of 152-mm (6-in) AC, over 76 mm (3 in) of AC-treated base, and a

thick subbase providing high overburden on the subgrade. The subgrade has a P200 content of

22 percent and a moisture content of 5 percent. The IRI value for this section was 0.84 mIkm (53

in/mi) 16 years after the overlay. Another good performer in wet freeze conditions was section

21-6043 (see figure 5.7.4). This is a high traffic route (970 KESAL/yr) located on a subgrade

that is similar to that discussed in the previous section. This section had an estimated SN of 5.1

with a thick subbase, but the freezing index at this site was less than for section 84-6804. This

section had a IRI of 1.04 mIkm (66 in/mi) 15 years after overlay.

The GPS-7 experiment is the study ofPCC pavements that are overlaid with AC. The

PCC pavements include jointed plain concrete pavements (JPCP), jointed reinforced concrete

pavements·(JRCP), and continuously reinforced concrete pavements (CRCP). The GPS-7

experiment is divided into two categories: GPS-7 A and GPS-7B. The GPS-7 A sections were

existing overlaid pavements that were at different periods of their service life when they were

accepted into the LTPP program. The GPS-7B sections were overlaid after they were accepted

to the LTPP program, and the IRI of these sections immediately after the overlay is known.
There are 21 GPS- 7B sections in the LTPP experiment for which profile data are

available; 15 of these sections had been profiled prior to being overlaid, and therefore the IRI of

these sections before the overlay is known. Figure 5.7.5 presents the IRI before the overlay, the

IRI after the overlay, and the overlay thickness for the 15 sections for which the IRI before and

after the overlay is known. In this figure, the overlay thickness are arranged in ascending order.

The IRI after the overlay of 12 of the sections shown in figure 5.7.5 ranged from 0.73 to 1.06

m/km (46 to 67 in/mi), with three sections having an IRI between 1.32 and 1.51 m/km (84 and 96

in/mi). The data shown in figure 5.7.5 indicate that even a relatively thin overlay that is less than

88 mm (3.5 in) can substantially reduce the IRI of a pavement. For three cases shown in figure

5.7.5, the IRI of the pavement after the AC overlay was more than 1.26 m/km (80 in/mi). For

these three cases, the AC overlay reduced the IRI of the pavement by a very small amount.

180

160

140

120

.-

~

100

:::. 80

i

60

40

20

0

•

,

:

•

•

I I I III II I II I

I

• Before Overlay

• After Overlay

~ g ~ ~

(") M (") M

Overlay Thlckne •• (In)

1 in/mi = 0.0158 m/km

Figure 5.7.5. IRI before and after overlay for GPS-7B sections.

As described previously, there are 21 sections in the GPS-7B experiment, and the IRI

before and after the overlay is known for 15 of these sections; for the other six sections only the

IRI after the overlay is known. Therefore, the IRI after overlay is known for 21 sections. The
frequency distribution of the IRI after overlay of all 21 GPS-6B sections is shown in figure 5.7.6.

As 'shown in this figure, the IRI after overlay ranged from 0.6 to 1.58 m/km (40 to 100 in/mi),

with 76 percent of the IRI values after the overlay being less than 1.10 m/km (70 in/mi). The

factors that influence the IRI value of an overlaid pavement include: (1) the wavelength

distribution in the pavement prior to the overlay, (2) the thickness of the overlay, (3) construction

procedures, ,and (4) capability of the contractor.

9

8

7

6

g 5

Q)

j

i 4 ... u..

3

2

0

40- 50-

50 60

100%

90%

80%

70%

60%

50%

40%

30%

_Frequency

_ Q.lll1Jlative %

20%

10%

0%

60- 70- so- 90-

70 80 90 100

RI Range (inlrri)

1 in/mi = 0.0158 m/km

Figure 5.7.6. Frequency distribution ofIRI after overlay for GPS-7B sections.

GPS-7B sections are located only in the North Atlantic and North Central regions, and the

IRI versus pavement age (after overlay) plots for the GPS-7B sections in these two regions are

presented in figure 5.7.7. The age of all the GPS- 7B sections at the last profile date was less than

6 years, with 72 percent of the sections being less than 4 years old. As shown in figure 5.7.7,

virtually all sections are showing a flat response of IRI over time. Of the GPS-7B sections, 16

sections had been profiled three or more times. The linear regression analysis described in section

5.1 of this report computed a slope for each section that is the rate of increase of IRI for the

section. The analysis indicated that the slope for eight of the sections to be negative, and a

statistically significant linear relationship between IRI and pavement age existed only for three
140

GPS-7B, North Central

120

100

I 80

~

•

.~::.~ a 60

•

40

20

0

0 1 2 3 4 5 6 7 8

Age After Overlay. yr

140

GPS-7B, North Atlantic

120

100 •

....•

~

• •

80

~

....•

a

••..,

• •

60 •

I

•

• •

• •

40

20

0

,

0 1 2 3 4 5 6 7 8

Age Arter Overlay. yr

1 in/mi = 0.0158 m/km

Figure 5.7.7. Trends in IRI development for the GPS-7B sections.
sections. Therefore, a statistical analysis to quantify the change in IRI for GPS- 7B sections

cannot be carried out yet.

In the LTPP program, GPS-7 A sections were established on in-service pavements, and

the IRI of the pavement immediately after the overlay is not known for these sections. The 00

versus pavement age (after overlay) plots for the GPS-7A sections are presented in figure 5.7.8.

The age of the GPS- 7A sections in the Western region at last profile date are all less than 5 years.

As seen in figure 5.7.8, most GPS-7A sections show a flat response ofIRI over time, with a few

sections showing an increase of Iiu with time. Thirty GPS-7 A sections had been profiled three

or more times, and the linear regression analysis described in section 5.1 of this report computed

a slope (rate of increase of 00) for each section. These values are presented in appendix B. The

frequency distribution of the slopes is presented in table 5.7.3. Twenty-three percent of sections

had negative values for the rate of change of 00, with 37 percent of sections having a rate of

increase ofIRI of between zero and 0.03 m/km (2 in/mi).

Range of Number of Percentage

Slope Sections of Sections

(in/mi/yr) (%)

<0 7 23

0-1 6 20

1 -2 5 17

2-3 1 3

3-4 2 7

>4 9 30

A correlation analysis was performed between the slope and possible factors that could

cause an increase in roughness. The analysis was carried out separately for all GPS- 7A sections

and for sections that showed a slope greater than 0.03 m/km (2 in/mi/yr). When the analysis was

carried out for all GPS- 7A sections, the slope of sections that had a negative value was set to

zero. The results ofthis correlation analysis are presented in table 5.7.4. PCC properties such as

compressive strength, elastic modulus, and split tensile strength were not available for many GPS

sections and were not used in the analysis. Correlation coefficients are affected by the biases in

the distribution ofthe data, and some of the illogical correlations are attributed to these biases.

None of the factors that were explored had a high and logical correlation with the slope.
250 GPS-7A, North Central

200

Os

ISO 31·7017

"",-

:s~ ...e=: · - 100 ~~L-· Ill:

- . •

SO

0

0 5 10 IS 20 25 30

Ale Arter Overlay, yr

GPs-7A, North Atlantic

250

200

Os

ISO :e ~ ~

100 -. . -

. .•---- Ill:

-

•... ?: !---:..q SO

23-7023

0

0 5 10 IS 20 25 30

Age After Overlay, yr

GPS-7A, Southern

250

200

Os

ISO .•..

05 ..--

-Ill: 100 .~

-

, a:e: .

SO .

48-3629

0

0 5 10 IS 20 25 30

Age Arter Overlay, yr

250 GPs-7A, Western

200

Os

ISO

~

.•..

05

~

- 100 Ill:

- SO ~

0 ,

0 5 10 IS 20 25 30

Age After Overlay, yr
nua reclpl a Ion

Average Annual Days Below 32°F

Average Annual Days Above 90°F

Average Annual Wet Days

Average Annual Freeze-Thaw Cycles

Average Annual Freeze Index

Percent Passing No. 200 Sieve for Subgrade

Subgrade Moisture Content

Liquid Limit of Subgrade

Plastic Limit of Subgrade

Plasticity Index of Subgrade

Annual KESALs

Overlay Thickness

PCC Thickness

Base Thickness

-0.10

-0.02

-0.08

-0.15

0.01

-0.12

-0.09

-0.20

-0.20

-0.16

-0.11

0.01

-0.02

-0.12

-0.68

0.20

0.09

-0.62

-0.48

-0.63

-0.44

-0.81

-0.84

-0.64

-0.07

0.55

0.27

-0.40

Therefore, models for predicting the IRI development for GPS-7 A experiment could not

be developed. Distresses in AC overlays ofPCC pavements primarily occur at joints and

working cracks, and generally begin with reflection cracks that deteriorate and spall with time.

The IRI will therefore be affected by the joint spacing of the PCC pavement. The development

of roughness will also be affected by the type of joint repairs that are carried out prior to the

overlay. The joint repairs can consist of full-depth PCC or AC patches, partial-depth PCC or AC

patches, and load transfer restoration. Strategies such as placing a paving fabric at joints prior to

overlay to retard reflection cracking, or sawing and sealing of joints in the AC overlay will also

influence the development of roughness. The factors that were described previously, as well as

the properties ofPCC that control temperature-related movement of the PCC slab, are expected

to be the primary factors that relate to roughness development in AC overlays of PCC

pavements.

Characteristics of three sections that have low IRI values and a section that has a high IRI

value are described in this section. The described sections are indicated in figure 5.7.8 .

....
A section that is performing well is section 23-7023, which is located in the wet freeze

zone. The IRI value of this section is 1.0 mIkm (63 in/mi) 10 years after the overlay. This is a

196-mm-(7.7-in-) thick JRCP that was 27 years old when overlaid with 157 mm (6.2 in) of AC.

The section experiences approximately 1100 KESAL/yr. The combination of thicker overlay and

subgrade conditions (p200 = 23 percent, w% = 6 percent) can be attributed to the good

performance of this section.

A poor performer that is noted is section 31-7017, which is located in the wet freeze zone.

The IRI of the pavement 11 years after the overlay is 2.1 mIkm (133 in/mi). This is a section that

is subjected to a low traffic rate of 50 KESAL/yr, and the pavement section is a 201-mm (7.9-in)

thick JRCP slab that was 30 years old when overlaid with 81 mm (3.2 in) of AC. This section

rests on subgrade with a P200 of9l percent and a moisture content of28 percent.

There are two sections that are AC overlays of CRCP sections that have performed well

(48-3629 and 17-5453). The IRI of section 48-3629 is 0.76 mIkm (48 in/mi) 10 years after the

overlay, while the IRI of section 17-5453 is 1.15 mIkm (73 in/mi) 10 years after the overlay.

The pavement section for both of these sections is approximately 66-mm (2.6- in) AC overlay of

203-mm (8-in) CRCP slab. Both sections rest on sub grades with relatively high P200 and high

moisture contents and are subjected to high traffic volumes.

The GPS-9 experiment is the study ofunbonded PCC overlays ofPCC pavements. The

PCC pavement types can include jointed plain concrete pavement (JPCP), jointed reinforced

concrete pavement (JRCP) and continuously reinforced concrete pavement (CRCP). There were

28 GPS-9 test sections for which profile data were available. There were six combinations of

pavement types included in this experiment, as shown in table 5.7.5.

The IRI versus age of pavement (after overlay) plots for GPS-9 sections are shown in figure

5.7.9. At the latest profile date for these sections, 60 percent of the sections were less than 5

years old after the overlay. As seen in figure 5.7.9, some sections showed a relatively flat
response of 00 over age, with some sections showing a variable IRI pattern over the years, and

some sections showing an increase in IRI with age.

Twenty-four GPS-9 sections had been profiled three or more times, and the linear

regression analysis described in section 5.1 of this report computed a slope (rate of increase of

00) for each section. These values are presented in appendix B. The frequency distribution of

the slopes are presented in table 5.7.6. A statistically significant linear relationship between 00

and pavement age existed for only four sections. Twenty-nine percent of sections had negative

values for the rate of change of IRI, with 38 percent of sections having a rate of increase of 00

between zero and 0.03 mIkm (2 in/mi). Concrete strength data from laboratory tests (such as

compressive strength) are not available for the majority of the sections.

Range of Number of Percentage

Slope Sections of Sections

(in/mi/yr) (%)

<0 7 29

0-2 9 38

2-4 1 4

4-6 2 8

6-8 3 13

>8 2 8

A correlation analysis was performed between the slope and the parameters presented in

table 5.7.4 to see if the slope could be related to these parameters. The analysis was carried out

separately for all sections and then for the sections that showed a slope greater than 0.03 mIkm (2
2SO GPS-9, North Central

200

l

150

~

20-1037

.--'v -.• --

a 100 __ ;;;;;3)'7

50

0

0 5 10 15 210 25 30

Ace After OYerle)', )'r

2SO GPS-9, North Atlantic

200

1

•...•... . •....•..•.•

150

~ a 100

50

0

0 5 10 15 210 25 30

Age After OYerl_)',)'r

2SO GPS-9, Southern

200

'il 150 :5

a 100

~ 50 . ,

0

0 5 10 15 20 25 30

Age After OYerle)',)'r

2SO GPS-9, Western

200

'il 150 :5

a 100 ~."O7

50

0 i

0 S 10 15 20 25 30

Ace After OYerle)',)'r
in/mi/yr). However, no strong logical correlations could be obtained. Therefore, a model to

predict the development of 00 for GPS-9 sections could not be developed. The roughness

development characteristics that are associated with the six combinations of pavement types

included in this experiment (see table 5.7.5) are expected to be different. The low number of test

sections for each pavement type prevents a detailed analysis from being performed on each

pavement type.

Characteristics of four GPS-9 sections that have a high rate of increase of 00 are

described in this section. Only four GPS-9 sections had a statistically significant relationship

between IRI and pavement age, and all these sections had a high rate of increase of 00. These

sections are shown in figure 5.7.9. The rate of increase of 00 for these sections are: (1) section

06-9107: 0.11 m/km/yr (7.2 in/mi/yr), (2) section 20-9037: 0.07 m/km/yr (4.2 in/mi/yr), (3)

section 31-6701: 0.11 m/km/yr (7.2 in/mi/yr), and (4) section 89-9018: 0.16 m/km/yr (10.4

in/mi/yr).

For these four sections, test data for concrete were available only for section 06-9107,

which was a 220-mm (8.8-in) thick JPCP overlay of a JPC pavement. The average compression

strength and the split tensile strength of concrete for this site were 29.7 MPa (4315 psi) and 3.1

MPa (447 psi), respectively. These were the lowest values in the entire GPS-9 data set. The

original PCC was much stronger than the overlay. It is believed that this low overlay strength

combined with high traffic volume (800 KESAL/yr) led to the high rate of 00 development at

this site. The average thickness of the unbonded PCC overlay determined from cores at test

sections 89-9018 (JPC overlay of a JPC pavement) and test section 20-9037 (JPC overlay of a

JRC pavement) were 160 mm (6.4 in) and 145 mm (5.8 in), respectively, which are relatively

low overlay thicknesses. Both sections had low traffic volumes «180 KESAL/yr).

The 00 values before an9 after the overlay were available for 19 GPS-6B and 15 GPS-7B

sections. A review of the IRI data at these sites indicated there is little effect of the roughness

prior to overlay on the roughness immediately after the overlay. The 00 after overlay of the

GPS-6B sections ranged from 0.42 to 1.80 mIkm (27 to 114 in/mi), with 73 percent of the

sections having an 00 less than 1.10 mIkm (70 in/mi). For GPS- 7B sections, the 00 after the

overlay ranged from 0.73 to 1.58 mIkm (46 to 100 in/mi), with 76 percent of the 00 values being
less than 1.10 mIkm (70 inlmi). The trend in the data of the GPS-6A versus GPS-6B experiments

indicates on a qualitative basis that roughness starts to develop after about 10 years of service.

The continued monitoring of these sections is considered important. It could be seen that the

trend in increase in IRI after 10 years is a larger rate of increase than that for the GPS-l or GPS-2

pavements.

GPS-6B and GPS- 7B sections are generally less than 5 years old. Very little change in

00 has occurred at most of these sections after the overlay. Therefore, a statistical analysis on

the data cannot be performed yet. For GPS-6A, -7A and -9 sections, a linear regression analysis

was performed on the time-sequence 00 data to determine the rate of increase of 00.

Correlation analyses were performed separately for each experiment to relate the rate of increase

of 00 with parameters that could affect the increase ofIRI. For GPS-7A and GPS-9

experiments, strong and logical correlations were not observed, and models to predict 00

development could not be developed. For GPS-6A sections, a relationship between the rate of

increase of 00 and the minimum surface modulus was developed by considering all sections,

while a relationship between the rate of increase of 00 and SN was developed for the sections

that had a rate of increase of 00 that was greater than 0.03 m/krn/yr (2 inlmi/yr). However, both

these relationships had low R

2

values and high standard errors of estimates.

The increase in 00 of a pavement after construction will depend on factors such as

pavement layer thickness, subgrade properties, climatic factors, and applied traffic. If the initial

00 of the pavement is known, the current 00 of the pavement can be used to find the change in

00. The change in 00 can then be related to the variables that affect the development of 00 to

develop models. An approach that has been used in some research efforts to develop roughness

models is to treat the current IRI as the dependent variable and relate it to a set of independent

variables that are associated with the development of roughness. However, these independent

variables are related to the change in roughness, which is the difference between the current

roughness and the initial roughness, and not simply to the current roughness. Therefore, simply

developing models by relating the current IRI with parameters that may cause the 00 to increase,

without considering the initial IRI of the pavement, will not result in accurate models.

Initially, in this research effort models were developed by relating the current 00 of the

pavement to factors that may cause an increase in 00. Multiple regression analysis was

performed using computerized stepwise regression procedures to develop these models. The
following is an example of a model that was developed for the GPS-3 sections by considering all

GPS-3 sections.

Adjusted R

2

= 0.23 and standard error = 36

where:

IRI = current IRI (in/mi),

TIME = age of pavement (yr),

PCCTH = PCC thickness (in),

AVG32 = average days below 32°F,

PI = plasticity index, and

AVGWET = average wet days/yr

A residual analysis of this model performed by plotting the residuals with the predicted

IRI indicated a pattern in the residuals. The pattern showed that this model overpredicts the IRI

of pavements that have low IRI values and underpredicts the IRI of pavements that have high IRI

values. Therefore, such models are not considered to be accurate in correctly predicting the IRI

of the pavement.

A model developed for the GPS-9 sections using a similar approach yielded the following

model:

IRI = -45.4 + 4.08 * TIME + 0.43 * AVG32 + 5.34 * PCCOL + 0.72 * ANNP

R

2

= 0.45 and standard error = 21

where:

IRI = current IRI (in/mi),

TIME = age of pavement after overlay (yr),

AVG32 = average days below OF.

PCCOL = PCC overlay thickness (in), and

ANNP = annual precipitation (in).

The R

2

of this model may suggest that an approximate IRI of the pavement can be

obtained by this model. However, a residual analysis ofthis model performed by plotting the

residuals versus the predicted IRI indicated a pattern in the residuals. The residuals showed that
this model overpredicts the IRI of pavements that have low IRI values and underpredicts the IRI

of pavements that have high IRI values.

The Specific Pavement Studies SPS-l experiment is titled the Strategic Study of

Structural Factors for Flexible Pavements. The SPS-l experiment was developed to investigate

the effect of selected structural factors on the long-term performance of flexible pavements

constructed on different subgrade types in different environmental regions. The sub grade types

considered in this experiment are classified as fine grained and coarse grained, while the

environmental regions considered are the four LTPP environmental regions: wet freeze, wet-no

freeze, dry freeze and dry-no freeze.

Table 6.1.1 presents the structural properties of the test sections in the SPS-l experiment.

These 24 sections are to be constructed for each combination of sub grade type and environmental

region, with test sections 1 through 12 being constructed at one site, and test sections 13 through

24 being constructed at a different site. In a SPS-l project, the layout of the test sections can be

in any sequence; however, it is recommended that the expected life of the test section and base

type be considered when deciding on the layout. The structural factors considered in this

experiment are: surface layer thickness, base type, base course thickness, and drainability. Two

different surface course thicknesses of AC, 100 mm (4 in) and 175 mm (7 in), and five different

base types are used in the experiment. The base types are: dense-graded aggregate base, asphalttreated base, asphalt-treated base over dense-graded aggregate base, permeable asphalt-treated

base over dense-graded aggregate base, and asphalt-treated base over permeable asphalt-treated

base. The total base thickness at the test sections ranges from 200 mm (8 in) to 400 mm (16 in),

with underdrains being provided at all sections that have a permeable asphalt-treated base. The

estimated SN for each test section is also presented in table 6.1.1. The layer coefficients that

were used to compute the SNs are: AC - 0.40, asphalt-treated base - 0.32, permeable asphalttreated base - 0.22, and dense-graded aggregate base - 0.12. Each test section is constructed over

a minimum length of 182 m (600 ft), and 152 m (500 ft) of the section is used for monitoring.
Test Asphalt Layer ~ Layer;j t:stlmatea

Section Concrete Matenal I hlCKness Matenal I n1CKneSS Structural

Number Thickness (in) (in) (in) Number

1 7 DGAB 8 - -

3.76

2 4 DGAB 12 - - ;j.U4

3 4 ATB 8 - - 4.1t>

4 7 AII:S 1~ - - t>.t>4

5 4 AII:S 4 LJGAB 4 ;j.;jt>

t> 7 AII:S ~ LJGAI:S 4 :>.~

7 4 t"AII:S 4 LJGAI:S 4 4':.~t>

8 7 t"AII:S 4 LJGAI:S ~ 4.t>4

9 7 t"AII:S 4 UGAI:S 14': :>.14':

10 7 ATB 4 PATB 4 4.96

11 4 ATB 8 PATB 4 5.04

12 4 ATB 12 PATI:S 4 t>.;j4':

13 4 LJGAI:S ~ - - 4':.:>U

14 7 UGAI:S 14': - - 4.4':4

1:> f AII:S ~ - - :>.;jlj

16 4 AII:S 14': - - :>.44

17 7 ATB 4 UGAI:S 4 4.56

18 4 ATB 8 DGAB 4 4.64

19 7 t"AII:S 4 DGAB 4 4.1t>

~U 4 t"AII:S 4 LJGAI:S ~ ;j.44

~1 4 t"AII:S 4 UGAI:S 14': ;j.~4':

4':4': 4 AII:S 4 t"AII:S 4 ;j.fU

23 7 ATB ~ t"Alts 4 1j.4':4

24 7 ATB 12 PATB 4 7.52

Note 1: DGAB - dense-graded aggregate base, ATB - asphalt-treated base,

PATB - permeable asphalt-treated base.

The IRI values of the SPS-1 test sections were obtained from the RIMS, through the

regional contractors. Table 6.1.2 presents the SPS-1 projects for which IRI data were available.

This table also presents the test section numbers constructed at each site, the number of times the

project has been profiled, the sub grade type, the LTPP environmental region, and the estimated

annual ESALs applied to the test section. As shown in table 6.1.2, profile data are available for

six SPS-1 projects. One project has been profiled three times, while three projects have been
profiled twice, and the other two projects have been profiled only once. The SPS-l projects are

generally first profiled within a month after they are opened to traffic; thereafter, they are

profiled annually.

Extensive sampling is performed at the SPS-l projects to obtain samples from the

subgrade, base, and surface layers. Laboratory tests are performed on the samples to characterize

material properties. Data files containing laboratory test data were obtained from the RIMS,

through the regional contractors. A review of these data files indicated that very few data were

available in these files. Therefore, it was not possible to conduct an analysis to relate material

properties to the roughness at the test sites.

The IRl referred to in this section is the average IRl of the left and right wheel paths,

unless specifically mentioned otherwise. The IRl values at the test sections immediately after

construction were examined to study the IRl characteristics of new pavements. The average IRl

of all test sections at a SPS-1 project was computed by averaging the individual IRI of the test

sections. Figure 6.1.1 presents the average IRl for each SPS-l project. The SPS-1 project in

Arizona was constructed on a coarse-grained subgrade, while all other SPS-l projects were

constructed on a fine-grained subgrade. The average IRl for the SPS-1 projects in Alabama,

Arizona, Iowa, and Kansas ranged between 0.74 and 0.88 rn/km (47 and 55 in/mi), with the

project in Nebraska having the highest average IRl of 1.13 rn/km (72 in/mi). The COY of the

IRI between the test sections in each project is shown in figure 6.1.2. The COY ofIRl indicates

the variability ofIRI between the test sections. The SPS-1 projects in Virginia and Alabama had

the lowest variability in IRI between the test sections, with the COY for these two projects being

6 and 8 percent, respectively.
80

70

-

I

60

li 50

e' a.•.. 40

.2

i 30

3.

I! 20

~oC(

10

0

Alabarre Arizona bwa Kansas

Project Location

1 in/mi = 0.0158 m/km

Figure 6.1.1. Average IRI for SPS-1 projects.

20

18

16

14

i! 12

i

10 '0

~ 8

CJ

6

4

2

0

Alabarre Arizona bwa Kansas

Project Location

1 in/mi = 0.0158 m/km

Figure 6.1.2. COY ofIRI of test sections in SPS-1 projects.
The 00 values after construction of the test sections in the SPS-l projects in Alabama,

Iowa, and Kansas which consist of test sections 1 through 12 (see table 6.1.1), are shown in

figure 6.1.3. The 00 values after construction for the test sections in the SPS-l projects in

Arizona, Nebraska, and Virginia which contain test sections 13 through 24, are shown in figure

6.1.4 The subgrade at all projects was fine-grained, except for the project in Arizona, which had

a coarse-grained subgrade. These figures show that varying 00 values were obtained for the

same pavement section in projects constructed at different locations.

Large differences in 00 between the right and the left wheel paths were observed for the

SPS-l projects in Alabama and Arizona, with the right-wheel-path 00 being higher than the leftwheel-path 00. The average difference in 00 between the right and left wheel paths of the test

sites for the projects in Alabama and Arizona was 0.32 and 0.30 m/krn (20 and 19 in/mi),

respectively. Figure 6.1.5 shows the difference in IRI between the right and the left wheel paths

for the test sections in the SPS-l project in Alabama. The largest difference in 00 between the

wheel paths occurred at sections 1 and 4 in Alabama, and was 0.44 m/krn (28 in/mi). In the

Arizona SPS-l project, the largest difference between right- and left-wheel-path 00 at a test

section was 0.55 m/krn (35 in/mi). At the other four SPS-l projects, the average difference in

00 between the right and the left wheel paths ranged from 0.02 to 0.08 m/krn (l to 5 in/mi).

Figure 6.1.6 shows the relationship between the left- and the right-wheel-path IRI for the

individual test sites for the SPS-l projects shown in table 6.1.2. Each point in the plot represents

an SPS test section. The set of data that show high right-wheel-path 00 compared with the leftwheel-path 00 occurred in the test sections from the Alabama and Arizona SPS-l projects.

The 00 referred to in this section is the average 00 of the left and the right wheel paths.

The SPS-l projects in Arizona, Kansas, and Nebraska have been profiled twice, while the SPS-l

project in Iowa has been profiled three times. The SPS-l projects in Arizona and Nebraska

consist of test sections 13 through 24, while the test sections in Iowa and Kansas correspond to

test sections 1 through 12. The 00 of all test sections in these four projects for all profile dates is

shown in figures 6.1.7 through 6.1.10. The first profile date corresponds to the profile run

immediately after construction.
100

90

80

70

.....• 60

~

I

50 .Lo\

jg 40 OKS

30

20

10

0

1 inlmi = 0.0158 m/km

Figure 6.1.3. IRI after construction for test sections 1-12 in SPS-1 projects.

100

90

80

70

.....• 60

I

50

jg 40

30

20

10

0

~

AZ

• lIE

OVA

13 14 15 16 17 18 19 20 21 22 23 24

Section Hum ber

1 inlmi = 0.0158 m/km

Figure 6.1.4. IRI after construction for test sections 13-24 in SPS-1 projects.
30

I

E

l

25

!

••D. 20

)=

:c~c:::. 15

j!

10

J

5

CI

i

0

2 3 4 5 6 7 8 9 10 11 12

section Number

1 in/mi = 0.0158 mIkm

Figure 6.1.5. Difference in IRI between right and left wheel path for test sections in the

SPS-l project in Alabama.

100

•...•. 80

~:::.

i 60

~

Dr••

I. 40

E

I 20

0

0 20 40 60 80 100

Left·WheeI-Path IRI (Inlml)

1 in/mi = 0.0158 m/km

Figure 6.1.6. Comparison between left- and right-wheel-path IRI for SPS-l test sections.
80

70

60

.- 50

~ 40 ;.

i 30

20

10

0

• Date - 1/27/94

• Date - 1/27/95

13 14 15 16 17 18 19 20 21 22 23 24

section Number

1 in/mi = 0.0158 mIkm

Figure 6.1.7. IRI for SPS-1 project in Arizona.

120

100

80

.-

~ 60

;.

i

40

20

0

• Date - 5/13/94

• Date - 2/18195

1 in/mi = 0.0158 mIkm

Figure 6.1.8. IRI for SPS-1 project in Kansas.
120

100

80

-

~ 60 ;.

i.

40

20

0

C") ~ It) co •.... co Ol 0

.•.. .•.. .•.. .•.. .•.. .•.. .•.. N

Section Number

• Date - 1/11/95

• Date - 4/17/96 I

1 in/mi = 0.0158 mIkm

Figure 6.1.9. IRI for SPS-l project in Nebraska.

100

90

80

70

-

60

I

50

i

40

30

20

10

0

• Date - 10/15193

• Date - 2/15195

o Date - 4/15196

1 in/mi =0.0158 mIkm

Figure 6.1.10. IRI for SPS-l project in Iowa.
The rates of increase ofIRI at each test section in these SPS-l projects were computed

relative to the IRI after construction, using the following formula:

Rate of increase ofIRI = IRI (t) - IRI (0) (6.1)

Time

IRI (t)

IRI (0)

Time

= IRI at latest profile date,

= IRI after construction, and

= time in years between the profile dates.

The rate of increase in IRI for the SPS-l test sections computed from the above formula

is presented in figure 6.1.11 as a function of the estimated SN of the section. In this figure, the

rate of increase in IRI for the projects in Arizona, Kansas, and Nebraska represents the rate of

increase ofIRI at approximately 1 year after being subjected to traffic; for the project in Iowa it

reflects the rate of increase in IRI that occurred over a 2-year period. Section 1 in Kansas, which

had an estimated SN of3.76, had a rate of increase ofIRI of 1.04 m/km/yr (66 in/mi/yr), which is

not shown in the plot because of scaling effects.

30

25

....•

E 20

!

ii 15 '0

i 10

I

'0 5

i

0

2.

-5

I in/mi = 0.0158 mIkm

Figure 6.1.11. Rate of increase of IRI for SPS-I sections as a function of SN.
Approximately 1 year after construction, the majority of sections in Arizona had an IRI

that was less than that after construction. However, the IRI value after construction for test

section 13 in Arizona (which had an estimated SN of2.50) was not available; therefore, the rate

of development ofIRI for that section could not be computed. Apart from the project in Arizona,

all other projects showed a high rate of increase ofIRI for the sections that had a low SN..

Figure 6.1.10 shows the IRI at the test sections in the SPS-l project in Iowa, which reflect

variable IRI trends. The 00 at the second profile date was higher than the IRI at the first profile

date at all sections except for test sections 1,2,5, and 7. However, at the third profile date, all

sections had IRI values that were higher than the IRI at the first profile date. These variations

may have been caused by environmental factors, as the second profile date at this project was in

winter, and the third profile date was in early spring.

In the Kansas SPS-l project, test sections 1,2,5, and 7 had estimated SNs that were less

than 4, and all these sections showed high rates of increase ofIRI. The rate of increase of 00 at

test section 1 was 1.05 m/km/yr (66.3 in/mi/yr), while at sections 2, 5, and 7 the rate ranged from

0.23 to 0.46 m/km/yr (14.8 to 28.8 in/mi/yr). The Kansas and Iowa SPS-l projects have the

same series of test sections (see table 6.1.1). The high rate of increase ofIRI that was observed

at some test sections in the Kansas project was not seen at the corresponding test sections in the

Iowa project, where all sections had a rate of increase ofIRI that was between 0.06 and 0.19

m/km/yr (4 to 12 in/mi/yr). The SPS-l project in Iowa and Kansas are both located on fine-grade

subgrade; the Iowa project is in the wet-no freeze zone and the Kansas project is in the dry freeze

zone. However, the Kansas project is subjected to over three times the ESAL rate as the Iowa

project. The high traffic rate as well as different subgrade conditions may have caused the higher

rates of increase in IRI for these sections in Kansas.

The effects of seasonal variability in 00 of AC pavements is not known at this time.

Sub grade movements in fine-grained soils can occur due to variations in moisture and freezing

effects. Although the SPS-l projects are to be profiled annually, changes of several months can

occur between profile dates. For example, the profile dates for the Iowa SPS-l project were: (1)

October 15, 1993, (2) February 15, 1995, and (3) April 15, 1996. The first profiling date was in

the fall, the second profiling date was in the winter, and the third profiling date was in the

spring. The variability in sub grade conditions caused by seasonal effects may influence the

profile ofthe pavement, resulting in differences in 00. The rates of increase ofIRI that were

discussed will be affected if such conditions occur.
Profile data were available for six SPS-1 projects. Of these, one project has been profiled

three times, three projects had been profiled twice, and two projects had been profiled only once.

Therefore, only a limited amount of profile data were available for analysis. The average 00 of

the SPS-1 projects, which is the average of the 12 test sections in the project, ranged from 0.74

m/km (47 in/mi) for the SPS-1 project in Arizona to 1.13 m/km (72 in/mi) to the SPS-l project in

Nebraska. The COY of the 00 of the test sections within a project ranged from 8 percent for the

project in Alabama to 19 percent for the project in Kansas. There were large differences between

the right- and the left-wheel-path 00 for the SPS-l projects in Alabama and Arizona, where the

right-wheel-path 00 was higher than the left-wheel-path 00. The average difference between

the 00 of the wheel paths for the SPS-l projects in Alabama and Arizona was 0.32 and 0.30

m/km (20 and 19 in/mi), respectively. This average difference for the other four projects ranged

from 0.02 to 0.08 m/km (1 to 5 in/mi). The test sections in the SPS-l project in Arizona, which

is located on a coarse-grained subgrade in a dry-no freeze environment, showed little change in

00 when it was profiled approximately 1 year after construction. The test sections in the

Kansas project that have estimated SNs less than 4 showed a large increase in 00 when they

were profiled approximately 1 year after construction. The test sections in Kansas are located on

a fine-grained subgrade and are subjected to an estimated 469 KESAL/yr. Apart from the SPS-l

project in Arizona, all other SPS-l projects generally indicated that the sections that had low SNs

had a higher rate of increase of 00 when compared with the sections that had high SNs.

The Specific Pavement Studies SPS-2 experiment is titled the Strategic Study of

Structural Factors for Rigid Pavements. The SPS-2 experiment was developed to investigate the

effect of selected structural factors on the long-term performance of rigid pavements constructed

on.different subgrade types in different environmental regions. All rigid pavement test sections

are doweled, jointed PCC with a joint spacing of 4.6 m (15 ft). The subgrade types considered in

this experiment are classified as fine-grained and coarse-grained. The environmental regions

considered in this experiment correspond to the four LTPP environmental regions: wet freeze,

wet-no freeze, dry freeze and dry-no freeze.
Table 6.2.1 presents the structural properties of the test sections in the SPS-2 experiment.

These 24 sections are to be constructed for each combination of sub grade type and environmental

region, with half ofthe test sections (sections 1 through 12) being constructed at one site, and the

other half (sections 13 through 24) being constructed at a different site. The structural factors

considered in this experiment are: thickness ofPCC layer, flexural strength of the PCC, base

type, drainability, and lane width. The two PCC slab thickness considered in this experiment are

200 mm (8 in) and 275 mm (11 in), while the two PCC flexural strengths that are used are 3.8

MPa (550 psi) and 6.2 MPa (900 psi). The three base types used are: lean concrete base of 150

mm (6 in) thickness, dense-graded aggregate base of 150-mm (6-in) thickness, and a 100-mm (4-

in) thick permeable asphalt-treated base over a 100-mm (4-in) thick dense-graded aggregate base.

Underdrains are provided in the test sections that have a permeable asphalt treate~ base. The

lane widths considered in this experiment are 3.66 m (12 ft) and 4.27 m (14 ft). Each test section

is constructed over a minimum length of 183 m (600 ft), and 152 m (500 ft) of the section is used

for monitoring.

The IRI values of the SPS-2 test sections were obtained from the RIMS through the

regional contractors. Table 6.2.2 presents the SPS-2 projects for which IRI data were available.

This table also presents the test section numbers constructed at each site, the number of times the

project had been profiled, the subgrade type, environmental region, and the estimated E~ALs in

the study lane. As shown in table 6.2.2, profile data are available for seven SPS-2 projects. Two

projects have been profiled four times, one project has been profiled three times, two projects

have been profiled twice and two projects have been profiled once. The SPS-2 projects are

generally first profiled within a month after they are opened to traffic; thereafter, they are

profiled annually.

Extensive sampling is performed at the SPS-2 projects to obtain samples from subgrade,

base, and surface layers. Laboratory tests are performed on the samples to characterize material

properties. Data files containing laboratory test data were obtained from the RIMS, through the

regional contractors. A review of these data files indicated that very few data were available in

these files. Therefore, it was not possible to conduct an analysis to relate material properties to

the roughness at the test sites.
Section t"l,;l,; Flexural Lane Layer 2 Layer 3

Number Thickness Strength Width Matenal TniCKness Matenal I nlCKness

(in) (psi) (ft) (in) (in)

1 8 550 12 DGAI::S t) - -

2 8 900 14 DliAI::S t) - -

3 11 550 14 (JliAI::S t) - -

4 11 900 12 (JliAI::S t) - -

:> l:S 050 12 LLiI::S t) - -

t) l:S !::IOO 14 LLiI::S t) - -

7 11 OOU 14 Ll,;1::S 6 - -

8 11 !::IOU 12 Ll,;1::S 6, - -

9 8 550 12 PATB 4 DGAB 4

10 8 900 14 PATB 4 (JliAB 4

11 11 550 14 PAlB 4 (JliAB 4

12 11 900 12 PATB 4 LJliAB 4

13 8 550 14 DGAB t) - -

14 8 900 12 DliAI::S t) - -

15 11 550 12 LJliAI::S t) - -

16 11 !::IOU 14 LJliAI::S t) - -

17 l:S OOU 14 LLiI::S t) - -

1l:S l:S !::IUU 12 LLiI::S 6 - -

19 11 OOU 12 Ll,;t:S 6 - -

20 11 900 14 LeB 6 - -

21 8 550 14 PATB 4 LJliAB 4

22 8 900 12 PAII::S 4 LJliAB 4

23 11 550 12 PAIB 4 LJliAI::S 4

24 11 900 14 PAII::S 4 LJliAB 4

Note 1: DGAB - dense-graded aggregate base, LeB - lean concrete base,

PATB - permeable asphalt-treated base.

Note 2: 1 in. = 25.4 mm, 1 ft = 0.3 m, 1 psi = 6.89 kPa.
The IRI values at the test sites immediately after construction were examined to study the

IRI characteristics. The average IRI for each SPS-2 project was computed by averaging the IRI

values of the test sections in the project. The average IRI for each SPS-2 project, computed by

averaging the IRI of each test section, is presented in figure 6.2.1. The average IRI for the SPS-2

projects ranged from 1.12 to 1.47 m/km (71 to 93 in/mi). The COV ofIRI between the test

sections in each project is shown in figure 6.2.2. The COV presents the variability in IRI

between the test sections. The COV for the projects ranged from 11 to 24 percent.

The IRI values after construction of the test sections in the SPS-2 projects in Iowa,

Kansas, North Carolina, and Washington that contain test sections 1 through 12 (see table 6.2.1)

are shown in figure 6.2.3. The IRI after construction for the test sections in the SPS-l projects in

Arizona, Colorado, and Michigan that contain test sections 13 through 24 are shown in figure

6.2.4 The subgrade at the projects in Arizona and Colorado was coarse grained, while the

subgrade at the other projects was fine grained. These figures show that varying IRI values were

obtained for the same pavement section in the projects constructed at different locations.

The average left- and right-wheel-path 00 values for the different SPS-2 projects were

close to each other for all projects, with the average difference between the right- and left-wheelpath 00 (right IRI - left 00) ranging from -0.09 to 0.14 m/km (-6 to 9 in/mi). Figure 6.2.5

presents the relationship between the left- and the right-wheel-path 00 for the individual test

sites for the SPS-2 projects shown in table 6.2.1. Each point in the plot represents an SPS test

section. For most of the test sites, the difference in 00 between the wheel paths at a test section

was less than 0.16 m/km (10 inlmi).

The average 00 values of the test sections that have similar base types in each SPS

project were computed to examine if trends could be identified. The three base types present in

an SPS-2 project are: (1) dense-graded aggregate base, (2) lean concrete base, and (3) permeable

asphalt-treated base over dense-graded aggregate base. Each SPS-2 project has four sections that

have each of these base types. The average IRI for each base type for all SPS-2 projects is

presented in table 6.2.3 and shown in figure 6.2.6. The test sections that are included in each

base type have different slab thickness, flexural strengths, and lane widths. Although some SPS

projects showed clear differences, in the 00 values for a specific base type, an overall trend was

not evident when all projects were considered. For example, in the SPS-2 project in Colorado

(see figure 6.2.7) test sections 13 through 16, which are on a dense-graded aggregate base have a
100

90

80

70 -.

~ 60

:::.

e; 50

&

l! 40

II

~ 30

20

10

0

AZ CO L6. KA M NC WA

Project Location

1 in/mi = 0.0158 m/km

Figure 6.2.1. Average IRI for SPS-2 projects.

25

20

l

15

e;

'5

~ 10

CJ

5

0

AZ co L6. KS M NC WA

Project Location
140

120

100

80

.106.

:g

.KS

C;. ONC

~

60

.WA

40

20

0

1 inlmi = 0.0158 mIkm

Figure 6.2.3. IRI after construction for test sections 1-12 in SPS-2 projects.

120

100

80

-.E

!

60

i

40

20

13 14 15 16 17 18 19 20 21 22 23 24

section Number

1 inlmi = 0.0158 mIkm

Figure 6.2.4. IRI after construction for test sections 13-24 in SPS-2 projects.
_ 120

~

;'100 Ii

~ 80 !60

140

60 80 100

Left-wheel-Path IRI (In1m1)

1 in/mi = 0.0158 m/km

Figure 6.2.5. Comparison between left- and right-wheel-path IRI for SPS-2 test sections.

120

100

~

80

;.

Ii 60

t 40

~

20

0

AZ co IA KS tot NC WA

Project Location

.DGAB

.LC8

OPATBIDGAB

1 in/mi = 0.0158 m/km

Figure 6.2.6. Average IRI after construction for different base types.
Note 1: DGAB - dense-graded ag~regate base, LeB - lean concrete base,

PATB - permeable asphalt-treated base.

Note 2: 1 in/mile = 0.0158 m/km.

120

100

80

i'

~

60

=::.

i

40

20

0

I_ Date- 4/13/941

13 14 15 16 17 18 19 20 21 22 23 24

Section Number
lower IRI when compared with the other two base types. In the SPS-2 project in North Carolina

test sections 5 through 8 which are on a lean concrete base have a higher IRI when compared

with the other two base types. Similar observations on the differences in IRI were noted at other

several other SPS-2 projects.

Table 6.2.4 presents the av~rage IRI values ofthe test sections in each SPS-2 project that

have PCC flexural strengths of3.8 MPa (550 psi) and 6.2 MPa (900 psi). An overall trend in the

differences in IRI value between the PCC sections that have different flexural strengths could not

be observed for the SPS-2 projects. However, some specific trends related to the PCC flexural

strength were noted at some SPS-2 projects.

As shown in table 6.2.5, in the SPS-2 project in Washington, all test sections that had a

PCC flexural strength of 6.2 MPa (900 psi) had a lower IRI than the test sections that had a

flexural strength of3.8 MPa (550 psi). The test sections in the Michigan SPS-2 exhibited the

reverse of this pattern (see table 6.2.5), with test sections having a PCC flexural strength of6.2

MPa (900 psi) showing a higher IRI than the test sections that have PCC flexural strength of3.8

MPa (550 psi).

Figures 6.2.8 through 6.2.12 show the IRI at the test sections in each SPS-2 project that

has been profiled at least two times. The first profile date shown in these plots corresponds to

the IRI obtained immediately after construction. Examination of these plots does not indicate

clear trends in the change in IRI values. In some projects, large changes, either reductions or

increases in IRI values, are observed between the profiling dates.

Figure 6.2.9 show the IRI values at the test sections in the SPS-2 project in Arizona for

two different test dates. The first test date corresponds to testing performed after construction,

and the second test date is approximately 1 year after the first profile date. At all test sections, the

IRI value after approximately 1 year was less than the IRI values obtained after construction. The

test sections were first profiled after construction on January 25, 1994, at approximately 6 a.m.,

when the air temperature was estimated to be 13°C (45 OF). The sections were then profiled

approximately 1 year later on March 5, 1995, at 11:20 a.m., when the air temperature was

estimated to be 18°C (65 OF). The reductions in IRI noted at all test sites in this project between
Note 1: 1 in/mile = 0.0158 m/km.

Note 2: 1 psi = 6.89 kPa.

Note 1: DGAB - dense-graded aggregate base, LeB - lean concrete

base, PATB - permeable asphalt-treated base.

Note 2: 1 in = 25.4 mm, 1 psi = 6.89 kPa.
140

120

100

f

80

;.

60 i

40

20

0

2 3 4 5 6 7 8 9 10 11 12

Section Number

• Date - 3/30194

• Date - 1/6196

o Date - 2/28/96

1 in/mi = 0.0158 mIkm

Figure 6.2.8. IRI for SPS-2 project in North Carolina.

100

90

80

70

-. 60 'E

!

50

i

40

30

20

10

0

C') .". It) co •... co 0) .•.. .•.. .•.. .•.. .•.. .•.. .•.. ~

Section Number

• Date - 1/27/94

• Date - 2/27/95

1 in/mi = 0.0158 mIkm

Figure 6.2.9. IRI for SPS-2 project in Arizona.
140

120

100

f

80

;.

60 Ii

40

20

0

N (f) •

It) (l) ... CIO c» 0

•.. •.. •.. •.. N

section Number

140

120

100

-- 80 I

Ii 60

40

20

0

• Date - 2/16195

• Date - 4/16196

1 in/mi = 0.0158 m/km

Figure 6.2.10. IRI for SPS-2 project in Iowa.

• Date - 8/14/92

• Date - 3110/93

o Date - 5/15/94

• Date - 4120/96

1 in/mi = 0.0158 m/km

Figure 6.2.11. IRI for SPS-2 project in Kansas.
• Date- 9/16/94

• Date- 6/11/95

o Date· 1/8196

.-. 80 !

i

60

40

20

13 14 15 16 17 18 19 20 21 22 23 24

Section Number

1 inlmi = 0.0158 m/km

Figure 6.2.12. IRI for SPS-2 project in Michigan.

the profile dates are tabulated in table 6.2.6. Large reductions in IRI were noted at all test

sections that had 200-mm- (8-in-) thick slabs. The magnitude of the reduction in IRI at the sites

that had 200-mm- (8-in-) thick slabs ranged from 0.25 to 0.49 (16 to 31 inlmi). The changes in

IRI that were noted at the test sites are attributed to curling effects at the joints most likely caused

by temperature variations. Thin slabs are more susceptible to curling effects when compared

with thick slabs, and this effect is seen in the data presented in table 6.2.6. In the SPS-2 test

sections the joints are at 4.5-m (15-ft) intervals, and movements at these joints can have a large

influence in the IRI values as each joint has an influence on the IRI value. Vastly different

interpretations in the IRI at the test sections in this project can be drawn depending on the test

date that is used to compare the IRI between the test sections.

The IRI values of the test sections in the Michigan SPS-2 project are shown in figure

6.2.12. Test sections 13 through 16 have a dense-graded aggregate base, with test sections 17

through 20 having a lean concrete base, and test sections 21 through 24 having a permeable

asphalt-treated base. All test sections except for section 21 show a reduction in IRI between the

first and the second profiling date. All sections show an increase in IRI between the second and
Note1:DGAB - dense-graded aggregate base, LeB - lean concrete base,

PATB - permeable asphalt treated base.

Note 2: T1 - 1/25/94, T2 - 3/5/95.

Note 3: 1 in/mile = 0.0158 m/km, 1 in = 25.4 mm.

the third profiling dates. The magnitude of the change in 00 between the first and the second

profiling dates, and the second and the third profiling dates vary between the test sections.

Relatively small changes in 00 are observed at: (1) the four test sections located on the

permeable asphalt-treated base (sections 21 through 24), (2) the two test sections that have a PCC

flexural strength of3.8 MPa (550 psi) located on dense-graded aggregate base (sections 13 and

15), and (3) the two test sections that have a PCC flexural strength of3.8 MPa (550 psi) located

on lean concrete base (sections 17 and 19). The test sections having a PCC flexural strength of

6.2 MPa (900 psi) located on dense-graded aggregate base (sections 14 and 16) and lean concrete

base (sections 16 and 18) show large changes in 00. Figure 6.2.13 show the filtered profile of

the first 30 m (100 ft) of test section 14 for the first two profile dates. The peaks in this figure

correspond to the joints. The figure shows that the two profiles are different, with the

magnitudes of the peaks corresponding to the second test date being smaller. As described

earlier, the SPS-2 test sections have joint spacings of 4.6 m (15 ft), and changes in the profile at

joints can have a large influence'on the IR!.

The SPS-2 projects in North Carolina and Kansas both have been profiled three times

(see figures 6.2.8 and 6.2.11), while the SPS-2 project in Iowa has been profiled twice (see figure
0.15

0.1

!

.

0.05

. .

I

C :

;.

I

0

w

-0.05

-0.1

-0.15

Distance (ft)

__ Dlte - 9/6/94

....... Dlte - 8/1/95

1 in = 25.4 mm, 1 ft = 0.305 m

Figure 6.2.13. Filtered right-wheel-path profile for two profile dates for section 14 in the

Michigan SPS-2 project.

6.2.10). Large variations in IRI across all sites are not seen in these projects. However, large

changes in IRI are noted at some test sections.

The analysis of changes in IRI at SPS-2 test sections indicated that movement at joints

can have a large effect on the IRI value of the test sections. Joint movements are likely to be

influenced by: temperature gradients in PCC slabs, variations in the moisture conditions in PCC,

type of base, and mechanical properties ofPCC. The analysis indicated that the changes in IRI

of over 0.39 m/km (25 in/mi) can be caused by curling effects at joints. The limited timesequence data and the lack of test data for the SPS-2 projects prevent an extensive analysis from

being performed to explain the observations described in this chapter. The changes in IRI that

were noted at the test sections are expected to be caused by environmental as well ~ PCC mixrelated properties.
Profile data were available for seven SPS-2 projects. Of these, one project has been

profiled four times, two projects have been profiled three times, two projects have been profiled

twice, and two projects have been profiled once. Therefore, only a limited amount of profile

data were available for analysis. The average 00 of the SPS-2 projects, which is the average of

the 12 test sections in the project, ranged from 1.12 m/km (71 in/mi) to 1.47 m/km (93 in/mil.

The COY of the 00 of the test sections within a project ranged from 11 percent to 24 percent.

The left- and right-wheel-path 00 were generally close to each other for most test sections, with

this difference typically being less than 0.16 m/km (10 in/mil. The 00 after construction at all

test sections in the Washington SPS-2 project that had a PCC flexural strength of 6.2 MPa (900

psi) had an 00 that was less than the 00 at the test sections that had PCC flexural strength of3.8

MPa (550 psi). A reversal of this trend was observed at the Michigan SPS-2, where all test

sections that had PCC flexural strength of 6.2 MPa (900 psi) had an 00 that was greater than the

00 at the test sections with a PCC flexural strength of 3.8 MPa (550 psi). These observations

indicate that the PCC mix properties affect the 00 after construction of PCC sections. There

were large differences in the 00 values between the annual profiling dates at several sections.

The analysis of data from the Arizona SPS-2 project indicated that the 00 values obtained

approximately 1 year after construction were much less than the 00 values obtained after

construction at some sections. The maximum difference that was observed at a test section in

this project was 0.49 m/km (31 in/mil. Variability in 00 between profile dates was also noted at

some sections in the Michigan SPS-2 project, and a filtered profile analysis at such a section

indicated that the differences in 00 were caused by curling at the joints. Because of the short

joint spacing of the slabs, joint curling can have a large effect on the 00. Joint movements in the

PCC slabs are likely to be influenced by PCC mix properties, temperature, and support

conditions below the slab. Because of the confounding effect on 00 caused by these factors, an

accurate estimate of the development of roughness at SPS-2 sections cannot be estimated.

The Specific Pavement Studies SPS-5 experiment was developed to investigate the

performance of selected AC rehabilitation treatment factors. The rehabilitation treatment factors

include overlay mix type (recycied and virgin), overlay thickness, and surface preparation of the

existing AC surface prior to overlay (minimal and intensive preparation). Nine test sections are

included in each SPS-5 project, with eight sections being experimental sections and one section

being a control section. The overlay thickness as well as the type of material used for the overlay

(virgin or recycled AC) and the type of surface preparations that are carried out at the test

sections prior to placing the AC overlay are described in table 7.1.1. Table 7.1.2 presents a

description of the types of surface preparation activities that are carried out at the sections prior

to placing the AC overlay. Section 1 is designated as a control section, which receives only

limited routine-type maintenance. Repair activities on the control section are limited to those

maintenance activities needed to keep the section in a safe and functional condition. Repair

activities on this section are carried out according to the guidelines of the State highway agency.

The minimal level of surface preparation applies to test sections 2 through 5, and consists

primarily of patching of severely distressed areas and potholes and placement of a leveling

course in ruts that are greater than 12 mm (112 in) deep. The intensive level of preparation

applies to test sections 6 through 9, and includes milling of the existing AC surface, patching of

distressed areas, and crack sealing after milling. Milling of the surface is the primary difference

between the minimal and intensive preparation levels in this experiment. Milling is performed in

the intensive surface preparation sections to a depth of 38 to 50 mm (1.5 to 2 in), and the depth of

material removed by milling is replaced with an equal thickness of AC overlay material. This

material will be virgin mix on test sections 6 and 7 and a recycled mix on test sections 8 and 9.

The depth of replacement material is not counted as a part of the overlay thickness specified in

the experiment. The recycled AC that is used consists of 30 percent recycled asphalt mix. The

monitored portion of each test section is 152 m (500 ft).
eClon

Number

R - Recycled Hot Mixed Asphalt Concrete

V - Virgin Hot Mixed Asphalt Concrete

X - Peform

A· If ruts are >1/2 inch

B - Not permitted in first year of study

P - Peform after milling as required

F - Milling permitted only to remove open graded friction courses
The IRI values for the SPS-5 test sections were obtained from the RIMS, through the

regional contractors. Table 7.1.3 presents the SPS-5 projects for which IRI data were available

before and after rehabilitation of the pavement. This table also indicates the number of times

each project has been profiled after rehabilitation, the subgrade type~ the environmental region,

and the estimated ESALs/yr that will be applied to the test section As shown in table 7.1.3,

profile data are available for eleven SPS-5 projects. The number of times each project has been

profiled after pavement rehabilitation ranged from one to six. Table 7.1.4 presents the pavement

structure existing at the SPS-5 project locations prior to pavement rehabilitation.

For each SPS-5 project, the IRI was variable between the test sections. Figure 7.1.1

presents the IRI ofthe test sections prior to pavement rehabilitation for the projects located in

Georgia, Maryland, Mississippi, and California. As seen in this figure, the IRI of the test

sections for a given project was variable. The COY of the IRI of the test sections for each SPS-5

project are presented in figure 7.1.2. The COY of the IRI for the projects ranged from 10 to 33

percent.

The IRI values of each test section in each SPS-5 project before pavement rehabilitation

is presented in table 7.1.5. The average IRI for each SPS-5 project, computed by averaging the

IRI of the test sections, and the standard deviation of IRI of the test sections are also presented in

table 7.1.5. The difference between maximum and minimum IRI of the test sections in each

project is presented in table 7.1.6. As indicated in figure 7.1.2 and table 7.1.6, the IRI values at

the test sections within each project were variable. Large variations between the test sections

were noted at several SPS-5 projects. For example, in the SPS-5 project in California, section 1

had a IRI of 3.88 m/km (246 in/mi) while section 5 had a IRI of 1.56 m/km (99 in/mi). As

indicated in table 7.6.1, the difference between the maximum and the minimum IRI values of test

sections within a SPS-5 project ranged from 0.33 m/km (21 in/mi) for the project in Georgia to

2.32 m/km (147 in/mi) for the project in California.

-.150

~:::.

1 in/mi = 0.0158 m/km

Figure 7.1.1. IRI of test sections prior to pavement rehabilitation for selected SPS-5 projects.

'0

> 15

8

~ ~ ~ 00 ~ ~ ~ ~ ~ w ~

Project Location
an ar

Deviation of

IRI of Sections

IRI (in/mi)

Table 7.1.6. Difference between maximum and minimum IRI of test sections in an SPS-5

project before pavement rehabilitation.
Figure 7.1.3. presents the IRI before and after rehabilitation for four SPS-5 projects. As

seen in this figure, for a specific project, the IRI after overlay of the test sections fell within a

relatively narrow band, irrespective of the IRI before overlay. Figure 7.1.4 and 7.1.5 present the

00 before and after pavement rehabilitation for test sections 2 and 9, respectively, for all SPS-5

projects. A 50-mm- (2-in-) thick recycled AC overlay is placed on both these sections. In

section 2, minimum surface preparation is carried out prior to the overlay while in section 9

intensive surface preparation is carried out prior to the overlay. As indicated in these figures, a

unique relationship between the IRI before and after an overlay does not exist. As described

previously, the IRI after the overlay for a specific project fell within a relatively narrow range

irrespective of the IRI before the overlay. However, the limits of this range varied from project

to project. The factors that are expected to influence the IRI of the overlaid section are: profile of

the pavement prior to overlay, the predominant wavelengths in the section that contribute to the

00, and the capability of the contractor placing the overlay.

The 00 values before rehabilitation of each test section in a SPS-5 project was different.

If all test sections in each SPS-5 project had the same 00, a statistic such as the percentage

reduction in IRI could be used to compare the effectiveness in reducing the IRI associated with

each treatment. Because the 00 values of the test sections within a project were different, such a

statistic cannot be used to compare the effectiveness of each treatment in reducing the roughness

at a test section.

The 00 values at the test sections after the AC overlay are shown in table 7.1.7. The

average IRI after overlay for each SPS-5 project, computed by averaging the IRI of each test

section, is also shown in this table. The average IRI of the SPS-5 projects ranged from 0.50

m/km (32 in/mi) for the project in Georgia to 1.50 m/km (95 in/mi) for the project in Mississippi.

The project in Georgia had the lowest IRI before pavement rehabilitation of all SPS-5 projects,

with the average IRI being 1.04 m/km (66 in/mi). The average IRI values for each test section

computed across the SPS-5 projects are also presented in table 7.1.7. The average values for all

sections were generally close to each other, with the values ranging from 0.88 to 1.03 m/km (56

to 65 in/mi), and did not indicate a trend associated with a particular test section.

The average IRI values atter overlay for the test sections receiving minimum surface

preparation prior to overlay (sections 2 to 5) as well as for the test sections receiving intensive

surface preparation prior to overlay (sections 6 to 9) are presented in figure 7.1.6. For each SPS-5
=60

E

g 40

i 20

o

200

.-.150

E

C 100 0:-

i 50

o

140

120

=100

~ 80

;. 60

i 40

20

o

250

200

~ 150

;'100

i 50

o

4 5 6 7

Section Number

1 in/mi =0.0158 m/km

Figure 7.1.3. IRI before and after AC overlay for four SPS-5 projects.
100

80

-

I

t

60

"C

8 40

!oi(

Ii 20

• • •

•

\

.•.

•

•

•

1 in/mi = 0.0158 m/km

Figure 7.1.4. IRI after overlay versus IRI before overlay for test section 2 in SPS-5 projects.

100

80

~;. 60

t

~.. 40 •

~

Ii

20

• • • • •

•

• •

•

•

1 in/mi = 0.0158 m/km

Figure 7.1.5. IRI after overlay versus IRI before overlay for test section 9 in SPS-5 projects.
:state IRI After overlay Average :stanaara liuvof

Section Number IRlof Deviation of IRlof

2 3 4 5 6 7 6 ~ Sections IRlof Test

(in/mi) Sections Sections

(in/mi) (%)

Alberta 66 67 54 73 06 6~ of t>4 fil. 6 11

Arizona 66 OU 70 B1 00 Bil. OU t>o fil. 11 10

california 6U 66 05 45 01 03 4f t>4 Of 1U 1B

'coloraao 5~ 4~ 03 45 oB 40 41:1 OB Oil. 0 12

Georgia il.~13U 33 J4 il.f il.B 44 33 32 2 8

lIIalne 41:1 60 55 45 48 54 52 66 53 f 13

Maryland 88 65 57 64 47 50 5U 05 oil. 13 22

.Minnesota 54 48 71 68 69 54 03 4~ ou 11 18

MiSSISSiPPi 89 114 76 1U9 9U 6U 6~ 113 ~o 1B 19

,Montana 72 63 45 43 44 50 46 4t> Oil. 14 27

New Jersey 63 4il. 45 56 40 4~ 4f 4B OU 1U 1~

AVERAGE 65 61 60 60 56 59 56 61

100

90

• MnilTlJm

• Intensive

80

70 .-I

60

i 50

Sa

I! 40

•

~ 30

20

10

0

~ ~ ~ 00 ~ ~ ~ ~ ~ W ~

Project Location

1 in/mi = 0.0158 m/km

Figure 7.1.6. Average IRI after overlay for sections receiving minimum and intensive surface

preparation prior to overlay.
project, the average IRI values after overlay for both the minimum and intensive surface

preparation sections were close to each other. For eight projects, the average IRI after overlay of

the minimum surface preparation sections were higher than the intensive surface preparation

sections, with the magnitude of the difference ranging from 0.02 to 0.22 m/km (l to 14 in/mi),

with an average of 0.09 m/km (5.8 in/mi). In three projects, the average IRI after overlay of the

intensive surface preparation sections, was higher than the average IRI after overlay of the

minimum surface preparation sections, with the difference ranging from 0.015 to 0.044 m/km

(l to 2.8 in/mi), with an average of 0.027 m/km (1.7 in/mi).

The control section in the SPS-5 experiment is only subjected to routine maintenance

activities such as patching and crack sealing, carried out according to the standard maintenance

activities of the State in which the project is located. The IRI values of the control section in

each SPS-5 project before and after maintenance together with the change in IRI at the section,

which is the IRI after repair subtracted from IRI before repair, are presented in table 7.1.8. A

positive value for the change in IRI in table 7.1.8 indicates that the IRI of the section decreased

after pavement repairs, while a negative value indicates that the IRI of the section increased after

pavement repairs. The changes in IRI at the control sections, as indicated in table 7.1.8, were

generally small except for the projects in Colorado and California where the IRI of the section

decreased by 0.66 and 2.76 m/km (42 and 175 in/mi) respectively, after pavement repairs.

The number of times each SPS-5 project has been profiled after rehabilitation ranged

from one to six as shown in table 7.1.3. The rate of increase ofIRI at each test section that has

been profiled two or more times was computed using the equation presented below:

Rate of increase of IRI =IRI ill-IRI (0)

Time

where:

IRI (t) =

IRI (0) =

Time =

IRI of section at last profile date,

IRI of section immediately after pavement rehabilitation, and

time in years between the two profile dates.

The rate of increase of IRI for the SPS-5 projects in Alberta, Mississippi, Maryland, and

Colorado that are over 3.5 years old are presented in figure 7.1.7. This figure indicates different

trends for the four projects, and conclusions about the effectiveness of each treatment on the rate
Table 7.1.8. IRI before and after pavement rehabilitation for control section in the SPS-5

projects.

Note 1: Change in IRI = IRI before rehabilitation - IRI after rehabilitation.

Note 2: 1in/mi = 0.0158 m/km.

of increase of IRI cannot be made yet. Most test sections are not yet showing much change in IRI

after overlay and, as seen in figure 7.1.7, the rate of increase of IRI for most sections is very

small. The IRI values at the test sections for the SPS-5 projects in Arizona and Colorado for the

different profile dates are presented in figures 7.1.8 and 7.1.9, respectively. The first IRI value in

each figure is the IRI of the section prior to pavement rehabilitation. As shown in these figures,

changes in IRI at most sections after pavement rehabilitation are small.

The rate of increase ofIRI for all test sections in each SPS-5 project is shown in table

7.1.9. The age of the projects ranges from 1.9 to 4.7 years. A negative value for the rate of

change of IRI indicates that the IRI at the last profile date was lower than the IRI obtained after

the overlay. The average rate of increase ofIRI for each test section, computed by averaging the

rate of increase ofIRI across all SPS-5 projects, is also presented in table 7.1.9. The average

rates of increase of IRI for all overlaid sections are close to each other and, in general, the sections

with a 125-mm- (5-in-) thick AC overlay have a lower rate of increase ofIRI than the sections

with a 50-mm- (2-in-) thick overlay. The largest rate of change of IRI was noted for the control

section in Georgia. However, this section has been profiled only twice after rehabilitation,

immediately after the overlay and 1.9 years after the overlay. Therefore, the rate of increase of
5

Section Number
200

180

160

140

I

120

100

jg 80

60

40

20

0

2 3 4 5 6 7 8 9

SE:TION NUMBER

• Before Rehab

• After Rehab

OVear 1

.Vear2

1 in/mi = 0.0158 m/km

Figure 7.1.8. IRI for SPS-5 project in Arizona.

SPS-S - COLORADO

200

180

160

140

• Before Rehab

.-. 120 • After Rehab

~ 100 OVear 1

:::. .Vear2 jg 80 .Vear3

60 .Vear4

40

20

0

2 3 4 5 6 7 8 9

SE:TION NUMBER

1 in/mi = 0.0158 m/km

Figure 7.1.9. IRI for SPS-5 project in Colorado.
00 was based on these two points. IRI values at this section at future dates should be used to

investigate the rate of increase of 00 at this section to see if this rate of increase of 00 is

representative for the section.

~tate I Average IKIOT Age of Rate ot Change of IRI(in/mi/yr)

Project Before Project Surtacepreparation

Rehabilitation (yr) Icontrol I Minimumpreparation Intensive preparation

(in/mi) section Number

1 2 3 I 4 t) 0 ( 6 ~

uvenay Iype and InlCKneSS

un 2' 0" 0" 2n 2n on 5n 2"

R R V V V V R R

AlDerta 111 4.7 3.3 1.5 1.7 2.8 1.2 0.2 1.0 1.r 2.0

AriZOna 117 2.4 2.1 1.9 0.3 1.0 0.8 0.7 1.6 0.3 1.2

California 146 2.2 3.4 1.4 0.5 0.2 7.1 3.6 4.1 1.8 3.3

\,oloradO 119 3.9 3.0 0.0 0.1 0.1 0.8 4.2 2.3 0.7 0.6

Georgia 06 1.9 22.9 2.0 2.0 -1.7 0.5 1.1 0.8 -0.9 -0.2

Maryland 106 3.5 1.9 3.2 3.2 4.0 3.4 1.7 2.0 1.7 1.2

Minnesota 101 3.0 6.0 0.0 4.1 2.f 4.6 2.f 1.4 1.1 4.9

MISSISSippi 13~ 4.f 0.2 3.2 2.4 4.6 1.3 0.0 3.6 1.9 1.6

Montana 66 3.9 - 2.3 U.O U.~ 3.9 2.0 3.7 U.l 4.4

NewJersey 116 2.0 4.6 0.6 U.6 0.4 0.2 0.0 0.0 O.U U.5

AVERAGE 118 3.3 6.2 2.2 1.6 1.6 2.4 2.3 2.1 0.8 2.0

Note 1: V - virginAC. R - recycled AC.

Note 2: 1 in = 25.4 mm.

Note 3: 1 in/mi = 0.0158 m/km.

7.1.6 Summary for SPS-S Projects

,

Profile data were available for 11 SPS-5 projects, and the number of times these sections

have been profiled after rehabilitation ranged from one to six. The 00 values of the test sections

within a SPS-5 project were variable, and the COY of the 00 between the test sections for the

projects ranged from 10 to 33 percent. The data indicated that, irrespective of the 00 before

overlay of the section, the 00 after overlay of the sections for a specific project would fall within

a relatively narrow band. However, the limits of this range varied from project to project. For all

projects, the average 00 after overlay of the sections that received minimal and intensive surface

preparation were very close to each other. Most of the SPS-5 test sections have shown little

change in IRI after pavement rehabilitation. A detailed analysis of the rate of increase of 00

carried out on four projects that were over 3.5 years old did not indicate any common trends in

the increase of the 00.
The SPS-6 experiment was developed to investigate the ,effect of different rehabilitation

techniques performed on jointed concrete pavements. In the experimental design, preparation

and/or restoration of the existing pavement is classed into three levels: minimal, intensive, crack

and seat or break and seat. These preparation treatments are applied with and without an AC

overlay. The rehabilitation treatments applied to the test sections in the SPS-6 test sections are

presented in table 7.2.1. A detailed description of the surface preparation that is applied to the

test sections is presented in table 7.2.2. Each SPS-6 project consists of seven test sections and a

control section. The control section designated as section 1 receives only maintenance activities

that are needed to keep the section in a safe and functional condition in accordance with the

standard procedure ofthe State agency where the project is located. The monitored portion of

test sections 2 and 5 are 305 m (1000 ft), while that of the other sections is 152 m (500 ft).

eClon

Number

Note 1: In section 4, after the placement of the AC overlay, the AC surface

is sawed and sealed over the joints and working cracks of the PCC.

Note 2: 1 in = 25.4 mm.
x - Apply treatment as warranted

R&R - Remove and replace existing and apply additional as warranted

N - Do not perform

B - Full depth doweled patch or retrofit dowels in slots.

A - Apply treatment regardless of condtion or need.

The IRI values for the SPS-6 test sections were obtained from the RIMS, through the

regional contractors. Table 7.2.3 presents the SPS-6 projects for which IRI data were available

before and after rehabilitation of the pavement. This table also indicates the number of profile

runs available after rehabilitation, the subgrade type, environmental region, and the annual

ESALs in the test lane for each SPS-6 project. As shown in table 7.2.3, profile data are available

for nine SPS-6 projects. The number of times each project has been profiled after rehabilitation

ranged from two to five. Table 7.2.4 presents the existing pavement structure prior to pavement

rehabilitation for each SPS-6 project.
For each SPS-6 project, the IRI was variable between the test sections. Figure 7.2.1

presents the IRI of the test sections prior to pavement rehabilitation for projects located in

California, Indiana, Pennsylvania, and Oklahoma. As seen in this figure, the IRI of the test

sections for each project was variable. The COY of the IRI of the test sections for each SPS-6

project is presented in figure 7.2.2. The COY of the IRI for the projects ranged from 10 to 24
250

200

150

~

.-.

~

.N

:::. OPA

i 100 .OK

50

0

2 3 4 5 6 7 8

Section Number

1 in/mi = 0.0158 mIkm

Figure 7.2.1. IRI of test sections prior to pavement rehabilitation for selected SPS-6 projects.

25

20

l

15

i

'0

~

10

CJ

5

0

AZ CA L
percent. The IRI values of the sections in each SPS-6 project before pavement rehabilitation is

presented in table 7.2.5. The average IRI of the test sections in each project and the standard

deviation ofIRI for the test sections are also presented in table 7.2.5. Large variations in IRI

between the test sections exist in many projects. The difference between the minimum IRI and

the maximum IRI of test sections in a project is presented in table 7.2.6, and ranged from 0.49 to

1.55 milan (31 to 98 in/mi).

Table 7.2.6. Difference between maximum and minimum IRI of test sections in an SPS-6

project prior to treatment.
Figure 7.2.3 presents the IRI values before and after pavement rehabilitation for the test

sections that receive a AC surface for four SPS-6 projects. Test sections 3, 4, and 6 receive an

AC overlay, while test sections 7 and 8 are crack/break seated, and then an AC surface is placed.

As seen in figure 7.2.3, for the three sections receiving an overlay on the existing PCC surface

(sections 3, 4, and 6), the 00 values after the overlay for a specific project were close to each

other irrespective of the 00 before the overlay. Except for the project in Oklahoma, the other

three projects showed more uniformity of 00 after rehabilitation for all five sections. For each

project, the IRI of the test sections after the placement of the AC surface was within a narrow

band. However, the lower and upper limits of this band varied from project to project. Figures

7.2.4 and 7.2.5 present the 00 before and after the AC overlay for sections 3 and 6. Both

sections received a 100-mm- (4-in-) thick AC overlay. Prior to placing the overlay, section 3

received minimum surface preparation while section 6 received intensive surface preparation. As

seen from these figures, no unique relationship exists between 00 before and after overlay. The

factors that are expected to influence the IRI of the overlaid section are: profile of the pavement

,

prior to overlay, the predominant wavelengths in the section that contribute to the 00, and the

capability ofthe contractor placing the overlay.

The IRI values of the test sections in all SPS-6 projects after the placement of the AC

surface are tabulated in table 7.2.7. The average IRI of the test sections in eac~ project that

received an AC surface ranged from 0.90 to 1.18 m/km (57 to 75 in/mi). The variability of the

00 (after the placement of the AC surface) between the test sections can be characterized by the

standard deviation and COY that are presented in table 7.2.7. The COY of the 00 for the

projects ranged from 4 to 22 percent, with an average of 11 percent. The average 00 for each

test section averaged across the SPS-6 projects is also presented in table 7.2.7. These averages

were close to each other, ranged from 0.99 to 1.07 m/km (63 to 68 in/mi), and did not show a

specific trend associated with a particular treatment. The 00 before rehabilitation of each test

section within a project was different. If all test sections had the same IRI, a statistic such as the

percentage reduction in 00 could be used to compare the effectiveness in reducing the IRI

associated with each treatment. However, because the 00 of the test sections within a project

were different, such a statistic cannot be used to compare the effectiveness of each treatment in

reducing the 00.
150

=100

~;.

i

50

0

3

250

200

~ 150

;'100

i 50

o

140

120

=100

~ 80

;. 60

i 40

20

o

200

_150

;.~ 100

i 50

I_ Befae Rehab I

_ AftBr Overlay

I_ Befae Rehab I

_ After Overlay

I_ Befae Rehab I

_ AftBr Overlay

6

section Number

1 in/mi = 0.0158 m/km

Figure 7.2.3. IRI before and after placement of AC surface for four SPS-6 projects.

6

section Number

6

section Number

6

section Number
100

90

_ 80

! 70

~ ·60

~ 50

40

~

~ 30

i 20

10

o

o

•

.•. ~

•

•

•

•

•

1 in/mi = 0.0158 m/km

Figure 7.2.4. IRI after overlay versus IRI before overlay for section 3 in SPS-6 projects.

100

90

80

-

~

70

;..

60

~

'C 50

~•.. 40

•

~ 30

i

20

10

~ ~

•

.•.

•

..

•

.•. •

1 in/mi = 0.0158 m/km

Figure 7.2.5. IRI after overlay versus IRI before overlay for section 6 in SPS-6 projects.
Table 7.2.8 presents the IRI values before rehabilitation and after rehabilitation for the

three sections not receiving an overlay (Sections 1, 2, and 5). The change in IRI presented in this

table for each treatment is the IRI after rehabilitation subtracted from the IRI before rehabilitation.

Positive values for change in IRI indicate that the IRI of the section decreased after the treatment,

while negative values indicate that the IRI of the sections increased after the treatment. Section I

is subjected to routine maintenance according to agency practice, with section 2 being subjected

to minimal surface preparation, and section 5 being subjected to intensive surface preparation

(table 7.2.2 presented the activities associated with these treatments). In the SPS-6 projects, the

following observations were noted for the section receiving routine maintenance: (1) the IRI of

some sections decreased by a large amount, (2) the IRI of some sections remained at the relatively

same level, and (3) the IRI of some sections increased. Similar observations were noted for the

minimum restoration section (section 2). The guidelines for maintenance activities in these

sections may have been interpreted differently by the different agencies, and the amount of

maintenance activities carried out at the sections may have been different, resulting in these

observations. Reductions in IRI observed at all sections receiving intensive surface restoration

(section 5), except for the sections in Michigan and Indiana where the IRI increased after

rehabilitation. The IRI of section 5 should have decreased for all SPS-6 projects as the surface

should have been diamond ground after pavement rehabilitation. 'However, test section 5 in the

SPS-6 project in Michigan and Indiana had not been diamond ground. Patching was performed on

both these projects and the increase in IRI is likely due to roughness created at the interfaces
Table 7.2.8. IRI before and after maintenance for sections not receiving an AC surface in the

SPS-6 experiment.

Note 1: Change = IRI before rehabilitation - IRI after rehabilitation.

Note 2: 1 in/mi = 0.0158 m/km.

between the patches and the original pavement. The rehabilitation files in the database do not

contain this information yet, and an evaluation of the type of maintenance activities carried out at

each section could not be studied in detail.

The number of times each SPS project has been profiled after rehabilitation, as shown in

table 7.2.3, ranged from two to five. The rate of increase ofIRI at each section was computed by

subtracting the IRI obtained immediately after pavement rehabilitation from the latest available

IRI, and dividing the value by the time period between the two observations (see section 7.1.5).

This provides a rate of increase ofIRI in terms of inches per mile for a section. The rate of

increase ofIRI is presented in figure 7.2.6 for four SPS-6 projects that are more than 3 years old.

A negative rate of increase of IRI at a test section indicates that the IRI at the last profile date is

less than the IRI after pavement rehabilitation. Most test sections are not yet showing much

change in IRI after the overlay, and small changes in IRI can be masked by variability in profile

measurements between the years. Trends in IRI development at the test sections cannot yet be

seen across all sections because of this variability.
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Figure 7.2.6. Rate ofincrease ofIRI for selected SPS-6 projects.
Section 6 received a lOO-mm (4-in) overlay after intensive restoration of the existing

PCC surface, but its rate ofIRI development was generally comparable to section 3, which

received the same overlay thickness but with minimum restoration of the existing surface (see

figure 7.2.6). Section 4 received a lOO-mm- (4-in-) thick AC overlay after minimum restoration

of the existing PCC surface, but the AC surface was sawed and sealed over joints and working

cracks. As seen in figure 7.2.6, this section had a lower rate of increase in IRI when compared

with sections 3 and 6.

Figures 7.2.7 and 7.2.8 present the IRI values for the SPS-6 projects in Illinois and

Michigan for the different profile dates, respectively. As seen in these figures, most of the

sections are not yet showing much change in IRI after pavement rehabilitation. This

phenomenon was also noted for the other SPS-6 projects.

The computed rates of increase ofIRI for the test sections at all SPS-6 projects are

tabulated in table 7.2.9. The time period in years between the latest profile date and the profile

date immediately after construction is presented as the age of the project in this table.

Four projects presented in table 7.2.9 that have the age of project listed as less than 2

years old have been profiled only twice after pavement rehabilitation. The rates of increase in

IRI for these projects were computed from these two IRI values. As the profile data for the SPS-6

projects are available for only a limited time period, the rates of increase in IRI values presented

in table 7.2.9 should be considered as preliminary values.
200

175

150

125

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Figure 7.2.7. IRI values for SPS-6 project in Illinois.

• Before Rehab

• After Rehab

oVear 1

• Year 2

.Vear3

• Vear 4

1 in/mi = 0.0158 m/km

Figure 7.2.8. IRI values for SPS-6 project in Michigan.
Profile data were available for nine SPS-6 projects, and the number of times these

sections have been profiled after rehabilitation ranged from two to five. The IRI of the test

sections within a SPS-6 project prior to pavement rehabilitation was variable, and the COY of

the IRI between the test sections for the projects ranged from 10 to 24 percent. Three sections in

an SPS-6 project received an AC overlay, while two sections received an AC surface after being

crack/break: seated. In general, for a specific project, the IRI values for all test sections that

received an AC surface (overlaid and crack and seated sections) was within a relatively narrow

band. The lower and upper limits of this band varied from project to project. The capability of

the contractor and the wavelength content of the existing pavement are likely to be important

factors in determining the IRI after the placement of the AC surface. Most of the test sections in

the SPS-6 projects are showing little change in IRI after pavement rehabilitation, and overall

trends across all sections cannot be seen. The variability of profile measurements between the

years may be masking the small changes in IRI that occur at most of the sections. Three test

sections in this experiment received an AC overlay of 100-mm (4-in) thickness. Prior to the

overlay, two sections received minimal surface preparation while one section received intensive

surface preparation. On one section that received minimal surface preparation, the AC overlay

was sawed and sealed over joints and working cracks. The rate of increase ofIRI of this section

was lower than the rate of increase ofIRI of the other two sections that received the AC overlay.

CHAPTER 8: RECOMMENDATIONS FOR QUALITY ASSURANCE AND

PROFILING FREQUENCY

The current profile data collection procedure at GPS sections requires the collection of

five profiler runs that satisfy the following two criteria for the both-wheel-path 00 (average of

left- and right-wheel-path IRI):

1. The 00 of three pro filer runs should be within 1 percent of the mean 00 computed from

the five runs.

2. The standard deviation ofIRI of the five pro filer runs should be within 2 percent of the

mean 00.

If the first five pro filer runs that are obtained do not satisfy the above criteria, additional

pro filer runs, up to a maximum of nine runs, are performed at the section in order to obtain five

pro filer runs that satisfy the two criteria. After performing nine profiler runs, if five runs that

satisfy the criteria cannot be obtained, the five best pro filer runs are selected.

The criterion that the IRI of the five pro filer runs should be less than 2 percent of the

mean IRI is similar to stating that the COY of the five runs should be less than 2 percent. At the

beginning of the LTPP program this criterion was at 3 percent, and later it was reduced to 2

percent. The COY of the 00 values obtained from the five pro filer runs at a section were

analyzed previously (see section 3.4 of this report). These COY values had the cumulative

frequency distribution that is shown in table 8.1.1. The data for which the COY were analyzed

were all collected during or prior to 1993, and most of the data would have been collected when

the COY criterion was 3 percent. At an average of94 percent of the test sections, the COY of

IRI was less than 3 percent. The values shown in table 8.1.1 indicate that for each profile date at

a GPS section, the profile data that are collected are extremely repeatable with respect to 00.
The examination of time-sequence IRI data at the GPS sections was described in chapter

2. At some GPS sections inconsistent IRI values were observed for some profile dates, and the

cause of the inconsistency was attributed to one ofthe following factors:

1. Temperature or seasonal effects

2. Maintenance activities at the section

3. Variations in the profiled path

4. Data collection errors

Inconsistent IRI values that are caused by the first two factors cannot be avoided.

However, inconsistencies in IRI caused by data collection errors can be avoided, and it may be

possible to minimize inconsistencies caused by variations in the profiled path. The current

profile data collection criteria at GPS sections ensure that five repeatable runs are collected when

each section is profiled. However, it is possible to collect five repeatable runs at a section when

the equipment is not working properly. For example, even if the sensors are incorrectly

calibrated, five repeatable runs can be obtained at the section. At sections where transverse

variability exists, it is possible to obtain five profile runs that satisfy the data collection criteria,

and yet obtain an IRI value that is substantially different from the IRI value that was obtained

when the section was profiled previously because a different wheel path was followed.
We recommend that the following procedures be adopted during profiler operations to

minimize the variability in profile data caused by transverse variability at test sections and to

eliminate data collection errors.

A consistent wheel path should be profiled at each section in order to identify changes in

roughness at a section over the years. At sections that have a transverse variability in profile, or

at sections where distresses are located close to the wheel paths, variations in the profiled path

can have a large influence on the IRI value. A regional contractor may utilize different drivers

for profiling within a year, and also between the years. The interpretation of the wheel path

could be different between the drivers. All pro filer drivers in each LTPP region should be

trained to follow a consistent path when the sections are being profiled, in order to ensure that the

interpretation of the wheel path is consistent between the drivers. The training of the drivers

could include videotaping the wheel path followed by the driver to ensure all drivers in each

region interpret a consistent wheel path.

After the profile data are collected at the test site, the IRI that is reported at 30-m (tOO-ft)

intervals for the different runs should be compared to see that they are comparable between the

runs. Then the IRI values for the left and the right wheel paths should be compared with the IRI

values that were obtained during the previous profile date. If the change in IRI that is noted for a

wheel path is greater than the limits presented in table 8.1.2, the operator should verify that these

differences are not caused by equipment errors. If the cause of the discrepancy is attributed to

equipment error, the problem should be corrected and additional pro filer runs should be obtained

at the test section.

100 - 200

>200
Two analysis methods were used to identify GPS sections that showed variations in IRI

over time. A linear regression analysis (described in section 5.1) was used to identify GPS

sections that showed a statistically significant linear relationship between IRI and pavement age

(determined from profile dates). At some GPS sections, the IRI showed variability between the

profile dates, and the COY of IRI between the years was used to identify the sections that

showed variable IRI patterns.

The percentage of sections that showed a statistically significant positive or negative

linear relationship between IRI and pavement age for each GPS experiment are presented in table

8.2.1. Appendix B presents the following information obtained from the linear regression

analysis for each GPS section: the intercept, slope (which is the rate of increase oflRI), the

coefficient of determination (R2), and if the t-test determined that a linear relationship existed

between IRI and pavement age. The IRI values for each GPS section are also presented in

appendixB.

Table 8.2.1. Percentage of GPS sections showing statistically significant linear

relationship between IRI and pavement age.
At some GPS sections the 00 showed an inconsistent pattern. For example, for a specific

year the 00 value would be higher or lower than the 00 observed for the previous and the

subsequent year, or the 00 was variable across the years. At each GPS section, the variability of

the time-sequence 00 values was examined by computing the COY of the 00. High COY

values would be obtained at the sections where high variability in 00 exists between the profile

dates. The COY values computed for each GPS section are presented in appendix B. The

percent distribution of COY of the test sections that did not show a statistically significant linear

relationship between 00 and pavement age, which fall into the different ranges of 00, are

presented in table 8.2.2. These values indicate there are sections that show very high variability

in 00 across the years, as well as sections that show very low variability in 00 across the years.

Table 8.2.2. Distribution of COY of GPS sections not exhibiting a statistically significant linear

relationship according to ranges of COV.

Table 8.2.3 presents the recommended profiling frequency for the GPS sections. The first

priority for profiling should be given to the test sections that show a statistically significant linear

increase or decrease in 00 with time, and for the sections that show high variability in IRI

between the years. We recommend that the sections that show a statistically significant linear

behavior and the sections that have a COY greater than 6 percent be profiled annually. The

6 percent value was selected on the basis of the distribution of COY values presented in table

8.2.2 and the COY values presented in appendix B. For each GPS section, the regression

analysis computes a slope, which indicates the rate of increase ofIRI at that section. Ifa section

does not show a statistically significant linear relationship and if the COY is not greater than 6

percent, but the slope is greater than 0.05 m/km/yr (3 in/mi/yr), these sections should also be
Priority Frequency DeScription Of IRI t"attern

1 Once a Year 1. Test sections that show a statistically significant linear relationship

between time and IRI

2. Test sections that have a COy greater than 6 percent for

time-sequence IRI values

3. Test sections that do not satisfy criteria 1 or 2, but have a

a rate of increase of IRI greater than 3 in/mi/yr from

linear regression analysis of time-sequence IRI

4. Test sections that have been profiled less than three times

2 Once a Year Test sections that have a COy between 4 and 6 percent

for time-sequence IRI values

3 Once in 2 Test sections that have a COY of 4 percent for time-sequence

years IRI values

Note 1: Closely monitor the IRI history of the section. If a sudden increase in IRI is noted

at a section, this may indicate that the section is starting to deteriorate rapidly and these sections

should be monitored annually. The following guidelines are offered to define a sudden increase

in IRI when compared with the previous IRI value:

(a) IRI < 100 in/mi: 5 in/mi

(b) IRI between 100 and 200 in/mi: 10 in/mi

(c) IRI > 200 in/mi: 15 in/mi.

Note 2: 1 in/mi = 0.0158 m/km.

profiled annually. As indicated in appendix B, most ofthe sections that have a slope greater than

0.05 mlkm/yr (3 in/mi/yr) show a statistically significant linear relationship, or have a COy that

is greater than 6 percent. The test sections that have been profiled less than three times should

be profiled annually until their IRI behavior pattern can be identified.

The second priority for profiling should be given to the sections that have a COY between

4 and 6 percent; these sections may be profiled annually. The last priority should be given to the

sections that have a COY less than 4 percent; these sections may be profiled once in 2 years. The

IRI history of the sections should be monitored closely to see if a sudden increase in IRI is noted

at a section. This may indicate that the section is starting to deteriorate rapidly, and such sections
should be monitored annually. The following guidelines are presented to defme a sudden increase

in IRI when compared with the previous IRI value for different IRI ranges: (1) IRI < 1.58 m/km

(100 in/mi) - 0.08 m/km (5 in/mi), (2) IRI between 1.58 and 3.15 m/km (100 and 200 in/mi) - 0.16

m/km (10 in/mi), and (3) IRI> 3.15 m/km - 0.24 m/km (15 in/mi).

The time-sequence IRI values available at the GPS sections ranged from 3 to 7. The

statistical tests as well as the COY analysis could be affected by the number of data points that

were available for each test section. Therefore, the more recent IRI values that are available for

the test sections should be used to update the information presented in appendix B.

In the SPS experiments that were evaluated in this study (1,2,5, and 6) the timesequence IRI data were variable at some projects. This variability is attributed to seasonal

variations, and for the SPS-2 projects, temperature appears to be a key factor that affects the 00.

A better understanding of the IRI behavior of new pavements could be obtained by profiling the

SPS-l and SPS-2 projects twice a year. This will provide an understanding of how seasonal

variability affects changes in IRI of new pavements, and the collected profile data can also be

analyzed to determine the changes in wavelengths that occur in these sections. Extensive

laboratory and field testing was performed for SPS-l and SPS-2 projects, and this information

can be used with the profile data to understand the behavior of new pavements.

On the basis of analysis of time-sequence IRI data, recommendations for data collection

were presented in this chapter. One recommendation is to train the pro filer drivers to ensure that

the interpretation of wheel path at a test section is consistent between the different pro filer

drivers in each LTPP region. This will ensure that consistent data are collected at the test

sections over the years. In addition, guidelines are provided to ensure that the collected data are

free of equipment errors. On the basis of information obtained from a statistical analysis,

recommendations for profiling frequency at GPS sections were developed, and these were

presented in table 8.2.3. Given the variability in IRI values that were noted at SPS-l and SPS-2

projects, we recommend that these projects be profiled twice a year.

The six main tasks that were addressed in this study were:

1. Investigate development of roughness at GPS sections

2. Compare International Roughness Index and Ride Number for GPS sections

3. Develop roughness models for GPS sections

4. Investigate roughness characteristics of SPS-l and SPS-2 projects

5. Investigate roughness characteristics ofSPS-5 and SPS-6 projects

6. Provide recommendations for quality assurance and profiling frequency

The collection of profile data at the GPS sections commenced on or after 1990. The

profile along the left and right wheel paths at the GPS sections are collected by a K.J. Law

pro filer that is equipped with optical sensors. The GPS sections are generally profiled annually.

Although the time difference between two consequent dates when a section is profiled is close to

a year, differences of several months can be present. This results in data being collected during

different seasons of the year, and therefore the profile collected between the years can include

seasonal variability effects.

The development of roughness at GPS sections was studied using the IRI as the roughness

index and the time-sequence IRI data that are available for each GPS section. For each profile

date at a GPS section, the IRI of five profiler runs are available. The IRI of the five pro filer runs

for a given profile date were averaged to obtain the average IRI for each wheel path. For each

GPS section, these data were used to prepare plots showing the variation ofIRI along the left and

the right wheel paths with pavement age. The IRI patterns that were observed for the sections

were: (1) IRI along both the left and the right wheel path or along one wheel path increases with

time, (2) IRI remains relatively stable over time with minor variations between the profile dates,

(3) IRI of both the left and the right wheel path or one of the wheel paths for a given profiling date

is higher than the IRI obtained for the profiling dates before and after this date, (4) IRI of both the

left and the right wheel paths or one of the wheel paths was lower than the IRI obtained for the

profiling dates before and after this date, (5) IRI of both the left and the right wheel paths for

different profiling dates were variable, and (6) an abrupt decrease in IRI was observed for both

wheel paths, and the IRI at the subsequent dates remained close to this value. The sections that
showed a IRI pattern similar to cases 3 through 6 above were classified as sections that showed

inconsistent IRI values; 27 percent of GPS sections showed such behavior.

For cases 3 through 5, the variability of the IRI may have been caused by variations in the

profiled paths between the years or by temperature or seasonal effects. In jointed PCC

pavements, variations in the profile of the pavement can occur due to temperature effects. The

profile of the pavement can also change because of frost heave and changes in moisture content

ofthe subgrade that cause the subgrade to swell or shrink. For pavements that have transverse

variability or that have distress adjacent to or along the wheel paths, the IRI will be affected by

the path that is profiled. For case 6 where the IRI ofthe section drops and remains at that level at

subsequent profile dates, the change in IRI can be caused by maintenance activities at a section.

However, it should also be noted that maintenance activities at a section could also cause the IRI

of the pavement to increase (e.g., a patch that introduces a bump).

In this study, a comparison between the IRI and RN computed by the method developed

by Spangler and Kelly(l)was performed for the GPS sections. The IRI was developed from the

data obtained from the International Road Roughness experiment that was sponsored by the

World Bank.(3) The IRI is sensitive to wavelengths between 1 and 30 m (4 and 100 ft). The RN

is a roughness index that is correlated with the subjective opinion ofthe road users, and

procedures for computing the RN are presented by Spangler and Kelly.(l) The RN assigns a

score between 0 and 5 for a pavement, and is computed by an algorithm that provides a method

to predict mean panel ratings from measured profiles. The data from two studies that were

sponsored by the NCHRP and two studies funded by the Ohio Department of Transportation

were used in developing RN.(1,7,8,9)Only wavelengths in the range of 0.48 and 2.4 m (1.6 and 8

ft) are considered in the computation ofRN. The RN computation method requires road profile

data measured in two wheel paths at 25-mm (I-in) intervals, averaged over a 305-mm (12-in)

distance, and stored at 152-mm (6-in) intervals. The data collected by the K.J. Law profilers at

GPS sections satisfy these requirements.

The IRI computed from the profile data is available in the NIMS database. However, the

RN is not available in the NIMS. Profile data obtained from NIMS were used to compute the

RN of the profiles. For each GPS section five profile runs are available for each profile date. In

the comparison between IRI and RN, the average value obtained for each parameter for the five

runs was used.
Separate comparisons between 00 and RN were performed for experiments GPS-l

through GPS-5. These comparisons indicated that a unique relationship between the IRI and RN

does not exist. The 00 is sensitive to wavelengths between 1.2 and 30 m (4 and 100 ft), while

the RN is sensitive to wavelengths between 0.48 and 2.4 m (1.6 and 8 ft). There could be a range

of 00 values that correspond to a specific value of an RN. For example, consider two profiles

that have similar short wavelength contents, but different long wavelength contents. As the RN

is sensitive to the short wavelength content, the two profiles can have the same RN, but because

the long wavelength contents are different, they can have different IRI values. For GPS-l and

GPS-2 sections, the spread in IRI values associated with a given RN increased with decreasing

RN values, with this effect being more pronounced for RNs less than 3. For the PCC pavements

(G~S experiments 3 through 5), the band of 00 values associated with a specific RN was much

narrower.

The study of trends in IRI and RN at selected sections indicated that these two parameters

generally have similar trends. As the IRI has a maximum sensitivity at wavelengths of 2.1 m (7

ft) and 15.2 m (50 ft), short wavelength content also has a significant contribution to the 00.

Therefore, short wavelength contents that are between 0.48 and 2.4 m (1.6 to 8 ft) that reduce the

RN will cause an increase in the IRI.

The algorithm presented by Spangler and KellyCl)for computing the RN is only valid for·

profiles obtained from pro filers that obtain data at 25-mm (I-in) intervals, and then apply a 305-

mm (12-in) moving average and record data at 152-mm (6-in) intervals. Currently, only K.J.

Law pro filers satisfy this data collection criterion, and this limitation prevents the RN from being

computed for profiles collected by other profiling devices. The UMTRI developed a new ride

index called the Rideability Index in a research project that was sponsored by the FHW A.C10).

The UMTRI final report documenting the research was published in June 1996, after the analysis

for this study had been completed. The Rideability Index developed by UMTRI is not restricted

to a specific profiling sample interval and can be computed for any data sampling interval. The

Rideability Index is influenced by wavelengths ranging from 0.6 to 9.1 m (2 to 30 ft). For a

profile, the RN computed from the Spangler and Kelly algorithm is not equal to the Rideability

Index developed by UMTRI. This is because the wavelengths and the gain factors that are used

by the two indices are different. However, there is a very good correlation between these two

indices. As there was a very good correlation between the RN and the Rideability Index,

relationships between the Rideability Index and the 00 are expected to be similar to the

relationships that were observed for the RN. Currently, an ASTM standard for computing the
RN, which uses the algorithm for Rideability Index developed by UMTRI, has been proposed

and is going through the balloting process of ASTM.

This section will present the overall trends in development of 00 that were observed for

each GPS experiment, describe models that were developed to predict 00, and describe general

observations on factors that related to 00 for each GPS experiment.

The first step in investigating the general trends in 00 was to study plots of 00 versus

pavement age for each GPS experiment. Four time-sequence 00 values that had been obtained

over a 4-year period were on average available for the GPS sections. The 00 versus age of

pavement plots indicated that most pavement sections showed little change in 00 over the

monitored period. A linear regression analysis was performed between the time-sequence 00

values and the pavement age at each GPS section to identify the sections for which a linear

relationship exists between the two parameters. The percentage of sections for which a positive

or negative linear relationship existed between 00 and pavement age are presented in table 9.3.1.

All GPS sections that had 3 or more years of data were used in this analysis. The frequency

distribution of GPS sections according to the slope obtained from linear regression analysis is

presented in table 9.3.2.

Table 9.3.2 shows that for each GPS experiment a large number of sections had either a

negative rate for the increase of 00 or a low rate that was between zero and 0.03 mlkm/yr (2

inlmi/yr). The available 00 data indicated that the GPS sections have been profiled an average of

four times, and a rate of increase of 00 of 0.03 mlkm/yr (2 inlmile/yr) corresponds to a change of

0.13 m/km (8 inlmi). This change represents a small value when considering the changes in 00

that can occur between the years resulting from environmental effects and variability in the

profiled path. For nonoverlaid PCC pavements (GPS-3 to GPS-5), the percentage of sections

that had a rate of increase of 00 less than 0.03 mlkm/yr (2 inlmi/yr) or a negative rate were:

GPS-3: 72 percent, GPS-4: 73 percent, and GPS-5: 91 percent. For nonoverlaid AC pavements

(GPS-l and GPS-2 sections), the percentage of sections exhibiting a similar behavior were
Table 9.3.1. Percentage ofGPS sections showing statistically significant linear relationship

between IRI and pavement age.

Table 9.3.2. Distribution of sections according to rate of increase ofIRI obtained from linear

regressIon.
48 percent and 55 percent, respectively. GPS-6B and GPS-7B are overlaid pavements, and

virtually all of these are less than 5 years old: 71 percent of GPS-6B and 81 percent of GPS-7B

sections exhibited a growth rate that was negative or less than 0.03 m/km/Yr (2 in/mi/Yr).

The IRI data that were available in the database when this study commenced indicated that

the GPS sections have on average been profiled four times over a 4-year period. For a majority of

the sections, changes in IRI were minimal over this period. The pavements in the LTPP program

should see increase in roughness over time resulting from traffic and environmental effects.

However, the trend in roughness development cannot be observed for most of the sections from

the data that are available for such a short period. In the short-term data, variability caused by

seasonal effects and profile measurements can mask the increase in IRI that occurs at the section.

Long-term monitoring data are needed to observe the true trend in development of roughness.

The roughness models that were developed in this study were based on a data set that, on

average, had four time-sequence IRI values over a 4-year period. The models reflect the behavior

pattern observed for the analyzed data set, and could reflect the biases that were present in the

data set. The developed models have not been verified using an independent data set. For the

GPS sections that were used in the analysis, the applicability of the models in predicting the IRI

can be evaluated when more time-sequence data become available. However, these models can be

used to gain an insight into the factors that affect the IRI development at the analyzed sections.

The GPS sections in the LTPP program were established on in-service pavements, and the IRI of

the test section after construction is not known. The time-sequence IRI values at the test sections

are available starting from some point within its service life.

In this study, an optimization technique was used to develop models to predict IRI ofGPS

experiments 1 through 4. The models predict the initial IRI of the pavements with the use of

subgrade and structural properties of the pavement, and then predict a growth rate for the section

that is a function of time, traffic, subgrade properties, climatic properties, and pavement structure.

In the modeling process, the aim was to predict a model that would fit the time-sequence IRI data

that were available for a site. The developed models are sensitive to input parameters, and care

should be taken not to use input parameters that are unrealistic or beyond the range of data from

which the model was developed.
For GPS-I pavements (AC pavements on granular base), eight models were developed.

Four environmental zones, which correspond to the LTPP environmental zones, were used in the

analysis. These environmental zones are: wet freeze, wet-no freeze, dry freeze, and dry-no

freeze. In the analysis, the dry zone was defined to be an area that received less than 508 rom (20

in) of annual precipitation, and areas receiving a higher precipitation were in the wet zone. The

no freeze zone was defined to be areas that had an annual freezing index less than 89°C days

(160°F days), and areas higher than this were in the freeze zone. For the GPS-I sections in the

wet zones, separate models were developed based on the percentage of material passing the

number 75-l.lm (No. 200) sieve (P200). For GPS-I sections separate models were developed for

the following cases: (I) dry freeze, (2) dry-no freeze, (3) wet freeze, P200 greater than 50

percent, (4) wet freeze, P200 between 20 and 50 percent, (5) wet freeze, P200 less than 20

percent, (6) wet-no freeze, P200 greater than 50 percent, (7) wet-no freeze, P200 between 20 and

50 percent, and (8) wet-no freeze, P200 less than 20 percent.

For GPS-2 pavements (AC pavements on stabilized base), five base types were available.

These base types are: cement-aggregate mixtures, hot mix AC, asphalt-treated mixtures, soil

cement, and lean concrete. Models were developed for the different base types except for lean

concrete, which did not have sufficient sections for building a model.

Three models were developed for GPS-3 pavements (JPCP). For the wet regions,

separate models were developed for doweled and undoweled pavements. For the dry regions,

one model was developed as sufficient sections were not available to build separate models. One

model was developed for all GPS-4 pavements (JRCP) .

The GPS-5 sections showed a relatively constant IRI over time, and multiple linear

regression was used to develop a model to predict 00. However, this model has a very low

coefficient of determination (R2= 0.14).

GPS-6B and GPS- 7B sections are generally less than 5 years old. Very little change in

IRI has occurred at most of these sections after the overlay. Therefore, a statistical analysis on

the data cannot be performed yet. For GPS-6A, -7A, and -9 sections, a linear regression analysis
was performed on the time-sequence IRI data to determine the rate of increase of 00.

Correlation analyses were performed separately for each experiment to relate the rate of increase

oflRI with parameters that could affect the increase of 00. For GPS-7 A and GPS-9

experiments, strong and logical correlations were not observed, and models to predict 00

development could not be developed. For GPS-6A sections, a relationship between the rate of

increase of IRI and the minimum surface modulus was developed by considering all sections,

while a relationship between the rate of increase of IRI and SN was developed for the sections

that had a rate of increase ofoo·that was greater than 0.03 rnlkm/yr (2 in/mi/yr). However, both

these relationships had low R

2

values and high standard errors of estimates.

The analysis oflRI data indicated that flexible pavements generally start offwith lower

roughness but develop roughness more quickly and at lower traffic volumes than rigid pavements.

Climatic conditions and subgrade conditions showed a strong relationship with IRI for GPS-l

sections. Performance of GPS-l sections over fine-grained soils was strongly related to Atterberg

limits and the percentage of sub grade passing the 7S-J.1m(No. 200) sieve (P200). Good

performance was indicated for sections that rested on subgrade that were clayey sand with 10 to

30 percent P200 content and lower moisture contents. Generally higher roughness is indicated in

sections that are located in areas of higher precipitation. In freezing environments, pavements in

areas that have a high freeze index or high freeze-thaw cycles had higher 00 values. In freezing

environments, the data indicated that adequate frost protection is an important factor for good

performance. In hot climates, the 00 value of the pavements was strongly related to days above

32°C (90°F). Sections in very hot environments generally had higher 00 values, but better

performance was indicated for sections that are subjected to higher precipitation. This may be

related to the cooling effect on AC from precipitation. For the analyzed data set, traffic effects on

00 development were mostly seen for sections that had lower SNs.

The IRI behavior trends observed for the GPS-2 data set were generally similar to that

observed for GPS-l sections. It was noted that some GPS-2 sections located in freeze regions

did not perform well. The use of a stabilized base results in a lower total pavement thickness.

This has a significant effect on the frost heave potential for pavements located in freezing zones.
Poor performance was also noted for GPS-2 sections that were located on high-moisture-content

clay sub grades. Analysis of GPS-2 sections that contain cement stabilized bases indicated that

the best environment for these types of bases from an 00 point of view is the wet-no freeze

environment.

The overall analysis of all rigid pavements in the LTPP database shows that JPC

pavements with short slab lengths (GPS-3) have the highest 00, while CRC pavements have the

lowest 00, with the JRC pavements falling within these two limits. This observation indicates

that transverse joints and cracks have a large influence on the 00.

GPS-3 Sections: Sub grade and climatic properties were seen to have a strong influence in the

performance of the GPS-3 sections. It was noted that the IRI of undo we led jointed pavements

was closely related to the amount of faulting at a given site. Better performance can be achieved

for these pavements by designing them to minimize faulting. For PCC pavements located on

subgrades that are susceptible to pumping, an adequate base and subbase should be provided to

prevent pumping. A strong relationship between 00 and faulting was not seen for doweled

pavements. Lower water to cement ratios and higher cement content appear to indicate higher

initial IRI values, but the rate of development of 00 of these pavements seems to be low. It

appears that some sections that had a higher PCC split tensile strength while having a relatively

low PCC modulus provided good performance.

GPS-4 Sections: Subgrade and climatic properties were seen to have a major influence on the

performance of these pavements. Sections that had thicker PCC slabs had hi~er IRI, and this

factor is likely to be related to construction of the pavement Higher precipitation as well as

higher subgrade moisture contents indicated higher IRI values. This may be related to erosion

and faulting potential of the pavements. Increased joint spacing also indicated higher 00 values.

Longer joint spacing would likely result in a greater proportion of transverse discontinuities

(cracks and joints) that could increase the potential for faulting. The 00 value was related to the

water to cement ratio of the PCC mix, with higher water to cement ratios resulting in smoother

pavements. This can be explained in terms of workability because a mix with a low water to

cement ratio would be less workable. However, such a mix is expected to be more durable over

the long term. It appears that sections that had a higher PCC split tensile strength while having a

relatively low PCC modulus provided good performance.
GPS-5 Sections: The GPS-5 sections showed a nearly constant IRI over the monitored period.

This behavior was observed for young pavements (less than 5 years old) as well as for old

pavements (greater than 20 years). For all climatic regions, a higher percentage of longitudinal

steel and higher water to cement ratios indicated lower IRI values, while higher values of PCC

elastic modulus indicated higher IRI values.

The analysis of GPS-6B and GPS-7B pavements for which IRI before and after the

overlay were available indicated that relatively thin overlays can reduce the IRI of a pavement by

a large amount. For example, a 100-mm- (2.5-in-) thick AC overlay reduced the IRI of a flexible

pavement from 3.15 to 0.63 m/km (200 to 40 in/mi). Similarly, a 84-mm- (3.3-in-) thick overlay

reduced the IRI of a PCC pavement from 2.68 to 0.87 m/km (170 to 55 in/mi). A review of the

IRI data at these sites indicated there is little effect of the roughness prior to overlay on the

roughness immediately after the overlay. Generally, for most cases, the IRI after the AC overlay

of the pavement fell within a relatively narrow band, irrespective of the IRI value of the

pavement before the overlay. The IRI after overlay of the GPS-6B sections ranged from 0.42 to

1.80 m/km (27 to 114 in/mi) with 73 percent of the sections having an IRI less than 1.10 m/km

(70 in/mi). For GPS-7B sections, the IRI after the overlay ranged from 0.73 to 1.58 m/km (46to

100 in/mi), with 76 percent of the IRI values being less than 1.10 m/km (70 in/mi). However,

sufficient time-sequence IRI data are not available for the GPS-6B and GPS-7B experiments to

see how the rate ofIRI development is affected by the IRI before the overlay.

The trend in the data of the GPS-6A versus GPS-6B experiments indicates on a

qualitative basis that roughness starts to develop after about 10 years of service. The continued

monitoring of these sections is considered important. It could be seen that the trend in increase

in IRI after overlaid pavements after 10 years is a larger rate of increase than the GPS-1 or GPS2 pavements.

There are six different combinations of original pavement type and overlay type for the

GPS-9 experiment (e.g., CRCP overlay of JRCP, JPCP overlay of JPCP, etc.). Because there

were few GPS-9 sections (28 sections) and six combination of pavement types, specific trends in

IRI development could not be observed for this experiment.
The SPS-1 and SPS-2 experiments are designed to study the effect of specific design

features on pavement performance. Each SPS experiment consists of multiple test sections. An

SPS-1 project consists of AC-surfaced test sections that have different layer types and thicknesses.

An SPS-2 project consists of jointed PCC test sections that have different slab thicknesses, PCC

strengths, and base types. The investigation of roughness characteristics at SPS-1 and SPS-2

projects provided information on roughness characteristics in newly constructed pavements.

The SPS-1 experiment was developed to investigate the effect of selected structural factors

on long-term performance of flexible pavements constructed on different subgrade types in

different environmental regions. The subgrade types considered in this experiment are classified

as fine grained and coarse grained. The environmental regions considered are wet freeze, wet-no

freeze, dry freeze, and dry-no freeze. There are 24 test sections in an SPS-1 experiment. These 24

test sections are to be constructed for each combination of subgrade type and environmental

region, with test sections 1 through 12 being constructed at one site, and test sections 13 through

24 being constructed at a different site. The structural factors considered in this experiment are:

surface layer thickness, base type, base course thickness, and drainability. Two different surface

course thicknesses of AC, 100 mm (4 in) and 175 mm (7 in), and five different base types are used

in the experiment. The base types are: dense-graded aggregate base, asphalt-treated base, asphalttreated base over dense-graded aggregate base, permeable asphalt-treated base over dense-graded

aggregate base, and asphalt-treated base over permeable asphalt-treated base.

Profile data were available for six SPS-1 projects. Of these, one project has been profiled

three times, three projects had been profiled twice, and two'projects had been profiled only once.

As only a limited amount of profile data were available, a detailed analysis to quantify the changes

in IRI at the SPS-1 sites could not be carried out. The average IRI of the SPS-1 projects, which is

the average ofthe 12 test sections in the project, ranged from 0.74 mIkm (47 in/mi) for the SPS-1

project in Arizona to 1.13 mIkm (72 in/mi) for the SPS-1 project in Nebraska. The COY of the

IRI of the test sections within a project ranged from 8 percent for the project in Alabama to 19

percent for the project in Kansas. There were large differences between the right- and the leftwheel-path IRI for the SPS-1 projects in Alabama and Arizona, where the right-wheel-path IRI

was higher than the left-wheel-path IRI. The average difference between the IRI of the wheel
paths for the SPS-1 projects in Alabama and Arizona were 0.32 and 0.30 mIkm (20 and 19 in/mi),

respectively. This average difference for the other four projects ranged from 0.02 to 0.08 mIkm (1

to 5 in/mi).

The test sections in the SPS-I project in Arizona, which are located on a coarse-grained

subgrade in a dry-no freeze environment, showed little change in IRI when they were profiled

approximately 1 year after construction. The test sections in the Kansas project that have

estimated SNs less than four, showed a large increase in IRI when they were profiled

approximately 1 year after construction. The test sections in Kansas are located on a fine-grained

subgrade and are subjected to an estimated 469 KESAL/yr. Apart from the SPS-1 project in

Arizona, all other SPS-I projects generally indicated that the sections that had low SNs had·a

higher rate of increase ofIRI when compared with the sections that had high SNs.

The SPS-2 experiment was developed to investiga~e the effect of selected structural

factors on the long-term performance of rigid pavements constructed on different subgrade types

in different environmental regions. All rigid pavement test sections are doweled, jointed PCC

with ajoint spacing of 4.6 m (15 ft). The subgrade types considered in this experiment are

classified as fine grained and coarse grained. The environmental regions considered in this

experiment correspond to the four LTPP environmental regions: wet freeze, wet-no freeze, dryfreeze and dry-no freeze. The SPS-2 experiment consists of 24 tests sections. These 24 sections

are to be constructed for each combination of subgrade type and environmental region, with half

of the test sections (sections 1 through 12) being constructed at one site, and the other half

(sections 13 through 24) being constructed at a different site. The structural factors considered in

this experiment are: thickness ofPCC layer, flexural strength ofthe PCC, base type, drainability,

and lane width. The two PCC slab thicknesses considered in this experiment are 200 mm (8 in)

and 275 mm (11 in), while the two PCC flexural strengths that are used are 3.8 MPa (550 psi)

and 6.2 MPa (900 psi). The three base types used are: lean concrete base of 150-mm (6-in)

thickness, dense-graded aggregate base of 150-mm (6-in) thickness, and a 100-mm- (4-in-) thick

permeable asphalt-treated base over a 100-mm (4-in) thick dense-graded aggregate base.

Underdrains are provided in the test sections that have a permeable asphalt-treated base. The

lane widths considered in this experiment are 12 ft (3.66 m) and 14 ft (4.27 m).

Profile data were available for seven SPS-2 projects. Of these, one project has been

profiled four times, two projects had been profiled three times, two projects have been profiled
twice, and two projects have been profiled once. Therefore, a detailed study to quantify

roughness development at SPS-2 sections could not be performed because of the limited amount

of data that were available for analysis. The average IRI of the SPS-2 projects, which is the

average of the 12 test sections in the project, ranged from 1.12 mIkm (71 in/mi) to 1.47 mIkm (93

in/mi). The COY of the 00 of the test sections within a project ranged from 11 percent to 24

percent. The left- and right-wheel-path IRI were generally close to each other for most test

sections, with this difference typically being less than 0.16 mIkm (10 in/mi). The 00 after

construction at all test sections in the Washington SPS-2 project that had a PCC flexural strength

of 6.2 MPa (900 psi) had an IRI that was less than the IRI at the test sections that had PCC

flexural strength of3.8 MPa (550 psi). A reversal of this trend was observed at the Michigan

SPS-2, where all test sections that had PCC flexural strength of 6.2 MPa (900 psi) had an 00 that

was greater than the 00 at the test sections with a PCC flexural strength of3.8 MPa (550 psi).

These observations indicate that the PCC mix properties affect the 00 after construction ofPCC

sections. There were large differences in the IRI values between the annual profiling dates at

several sections. The analysis of data from the Arizona SPS-2 project indicated that the 00

values obtained approximately 1 year after construction were much less than the 00 values

obtained after construction at some sections. The maximum difference that was observed at a

test section in this project was 0.49 mIkm (31 in/mi). Variability in 00 between profile dates

was also noted at some sections in the Michigan SPS-2 project, and a filtered profile analysis at

such a section indicated that the differences in 00 were caused by curling at the joints. Because

of the short joint spacing of the slabs, joint curling can have a large effect on the 00. Joint

movements in the PCC slabs are likely to be influenced by PCC mix properties, temperature, and

support conditions below the slab. Because of the confounding effect on 00 caused by these

factors, an accurate estimate of the development of roughness at SPS-2 sections cannot be

estimated from the limited profile data that were available.

In the SPS-5 and SPS-6 experiments, differ~nt rehabilitation techniques are used in the

rehabilitation of existing AC and PCC pavements, respectively. The reduction in roughness

resulting from different rehabilitation techniques for SPS-5 and SPS-6 experiments, as well as

the development of roughness at the test sections, was studied.
The Specific Pavement Studies SPS-5 experiment was developed to investigate the

performance of selected AC rehabilitation treatment factors. The rehabilitation treatment factors

include overlay mix type (recycled and virgin), overlay thickness, and surface preparation of the

existing AC surface prior to overlay (minimal and intensive preparation). Nine test sections are

included in each SPS-5 project, with eight sections being experimental sections and one section

being a control section. Table 9.5.1 presents the treatments that are applied to SPS-5 sections.

eClon

Number

Each SPS-5 project is profiled prior to rehabilitation and then immediately after

rehabilitation. Thereafter, they are profiled annually. Profile data were available for 11 SPS-5

projects, and the number oftimes these sections have been profiled after rehabilitation ranged

from one to six. The profile data obtained prior to pavement rehabilitation indicated that the IRI

values of the test sections within a SPS-5 project were variable, with the COY of the IRI between

the test sections for the projects ranging from 10 to 33 percent. The difference between the

maximum and minimum IRI values oftest sections within an SPS-5 project prior to

rehabilitation ranged from 0.33 m/km (33 inlmi) for the project in Georgia to 2.32 m/km (147

inlmi) for the project in California.

The IRI values after pavement rehabilitation indicated that, for a specific SPS-5 project,

the IRI after overlay ofthe different sections fell within a relatively narrow band irrespective of

the IRI before the overlay of the sections. However, the limits of this band varied from project to

project. For example, the IRI values of the overlaid test sections (sections 2 through 9) for the
project in Minnesota before the overlay ranged from 2.07 to 2.85 m/km (131 to 181 in/mi) with

the standard deviation of the 00 between the sections being 0.32 m/km (20 in/mi) The 00 of the

test sections after the overlay ranged from 0.76 to 1.12 m/km (48 to 71 in/mi), with the standard

deviation in 00 between the sections being 0.17 m/km (11 in/mi). For the project in California,

the 00 before the overlay of the test sections (2 through 9) ranged from 1.56 to 3.13 m/km (99 to

199 in/mi) with the standard deviation of the test sections being 0.49 m/km (31 in/mi). The 00 of

the test sections after the overlay ranged from 0.70 to 1.07 m/km (45 to 68 in/mi) with the

standard deviation of 00 between the test sections being 0.16 m/km (l0 in/mi). For each SPS-5

project, the average 00 after overlay of the sections that received minimal and intensive surface

preparation were very close to each other.

Of the 11 projects for which profile data were available, four projects were more than 3.5

years old, with the other projects being less than 3.5 years old. Most of the SPS-5 test sections

have shown little change in 00 after pavement rehabilitation. A detailed analysis of the rate of

increase of 00 carried out on four projects that were more than 3.5 years old did not indicate any

common trends in the increase of 00.

The SPS-6 experiment was developed to investigate the effect of different rehabilitation

tecluiiques performed on jointed concrete pavements. The rehabilitation treatments applied to the

test sections in the SPS-6 test sections are presented in table 9.5.2. The SPS-6 sections are

profiled before and immediately after pavement rehabilitation, and thereafter they are profiled

annually.

Section Surface Preparation AC Overlay

Number Thickness (in)

1 Routine Maintenance 0

2 MinimumRestoration 0

3 MinimumRestoration 4

4 MinimumRestoration (saw and seal joints) 4

5 Intensive Restoration 0

6 Intensive Restoration 4

7 Crack/Break Seat 4

8 Crack/Break Seat 8

Note 1: In section 4, after the placement of the AC overlay. the AC surface

is sawed and ·sealed over the joints and workingcracks of the PCC.

Note 2: 1 in = 25.4 mm.
The IRI prior to rehabilitation of the test sections within a SPS-6 project was variable,

and the COY ofthe IRI between the test sections for the projects ranged from 10 to 24 percent.

Profile data were available for nine SPS-6 projects, and the number of times these sections have

been profiled after rehabilitation ranged from two to five. In general, for a specific project, the

IRI values for all test sections that received an AC surface (overlaid and crack and seated

sections) was within a relatively narrow band. The lower and upper limits of this band varied

from project to project. For example, the range ofIRI for these five sections (sections 3, 4, 6, 7,

and 8) for the project in California prior to pavement rehabilitation was between 2.38 and 3.23

m/km (151 and 205 in/mi) with the standard deviation in IRI between the test ,sections being 0.34

m/km (22 in/mi). The IRI after the placement of the AC surface at the test sections ranged from

0.85 to 1.05 m/km (54 to 67 in/mi) with the standard deviation ofIRI between the test sections

being 0.08 m/km (5 in/mi). For the project in Pennsylvania, the IRI of the sections receiving a

AC surface ranged from 1.97 to 2.93 m/km (125 to 186 in/mi) with the IRI after the overlay for

these sections ranging from 1 to 1.13 m/km (63 to 72 in/mi). The standard deviations ofIRI

between the test sections prior to the overlay and after the overlay were 0.44 m/km (28 in/mi)

and 0:06 m/km (4 in/mi), respectively.

Of the nine SPS-6 projects, four projects are between 3 and 4.5 years old, with the other

five projects being less than 3 years old. Most of the test sections in the SPS-6 projects are

showing little change in IRI after pavement rehabilitation, and overall trends across all sections

cannot be seen. The variability of profile measurements between the years may be masking the

small changes in IRI that occur at most of the sections. An analysis was performed to determine

the rate of development ofIRI at the four projects that were between 3 and 4.5 years old. Three

test sections in each project received an AC overlay of 100-mm (4-in) thickness. Prior to the

overlay, two sections received minimal surface preparation while one section received intensive

surface preparation. On one section that received minimal surface preparation, the AC overlay

was sawed and sealed over joints and working cracks. The rate of increase ofIRI of the saw and

seal section was lower than the rate of increase ofIRI of the other two sections that received the

AC overlay at three projects. Apart from this trend, other trends inIRI development were not

noted between the tests sections.
9.6. RECOMMENDATIONS FOR QUALITY ASSURANCE AND PROFILING

FREQUENCY

On the basis of analysis of the time-sequence IRI values at GPS sections,

recommendations for quality assurance during profiling were developed. The recommendations

included following a consistent wheel path between the years, and comparing the IRI values at

the section with the previous year's IRI value. If the change in IRI was greater than a specified

limit, it was recommended that the operator ensure that the differences were not caused by

equipment errors.

The trends in change of IRI observed at the test sections were used to develop guidelines

for profiling frequency at the GPS sections. The recommendations for profiling frequency were

developed on the basis of analysis of time-sequence IRI data.

The examination of time-sequence IRI data collected at GPS sections indicated that

variability in IRI values can occur between the test dates. For example, there were cases where

the IRI value for a specific test date would be higher than the IRI values prior to and subsequent

to this date. The reverse of this case, where the IRI value for a specific date was lower than the

IRI values prior to and subsequent to this date, was also noted. At some sections, the IRI values

were variable across all test dates. These variations in IRI values are attributed to the following

factors: variations in the profiled path, temperature or seasonal effects, and maintenance

activities at test sections.

A comparison between the IRI and RN computed from the algorithm developed by

Spangler and Kelly(l) indicated that a unique relationship between the IRI and RN does not exist.

The IRI js sensitive to wavelengths between 1.2 and 30 m (4 and 100 ft), while the RN is

sensitive to wavelengths between 0.48 and 2.4 m (1.6 and 8 ft). For a specific value ofRN, there

could be a range of IRI values that correspond to that RN. For example, consider two profiles

that have similar short wavelength contents, but different long wavelength contents. As the RN

is sensitive to the short wavelength content, the two profiles can have the same RN, but because

the long wavelength contents are different, they can have different IRI values. The study of

trends in IRI and RN at selected sections indicated that these two parameters generally have

similar trends. As the IRI has a maximum sensitivity at wavelengths of2.1 m (7 ft) and 15.2 m

(50 ft), short wavelength content also has a significant contribution to the 00. Therefore, short

wavelength contents that are between 0.48 and 2.4 m (1.6 and 8 ft) that reduce the RN will cause

an increase in the IRI.

The IRI data that were available in the database when this study commenced indicated

that, on average, four time-sequence IRI values obtained over a 4-year period were available for
a GPS section. For a majority of the sections, changes in IRI were minimal over this period.

The pavements in the LTPP program should see increase in roughness over time resulting from

traffic and environmental effects. However, a trend in roughness development cannot be

observed for most of the sections from data available for a such short period. In the short-term

data, variability caused by seasonal effects and profile measurements can mask the increase in

IRI that occurs at the section. Long-term monitoring data are needed to observe the true trend in

development of roughness.

In this study, an optimization technique was used to develop models to predict IRI of

GPS experiments 1 through 4. The models predict the initial IRI ofthe pavements with the use

of subgrade properties and structural properties of the pavement. The models reflect the behavior

pattern observed for the analyzed data set. For the analyzed GPS sections, the applicability of

the model can be evaluated when more time-sequence IRI data become available. The models

predict the initial 00 of the pavement with the use of subgrade properties and structural

properties of the pavement, and then predict a growth rate for the section that is a function of

time, traffic, sub grade properties, climatic properties, and pavement structure. In the modeling

process, the aim was to predict a model that would fit the time-sequence 00 data that were

available for a site. The developed models are sensitive to input parameters, and care should be

taken not to use input parameters that are unrealistic or beyond the range of data from which the

model was developed.

The GPS-5 sections showed a relatively constant 00 over time, and multiple linear

regression was used to develop a model to predict 00. However, this model has a very low

coefficient of determination (R2= 0.14). For the GPS experiments that include overlaid

pavements (GPS-6A, GPS-7 A, and GPS-9), a rate of development of 00 for each section was

obtained using linear regression analysis. This rate was then related to possible factors that could

affect the development of 00 that are available in the database. This effort was unsuccessful for

the GPS-7 A and GPS-9 experiments, and models could not be developed. For the GPS-6A

experiments, two models to predict the rate of increase of 00 were developed.

The analysis of 00 data indicated that flexible pavements generally start offwith lower

roughness but develop roughness more quickly and at lower traffic volumes than rigid

pavements. Climatic conditions and subgrade conditions showed a strong relationship with IRI

for GPS-l sections. Performance of GPS-I sections over fine-grained soils was strongly related
to Atterberg limits and the percentage of sub grade passing the 75-llm (No. 200) sieve (P200).

Generally higher roughness is found where sections are located in areas of higher precipitation.

In freezing environments, pavements in areas that have a high freeze index or a high number of

freeze-thaw cycles had higher IRI values. In the freezing environments, the data indicated that

adequate frost protection is an important factor for good performance. In hot climates, the IRI

value of the pavements was strongly related to days above 32°C (90°F). Sections in very hot

environments generally had higher IRI values, but better performance was indicated for sections

that are subjected to higher precipitation. This may be related to the cooling effect on AC from

precipitation. For the analyzed data set, traffic effects on IRI development were mostly seen for

sections that had lower SNs.

The IRI behavior trends observed for the GPS-2 data set were generally similar to that

observed for GPS-l sections. It was noted that some GPS-2 sections located in freeze regions

did not perform well. The use of a stabilized base results in a lower total pavement thickness.

This has a significant effect on the frost heave potential for pavements located in freezing zones.

Poor performance was also noted for GPS-2 sections that were located on high-moisture-content

clay subgrades. Analysis of GPS-2 sections that contain cement stabilized bases indicated that

the best environment for these types of bases from an IRI point of view is the wet-no freeze

environment.

The analysis of GPS-6B and GPS-7B pavements for which IRI before and after the

overlay were available indicated that a relatively thin overlay can reduce the IRI of a pavement

by a large amount. For example, a 100-mm- (2.5-in-) thick AC overlay reduced the IRI of a

flexible pavement from 3.15 to 0.63 m/km (200 to 40 in/mi). Similarly, a 84-mm- (3.3-in-) thick

overlay reduced the IRI of a PCC pavement from 2.68 to 0.87 m/km (170 to 55 in/mi).

Generally, for most cases, the IRI after the AC overlay of the pavement fell within a relatively

narrow band, irrespective of the IRI value of the pavement before the overlay. However,

sufficient time-sequence IRI data are not available for the GPS-6B and GPS-7B experiments to

see how the rate ofIRI development is affected by the IRI before the overlay. There are six

different combinations of original pavement type and overlay type for the GPS-9 experiment

(e.g., CRCP overlay of JRCP, JPCP overlay of JPCP, etc.). Because there were few GPS-9

sections (28 sections) and six combination of pavement types, specific trends in IRI

development could not be observed for this experiment.
The overall analysis of all rigid pavements in the LTPP database shows that JPC

pavements (GPS-3) have the highest IR!, while CRC pavements (GPS-5) have the lowest 00,

with the IRC pavements (GPS-4) falling within these two limits. Generally, for PCC pavements

higher PCC modulus was associated with higher 00, and a lower water to cement ratio was

associated with a higher 00. There are some indications that PCC pavements that have higher

strength (split tensile) while maintaining a relatively low modulus may provide better

performance. Sub grade and climatic factors were seen to have a strong influence on 00 for

GPS-3 and GPS-4 pavements. It was noted that the 00 of undoweled, jointed pavements is

closely related to the amount of faulting at a given site. For GPS-4 sections it was noted that

sections in higher precipitation areas had higher 00 values. Higher PCC thickness for GPS-4

pavements also indicated higher 00 values, and this factor is likely to be related to construction

of the pavement. The GPS-5 sections showed a nearly constant 00 over the monitored period.

This behavior was observed for young pavements (less than 5 years old) as well as for old

pavements (more than 20 years old). For CRC pavements, sections that had higher percentage of

longitudinal steel had lower 00 values.

Most ofthe SPS-1 and SPS-2 projects have been profiled fewer than three times, and

clear trends in 00 development cannot yet be established for the test sections in these projects.

At the SPS-1 projects in Arizona and Alabama, large differences were observed between the leftand the right-wheel-path 00, with the right-wheel-path 00 being higher. Differences as much as

0.6 m/km (37 in/mi) were noted at some sections. In SPS-1 projects, sections that had lower SNs

had a higher rate of increase of 00 when compared with sections that had higher SNs.

At several SPS-2 projects, large variations in 00 between profile dates occurred at some

test sections, with differences as much as 0.41 m/km (26 in/mi) being noted. These variations

are attributed to curling and warping effects of slabs. The variations in 00 were seen to be

related to PCC mix properties and support conditions below the PCC slab. Because of the

confounding effect of climatic factors on 00, estimates for development of 00 cannot be made

yet for the SPS-2 experiments. As more time-sequence IR! data are available, trends in 00

development will be seen more clearly.
The IRI values of the test sections prior to pavement rehabilitation in an SPS-5 as well as

an SPS-6 project were variable. The COY of the IRI prior to rehabilitation for a project ranged

from 10 to 33 percent for SPS-5 experiments and 10 to 24 percent for the SPS-6 experiments.

Most of the test sections in the SPS-5 and SPS-6 projects are currently showing little change in

IRI after pavement rehabilitation, and overall trends in the development of IRI across all sections

cannot be seen. The variability of profile measurements between the years may be masking the

small changes in IRI that occur at most of the sections.

For the SPS-5 projects the data indicated that, irrespective of the IRI before the overlay of

the sections, the IRI after overlay of the sections for a specific project would fall within a

relatively narrow band. However, the lower and upper limits of this range varied from project to

project. The average IRI after overlay of the sections that received minimum and intensive

surface preparation prior to overlay were close to each other for a specific project. Most of the

SPS-5 sections have shown little change in IRI after pavement rehabilitation, and common trends

in the increase of IRI for this experiment cannot be determined yet.

Three sections in the SPS-6 project received an AC overlay, while two sections received

an AC surface after being cracklbreak seated. In general, for a specific project, the IRI values

for all test sections that received an AC surface (overlaid and crack and seated sections) were

within a narrow band. The lower and upper limits of this band varied from project to project.

Four SPS-6 projects were between 3 and 4.5 years old, and the rate of increase ofIRI of the test

sections in these projects were computed. Clear trends in IRI development could not be seen

between the test sections except at the three test sections that received an AC overlay of 100-mm

(4-in) thickness. Prior to the overlay, two sections received minimal surface preparation while

one section received intensive surface preparation. On one section that received minimal surface

preparation, the AC overlay was sawed and sealed over joints and working cracks. ,The section

that was sawed and sealed had a lower rate of increase ofIRI than the other two sections for three

ofthe analyzed projects.

A statistical analysis was performed on the time-sequence IRI data to identify sections

that showed an increase in IRI over time and to identify sections that showed a variable IRI
behavior over time. On the basis of the information obtained from the statistical analysis,

recommendations for profiling frequency at GPS sections were developed.

1. At the time of this study, four time-sequence 00 values obtained over a 4-year period were

on average available for a GPS section. These limited data are not sufficient to characterize

the roughness growth at each section. The roughness of the pavement sections will increase

over time resulting from traffic and climatic effects. The roughness is also expected to vary

because of seasonal and environmental effects, and these effects can mask the true roughness

behavior of the section when looking at roughness data for a limited period. Both the GPS

and SPS sections should be profiled in the future to establish the roughness growth

characteristics of the sections. It is recommended that SPS-l and SPS-2 projects be profiled

twice a year to characterize the seasonal variability in 00.

2. The analysis of time-sequence 00 data available for the GPS sections indicated that

variability in IRI values was present at some GPS sections. For example, there were cases

where the 00 value for a specific test date would be higher than the IRI values prior to and

subsequent to this date. The reverse of this case, where the 00 value for a specific date was

lower than the 00 values prior to and subsequent to this date, was also noted. At some

sections the 00 values were variable across all test dates. We recommend that the cause of

these inconsistencies be analyzed, as it could provide valuable insight into variability of

roughness at sections. This analysis should consist of examining the filtered profile that

shows an inconsistency, and then comparing it with the profiles prior to and subsequent to

this date to identify the cause of the inconsistency.

3. The mix design parameters ofPCC appear to have a large effect on the performance ofPCC

pavements. Regions that are very hot have evolved low strength (split tensile) mixes that

generally result in low modulus PCC, while regions that are cool and wet but have little

freezing have evolved high strength (split tensile) mixes. The fact that such different mix

types have evolved in different climates indicates the importance of climate on performance.

There are indications that performance ofPCC pavements may be influenced by the relative

values of the split tensile strength and the modulus for a specific PCC mix. Further research

should be performed to investigate these observations.
4. Variations in IRI values were observed between the profile dates at several SPS-2 projects.

The changes in IRI that were observed at the SPS-2 projects were seen to be related to

temperature, PCC mix parameters, and the support conditions below the PCC slab. For

example, in the SPS-2 project in Arizona, the IRI value at some sections 1 year after

construction was lower than the IRI immediately after construction by as much as 0.41

mIkm (26 in/mi). Further research is recommended to investigate these observations.

APPENDIX A

GPS SECTIONS WITH INCONSISTENT IRI VALUES

REGION GPS STATE SECTION COMMENT

EXPERIMENT 10

North Atlantic 1 23 1001 Note 1

North Atlantic 1 23 1009' Note 1

North Atlantic 1 23 1012 Note 1

North Atlantic 1 23 1026 Note 2

North Atlantic 1 23 1028 Note 1

North Atlantic 1 33 1001 Note 1

North Atlantic 1 34 1003 Note 1

North Atlantic 1 34 1030 Note 2

North Atlantic 1 36 1011 Note 1

North Atlantic 1 37 1006 Note 2

North Atlantic 1 37 1024 Note 1

North Atlantic 1 37 1030 Note 1

North Atlantic 1 37 1817 Note 1

North Atlantic 1 42 1597 Note 1

North Atlantic 1 50 1002 Note 1

North Atlantic 1 50 1004 Note 1

North Atlantic 1 51 1002 Note 1

North Atlantic 1 89 1021 Note 1

North Atlantic 2 24 1632 Note 1

North Atlantic 2 34 1033 Note 1

North Atlantic 2 36 1644 Note 1

North Atlantic 2 37 2819 Note 1

North Atlantic 2 37 2824 Note 1

North Atlantic 2 50 1681 Note 1

North Atlantic 2 51 1417 Note 1

North Atlantic 2 84 1802 Note 2

North Atlantic 2 88 1646 Note 1

North Atlantic 2 89 2011 Note 1

North Atlantic 3 23 3013 Note 2

North Atlantic 3 23 3014 Note 1

North Atlantic 3 37 3011 Note 1

North Atlantic 3 37 3807 Note 1

North Atlantic 3 37 3816 Note 1

North Atlantic 3 42 3044 Note 1

North Atlantic 4 10 1201 Note 1

North Atlantic 4 34 4042 Note 1

North Atlantic 4 36 4017 Note 1

North Atlantic 4 54 4003 Note 1

North Atlantic 4 54 4004 Note 1

North Atlantic 5 9 5001 Note 1

North Atlantic 5 37 5827 Note 2

North Atlantic 5 42 1598 Note 2

North Atlantic 5 51 5008 Note 1

North Atlantic 6A 84 6804 Note 1

North Atlantic 68 36 1005 Note 1

North Atlantic 68 37 1803 Note 1
REGION GPS STATE SECTION COMMENT

EXPERIMENT 10

North Atlantic 68 51 1"23 Note 2

North Atlantic 7A 23 7023 Note 2

North Atlantic 7A 42 1610 Note 2

North Atlantic 7A 42 7037 Note 1

North Atlantic 7A 54 7008 Note 1

North Atlantic 7A 42 1613 Note 1

North Atlantic 9 42 1627 Note 1

North Atlantic 9 42 9027 Note 1

North Central 1 18 1028 Note 1

North Central 1 19 1044 Note 1

North Central 1 21 1010 Note 1

North Central 1 26 1001 Note 1

North Central 1 26 1010 Note 1

North Central 1 26 1013 Note 1

North Central 1 27 1016 Note 1

North Central 1 27 1023 Note 1

North Central 1 27 1029 Note 1

North Central 1 29 1005 Note 1

North Central 1 90 6405 Note 1

North Central 2 18 2008 Note 2

North Central 2 38 2001 Note 2

North Central 2 83 6454 Note 2

North Central 3 18 3003 Note 1

North Central 3 18 3030 Note 2

North Central 3 20 3013 Note 2

North Central 3 20 3060 Note 1

North Central 3 26 3068 Note 1

North Central 3 27 3003 Note 1

North Central 3 31 3033 Note 1

North Central 3 38 3005 Note 1

North Central 3 38 3006 Note 1

North Central 3 39 3801 Note 1

North Central 3 46 3009 Note 1

North Central 3 46 3010 Note 1

North Central 3 46 3013 Note 1

North Central 3 46 3052 Note 1

North Central 3 55 3014 Note 1

North Central 3 55 6351 Note 2

North Central 3 55 6355 Note 1

North Central 4 17 4082 Note 1

North Central 4 18 4021 Note 1

North Central 4 18 4042 Note 1

North Central 4 20 4052 Note 1

North Central 4 27 4033 Note 2

North Central 4 27 4037 Note 2

North Central 4 27 4040 Note 1

North Central 4 27 4050 Note 1

North Central 4 27 4055 Note 2

North Central 4 27 4082 Note 1
REGION GPS STATE SECTION COMMENT

EXPERIMENT 10

North Central 4 29 4036 Note 2

North Central 4 29 5503 Note 1

North Central 5 17 5843 Note 1

North Central 5 17 5849 Note 1

North Central 5 18 5043 Note 1

North Central 5 18 5518 Note 2

North Central 5 29 5047 Note 1

North Central 5 46 5020 Note 2

North Central 6A 18 6012 Note 1

North Central 6A 19 6049 Note 2

North Central 6A 26 6016 Note 1

North Central 6A 39 6019 Note 1

North Central 6A 90 6400 Note 1

North Central 68 29 5403 Note 2

North Central 68 29 5413 Note 2

North Central 68 83 6451 Note 2

North Central 68 90 6410 Note 1

North Central 68 90 6412 Note 1

North Central 7A 20 7085 Note 2

North Central 7A 27 7090 Note 2

North Central 7A 31 7017 Note 2

North Central 7A 39 7021 Note 1

North Central 78 29 4069 Note 1

North Central 78 29 5493 Note 2

North Central 78 31 6702 Note 1

North Central 78 39 5010 Note 1

North Central 9 18 9020 Note 2

North Central 9 26 9029 Note 1

North Central 9 39 9022 Note 1

Southern 1 1 1001 Note 1

Southern 1 1 4129 Note 2

Southern 1 12 1370 Note 2

Southern 1 12 4154 Note 1

Southern 1 47 3075 Note 2

Southern 1 48 1047 Note 1

Southern 1 48 1068 Note 1

Southern 1 48 1076 Note 1

Southern 1 48 1077 Note 1

Southern 1 48 1096 Note 2

Southern 1 48 1169 Note 2

Southern 1 48 1181 Note 1

Southern 1 48 3739 Note 1

Southern 2 13 4112 Note 1

Southern 2 13 4113 Note 2

Southern 2 28 3085 Note 2

Southern 2 28 3087 Note 2

Southern 2 35 2006 Note 1

Southern 2 40 4088 Note 1

Southern 2 40 4165 Note 1
REGION GPS STATE SECTION COMMENT

EXPERIMENT 10

Southern 2 47 1023 Note 2

Southern 2 47 1029 Note 2

Southern 2 47 2001 Note 1

Southern 2 47 3101 Note 1

Southern 2 48 2172 Note 1

Southern 2 48 3559 Note 1

Southern 3 1 3028 Note 2

Southern 3 12 4000 Note 1

Southern 3 12 4138 Note 2

Southern 3 48 3003 Note 2

Southern 4 1 4007 Note 2

Southern 4 5 4023 Note 2

Southern 4 48 4146 Note 2

Southern 4 48 4152 Note 1

Southern 5 1 3998 Note 1

Southern 5 28 5805 Note 1

Southern 5 48 5035 Note 1

Southern 5 48 5284 Note 1

Southern 5 48 5310 Note 1

Southern 6A 1 6019 Note 1

Southern 6A 48 6079 Note 1

Southern 7A 28 3097 Note 1

Southern 9 28 7012 Note 1

Southern 9 28 9030 Note 1

Western 1 4 1002 Note 1

Western 1 4 1003 Note 1

Western 1 4 1007 Note 2

Western 1 4 1015 Note 1

Western 1 4 1024 Note 1

Western 1 4 1025 Note 2

Western 1 4 1034 Note 1

Western 1 6 7454 Note 1

Western 1 1 8156 Note 1

Western 1 8 1029 Note 2

Western 1 6 8535 Note 1

Western 1 8 7780 Note 1

Western 1 16 1005 Note 1

Western 1 16 1009 Note 1

Western 1 16 9032 Note 2

Western 1 49 1008 Note 1

Western 1 53 1007 Note 1

Western 1 56 7775 Note 1

Western 2 4 1065 Note 1

Western 2 6 2002 Note 1

Western 2 6 2040 Note 1

Western 2 6 2053 Note 1

Western 2 6 2647 Note 1

Western 2 6 7452 Note 1

Western 2 6 8149 Note 1
REGION GPS STATE SECTION COMMENT

EXPERIMENT 10

Western 2 6 8151 Note 1

Western 2 6 8202 Note 2

Western 2 8 2008 Note 1

Western 2 32 2027 Note 1

Western 2 56 2015 Note 2

Western 3 6 3013 Note 1

Western 3 6 3017 Note 2

Western 3 6 3019 Note 1

Western 3 6 3024 Note 1

Western 3 6 3042 Note 1

Western 3 6 7456 Note 2

Western 3 32 3010 Note 1

Western 3 49 3011 Note 1

Western 3 49 7082 Note 1

Western 3 53 3013 Note 1

Western 3 53 3014 Note 1

Western 3 53 3019 Note 1

Western 3 53 7409 Note 2

Western 5 6 7455 Note 1

Western 6A 6 6044 Note 1

Western 6A 8 6002 Note 2

Western 6A 16 6027 Note 1

Western 6A 30 6004 Note 1

Western 6A 41 6012 Note 2

Western 6A 53 6048 Note 1

Western 6A 56 6029 Note 1

Western 7A 8 7035 Note 2

Western 7A 8 7036 Note 1

Western 7A 41 7018 Note 1

Western 7A 41 7019 Note 1

Western 7A 41 7025 Note 1

Western 9 6 9048 Note 2

Western 9 6 9049 Note 1

Note 2: The time-sequence data for both-wheel-path IRI do not show a

clear inconsistency, but the IRI of a wheel path shows inconsistencies.

APPENDIXB

OUTPUT FROM LINEAR REGRESSION ANALYSIS

State GPS GPS Number Both-Wheel-Path IRI (inImi) COy Linear Re ~sion Results Linear COy Slope

ID Expt Profile Profile SeQuence of Intercept Slope R2 Relationship > 6% ? >3

Runs 1st 2nd 3rd 4th 5th 6th 7th IRI (%) Simificant?

1 1001 1 6 54 62 59 58 57 40 14.1 96.0 -3.46 0.26 Yes

1 1019 1 4 87 90 89 96 4.1 79.8 1.93 0.89 Positive

1 4125 1 4 60 61 59 70 7.9 22.6 2.01 0.60 Yes

1 4126 1 3 52 53 . 53 1.6 50.7 0.47 0.83

1 4129 1 3 64 64 68 3.4 61.4 1.28 0.85

1 4155 1 4 60 60 61 68 6.4 32.9 1.87 0.79 Yes

4 1001 1 4 68 68 68 70 1.5 61.3 0.52 0.46

4 1002 1 3 99 191 201 34.1 -283.4 37.99 0.95 Yes Yes

4 1006 1 3 49 59 68 16.8 -29.5 6.70 0.99 Positive Yes Yes

4 1007 1 3 85 116 115 16.5 -37.5 10.83 0.84 Yes Yes

4 1015 1 3 71 62 69 7.1 79.5 -0.96 0.09 Yes

4 1016 1 3 51 56 60 7.7 20.5 2.93 1.00 Positive Yes

4 1018 1 3 62 65 68 4.6 35.2 1.96 0.99 Positive

4 1021 1 3 80 72 80 5.7 83.6 -0.50 0.03

4 1022 1 3 42 42 47 6.8 23.6 1.63 0.66 Yes

4 1024 1 3 64 59 75 11.9 33.2 2.65 0.24 Yes

4 1025 1 3 70 54 71 14.4 77.1 -0.92 0.02 Yes

4 1034 1 5 73 67 69 83 76 8.6 35.6 2.29 0.31 Yes

4 1036 1 4 45 49 109 122 49.0 -108.2 24.14 0.97 Positive Yes Yes

4 1037 1 3 112 126 143 12.1 61.8 10.22 0.95 Yes Yes

6 1253 1 7 110 110 109 111 112 113 117 2.2 82.5 1.36 0.75 Positive

6 7454 1 5 110 109 106 117 123 5.8 54.5 2.96 0.61

6 8153 1 4 86 79 96 98 10.0 16.1 5.23 0.61 Yes Yes

6 8156 1 4 109 98 108 112 5.8 77.0 1.74 0.15

8 1029 1 4 144 146 151 155 3.5 81.9 3.39 0.94 Positive Yes

8 1047 1 4 104 115 76 86 18.3 157.4 -7.36 0.42 Yes

8 1053 1 7 76 74 77 78 77 78 79 2.2 70.6 0.76 0.51 Positive

8 1057 1 4 70 71 73 77 4.1 62.5 1.77 0.82 Positive

8 7780 1 3 104 86 79 14.6 267.8 -9.90 0.99 Nee:ative Yes

9 1803 1 6 95 104 101 96 98 99 3.3 98.7 -0.01 0.00
State GPS GPS Number Both-Wheel-Path IRI (inlmil COY Linear Re ression Results Linear COY Slope

ID Expt Profile ProfIle SCQuence of Intercept Slope R2 Relationship > 6% '1 >3

Runs 1st 2nd 3rd 4th 5th 6th 7th IRI (%l Sie:nificant?

12 1030 1 3 70 74 69 3.6 n.l -0.29 0.05

12 1060 1 3 41 42 40 3.4 46.6 -0.43 0.36

12 1370 1 3 85 87 78 5.9 119.9 -1.92 0.58 .

12 3995 1 3 64 64 61 2.9 n.4 -0.88 0.89

12 3996 1 3 68 71 68 2.3 67.5 0.09 0.01

12 3997 1 4 70 72 70 80 6.6 27.5 2.56 0.74 Yes

12 4103 1 3 41 41 39 2.7 45.8 -0.51 0.83

12 4106 1 3 38 39 39 1.8 37.3 0.34 0.90

12 4107 1 3 73 76 73 2.5 73.8 0.04 0.00

12 4153 1 3 138 153 159 7.3 124.5 4.97 0.92 Yes Yes

12 4154 1 4 101 118 127 120 9.6 28.1 4.02 0.43 Yes Yes

12 9054 1 4 61 63 62 66 3.6 42.1 1.19 0.89 Positive

13 1001 1 3 51 52 54 2.7 47.9 0.73 0.99 Positive

13 1005 1 6 60 62 63 62 66 64 3.0 56.3 1.04 0.47

13 1031 1 6 49 55 55 53 56 63 8.0 16.2 3.48 0.88 Positive Yes Yes

16 1001 1 5 59 59 60 62 60 1.9 49.0 0.61 0.56

16 1005 1 7 93 85 81 112 114 126 111 16.4 -18.0 7.30 0.48 Positive Yes Yes

16 1007 1 7 73 69 73 78 80 84 83 7.4 21.7 2.75 0.87 Positive Yes

16 1010 1 7 83 85 89 96 94 93 95 5.8 28.1 2.76 0.87 Positive

16 1020 1 6 44 44 45 46 44 44 2.1 44.6 0.01 0.00

16 1021 1 5 81 78 80 81 79 1.6 80.6 -0.14 0.03

16 9032 1 6 112 99 108 115 116 120 6.6 99.1 2.84 0.45 Yes

16 9034 1 6 99 97 100 105 106 106 4.1 95.1 2.11 0.79 Positive

17 1002 1 5 78 73 77 74 86 6.5 65.6 2.28 0.45 Yes

17 1003 1 6 60 63 59 60 62 59 3.0 62.0 -0.35 0.08

18 1028 1 5 71 85 83 78 87 8.0 39.2 2.51 0.40 Yes

18 1037 1 4 119 126 127 131 4.0 101.2 2.74 0.93 Positive

19 1044 1 5 106 125 110 117 118 6.5 84.9 1.47 0.11 Yes

20 1005 1 4 180 189 181 184 2.2 180.0 0.19 0.00

20 1009 1 5 120 124 127 124 125 1.9 117.2 0.94 0.40

20 1010 1 4 83 88 84 85 2.7 87.9 -0.28 0.03

21 1010 1 6 78 69 69 71 72 74 4.9 70.9 0.21 O.oI

21 1014 1 5 91 100 103 103 128 13.0 57.6 7.19 0.80 Positive Yes Yes
State GPS GPS Number Both-Wheel-Path IRI (inImi) COY Linear Re ression Results Linear COY Slope

ID Expt Profile Profile Sequence of Intercept Slope R2 Relationship > 6%? >3

Runs 1st 2nd 3rd 4th 5th 6th 7th IRI (%) Siltnificant?

21 1034 1 5 69 72 65 69 71 3.9 66.1 0.17 0.01

23 1001 1 6 118 139 116 110 153 125 12.7 85.3 2.14 0.06 Yes

23 1009 1 7 61 67 61 62 60 49 51 11.4 121.8 -2.90 0.57 NelZative Yes

23 1012 1 6 46 56 48 45 52 51 8.4 47.5 0.36 0.03 Yes

23 1026 1 6 90 95 86 86 92 88 4.1 100.0 -0.57 0.08

23 1028 1 7 103 108 98 95 98 95 85 7.3 159.2 -3.14 0.67 Neltative Yes

25 1002 1 6 74 76 75 72 76 80 3.8 67.3 0.85 0.29

25 1004 1 6 68 71 66 69 64 69 3.5 74.9 -0.40 0.10

26 1001 1 4 48 48 47 47 1.6 57.5 -0.49 0.91 NelZative

26 1004 1 5 86 93 90 87 82 4.8 95.8 -1.37 0.22

26 1010 1 5 75 76 86 81 91 8.6 23.3 3.61 0.72 Positive Yes Yes

26 1012 1 5 60 66 69 72 92 16.7 -13.9 7.52 1.00 Positive Yes Yes

26 1013 1 6 77 80 75 95 87 103 12.9 23.0 5.92 0.69 Positive Yes Yes

27 1016 1 5 126 121 115 140 140 8.6 77.5 3.14 0.36 Yes Yes

27 1018 1 3 159 172 180 6.2 96.6 5.67 0.86 Yes Yes

27 1019 1 3 100 96 94 2.9 114.0 -1.44 0.82

27 1023 1 6 101 112 112 122 126 142 12.0 47.4 7.05 0.88 Positive Yes Yes

27 1028 1 5 144 129 143 146 142 4.7 111.3 1.51 0.15

27 1029 1 4 107 112 118 122 5.8 13.4 4.58 0.99 Positive Yes

27 1085 1 4 175 183 181 196 4.9 138.7 5.53 0.78 Yes

27 1087 1 4 88 101 102 116 11.2 1.3 7.63 0.94 Positive Yes Yes

27 6251 1 5 77 91 105 108 120 16.4 -1.5 9.57 0.97 Positive Yes Yes

29 1002 1 5 79 85 76 82 87 5.6 72.2 1.99 0.31

29 1005 1 4 50 50 51 46 4.1 59.9 -0.58 0.24

29 1008 1 5 101 102 104 101 103 1.3 99.9 0.34 0.17

29 1010 1 5 78 81 89 97 111 14.5 0.7 7.85 0.96 Positive Yes Yes

30 1001 1 5 65 65 65 68 68 2.7 57.8 0.93 0.69 Positive

30 7066 1 3 48 49 61 13.7 -14.3 7.90 0.89 Yes Yes

30 7088 1 3 56 65 69 10.7 -6.5 7.68 0.93 Yes Yes

30 8129 1 7 53 47 55 64 66 62 65 12.2 44.8 3.53 0.71 Positive Yes Yes

31 1030 1 6 69 71 68 87 101 107 20.7 -23.8 11.69 0.85 Positive Yes Yes

32 1020 1 5 44 46 59 60 69 18.7 15.0 5.24 0.96 Positive Yes Yes

32 1021 1 5 97 99 99 100 126 11.7 34.0 7.53 0.60 Yes Yes
State GPS GPS Number Both-Wheel-PathIRI (inImi) COy Linear Re rression Resuhs Linear COy Slope

10 Expt Proflle Proflle Sequence of Intercept Slope R2 Relationship > 6~? >3

Runs 1st 2nd 3rd 4th 5th 6th 7th IRI (~) Silmificant?

33 1001 1 6 37 47 39 39 41 46 10.2 31.7 0.93 0.16 Yes

34 1003 1 5 127 117 103 96 104 11.3 238.5 -7.50 0.72 Ne2ative Yes

34 1011 1 6 102 103 109 103 109 116 5.1 53.2 2.66 0.69 Positive

34 1030 1 6 225 253 302 285 304 364 16.5 -301.2 27.14 0.88 Positive Yes Yes

34 1031 1 6 111 115 122 115 127 155 13.1 -16.8 7.68 0.67 Positive Yes Yes

35 1003 1 3 56 62 66 8.6 33.4 3.12 0.87 Yes Yes

35 1005 1 3 34 39 42 11.4 17.8 2.65 0.92 Yes

35 1022 1 3 43 47 48 5.8 38.0 1.51 0.84

35 1112 1 7 49 48 50 52 54 54 53 5.3 39.3 1.35 0.90 Positive

36 1011 1 7 83 69 74 71 73 69 75 6.7 88.0 -2.07 0.23 Yes

37 1006 1 7 44 49 49 47 46 47 66 14.9 27.0 2.28 0.34 Yes

37 1024 1 7 61 78 75 65 85 88 88 13.9 15.7 5.30 0.64 Positive Yes Yes

37 1028 1 7 61 53 54 56 59 58 58 5.4 53.1 0.41 0.06

37 1030 1 7 66 69 71 70 63 67 70 3.9 67.8 0.04 0.00

37 1040 1 6 76 80 79 79 83 83 3.3 61.8 1.32 0.80 Positive

37 1352 1 7 75 74 73 72 70 71 71 2.4 81.7 -0.81 0.59 Neltative

37 1801 1 6 66 66 68 61 69 63 4.5 71.3 -0.31 0.04

37 1802 1 7 52 55 57 55 67 72 89 20.7 17.5 6.93 0.83 Positive Yes Yes

37 1814 1 6 54 48 49 51 57 56 6.9 27.0 1.17 0.33 Yes

37 1817 1 7 65 74 57 56 57 62 64 10.3 68.5 -0.79 0.03 Yes

37 1992 1 5 72 73 71 77 82 5.9 69.5 2.20 0.67 Positive

42 1597 1 7 108 126 110 114 125 129 146 11.0 52.2 6.23 0.67 Positive Yes . Yes

42 1599 1 6 87 89 89 92 94 103 6.2 79.5 2.73 0.84 Positive Yes

42 1605 1 7 123 122 119 121 123 123 134 3.9 85.8 1.87 0.47 Positive

45 1011 1 3 86 98 96 7.2 74.4 2.72 0.55 Yes

45 1024 1 6 88 96 90 88 87 90 3.4 87.7 0.31 0.01

47 3075 1 4 117 118 113 130 6.0 59.4 2.89 0.47 Yes

47 3104 1 3 133 153 156 8.4 111.8 6.03 0.90 Yes Yes

48 1 1 3 43 45 46 3.5 42.0 0.90 0.97

48 1039 1 3 66 86 145 41.6 -139.4 25.44 0.92 Yes Yes

48 1047 1 3 132 133 137 2.1 90.7 2.11 0.93

48 1050 1 4 77 86 88 110 15.7 44.0 6.81 0.93 Positive Yes Yes

48 1056 1 3 82 89 131 26.4 -314.9 19.48 1.00 Positive Yes Yes
State GPS GPS Number Both-Wheel-Path IRI (inimil COy Linear Re rression Results Linear COY Slope

ID Expt Profile Profile Scauence of Intercept Slope R2 Relationship > 6"? >3

Runs 1st 2nd 3rd 4th 5th 6th 7th IRI (,,) Sitmificant?

48 1060 1 7 88 80 84 86 87 86 89 3.3 81.5 0.57 0.13

48 1065 1 4 132 135 154 168 11.6 -48.0 8.79 1.00 Positive Yes Yes

48 1068 1 7 70 72 77 78 89 77 76 8.1 62.4 2.46 0.43 Yes

48 1076 1 4 117 117 121 105 5.9 148.0 -2.24 0.41

48 1077 1 7 76 79 79 68 76 80 84 6.2 71.2 0.55 0.03 Yes

48 1094 1 4 55 58 56 61 4.4 42.1 0.98 0.56

48 1096 1 4 135 146 146 147 4.0 114.4 2.61 0.72

48 1111 1 4 98 102 107 132 14.0 -37.9 7.41 0.84 Positive Yes Yes

48 1122 1 7 69 67 64 70 64 70 72 4.4 61.1 0.37 0.04

48 1123 1 4 48 51 58 50 8.6 36.2 0.99 0.10 Yes

48 1168 1 3 65 71 75 7.2 52.3 3.03 0.89 Yes Yes

48 1169 1 4 84 85 89 111 14.0 -20.4 5.67 0.80 Yes Yes

48 1174 1 4 75 75 84 90 9.1 1.7 4.80 0.87 Positive Yes Yes

48 1178 1 3 112 156 198 27.9 84.7 27.48 0.99 Positive Yes Yes

48 1181 1 5 107 105 126 120 148 14.6 18.1 8.51 0.79 Positive Yes Yes

48 1183 1 4 145 153 152 205 17.1 -85.9 14.54 0.73 Yes Yes

48 3579 1 4 83 87 89 87 3.1 83.0 0.82 0.34

48 3729 1 4 97 98 103 106 4.1 77.8 2.76 0.93 Positive

48 3739 1 7 127 138 141 161 160 163 149 9.2 69.2 7.51 0.82 Positive Yes Yes

48 3739 1 4 154 173 161 160 4.9 155.3 0.52 0.00

48 3749 1 5 79 78 . 83 102 117 18.7 -7.5 8.79 0.91 Positive Yes Yes

48 3769 1 4 91 101 109 128 14.7 -24.1 8.22 0.96 Positive Yes Yes

48 3835 1 3 95 110 116 10.0 91.9 9.12 0.95 Yes Yes

48 3855 1 3 83 81 86 3.0 75.4 0.68 0.19

48 3865 1 3 75 79 88 8.0 -22.6 4.65 0.99 Positive Yes Yes

48 9005 1 4 76 90 94 114 16.8 50.0 7.69 0.94 Positive Yes Yes

49 1001 1 7 63 59 66 68 69 70 71 6.2 42.9 2.05 0.88 Positive Yes

49 1008 1 5 98 82 90 96 96 7.2 77.3 0.99 0.07 Yes

49 1017 1 5 87 85 87 96 94 5.3 30.5 2.36 0.71 Positive

50 1002 1 6 78 77 68 71 83 83 8.1 66.1 1.42 0.18 Yes

50 1004 1 6 105 107 93 104 131 132 14.3 62.9 6.65 0.59 Positive Yes Yes

51 1002 1 - 6 176 160 149 146 152 154 6.9 199.1 -3.42 0.33 Yes

51 1023 1 7 96 97 98 107 100 113 113 7.2 57.5 4.13 0.73 Positive Yes Yes
State GPS GPS Number Both-Wheel-Path IRI (inImi) COY Linear Re ression Results Linear COY Slope

ID Expt ProfUe ProfUe Seauence of Intercept Slope R2 Relationship > 6%? >3

Runs 1st 2nd 3rd 4th 5th 6th 7th IRI (%) Silmificant?

51 1464 1 7 75 78 74 80 77 75 74 3.2 82.5 -0.51 0.15

53 1002 1 5 103 97 104 105 105 3.3 93.2 1.31 0.29

53 1006 1 7 54 53 55 56 58 63 64 7.7 35.6 2.62 0.87 Positive Yes

53 1007 1 6 69 63 107 99 97 97 20.1 31.9 6.74 0.44 Yes Yes

53 1008 1 5 65 72 83 87 99 16.2 -41.1 9.58 0.97 Positive Yes Yes

53 1501 1 5 56 63 68 73 80 13.3 8.2 6.57 1.00 Positive Yes Yes

53 1801 1 4 59 59 62 71 9.0 3.8 3.30 0.75 Yes Yes

56 1007 1 7 56 54 54 57 58 58 59 3.2 47.4 0.77 0.56 Positive

56 7775 1 6 64 42 52 63 63 80 20.8 43.2 3.80 0.33 Yes Yes

81 1803 1 5 79 79 82 81 80 1.8 75.6 0.56 0.44

81 1804 1 4 157 183 207 214 13.7 -33.2 24.62 1.00 Positive Yes Yes

81 1805 1 5 149 158 177 179 176 8.1 81.2 7.14 0.83 Positive Yes Yes

82 1005 1 4 41 42 45 47 6.4 32.4 2.09 0.94 Positive Yes

83 1801 1 5 89 90 92 90 96 2.9 81.3 1.29 0.68 Positive

84 1684 1 6 92 91 89 98 95 93 3.3 80.7 0.90 0.29

85 1801 1 6 112 128 136 136 156 160 13.0 68.4 9.28 0.93 Positive Yes Yes

85 1803 1 6 77 78 79 78 80 85 3.5 65.5 1.21 0.65 Positive

85 1808 1 6 54 61 60 57 55 57 4.5 58.9 -0.20 0.02

87 1620 1 5 98 110 107 108 106 4.4 82.5 2.44 0.25

87 1622 1 7 88 91 94 97 96 102 104 6.0 48.3 3.09 0.91 Positive Yes

87 1806 1 6 56 55 53 57 61 60 5.3 48.8 1.26 0.59 Positive

88 1645 1 6 55 58 61 62 63 60 4.5 54.8 1.07 0.54 Positive

89 1021 1 7 90 91 94 125 99 111 116 13.2 47.0 7.06 0.46 Yes Yes

89 1125 1 6 129 137 162 180 213 218 21.7 -95.7 20.12 0.98 Positive Yes Yes

89 1127 1 7 163 168 168 177 178 188 237 13.9 24.2 12.19 0.71 Positive Yes Yes

90 6405 1 6 72 86 72 92 104 89 14.5 -5.7 4.03 0.38 Yes Yes

1 1011 2 3 57 53 50 6.2 67.8 -2.04 0.97 Yes

1 1021 2 3 61 60 63 2.1 58.1 0.46 0.44

1 4073 2 3 56 56 56 0.6 55.8 0.09 0.27

4 1065 2 3 74 68 59 10.8 125.9 -4.74 0.92 Yes

5 2042 2 3 182 189 222 10.8 -86.6 14.74 0.96 Yes Yes

5 3048 2 4 104 105 107 106 1.2 99.9 0.62 0.68

5 3058 2 4 50 53 53 57 5.5 50.0 1.41 0.72
State GPS GPS Number Both-Wheel-Path IRI (inImi) COy Linear Re ression Results Linear COY Slope

ID Expt Proflle Profile Sequence of Intercept Slope R2 Relationship > 69'0? >3

Runs 1st 2nd 3rd 4th 5th 6th 7th IRI (%) Sie;nificant?

5 3071 2 4 38 38 41 45 8.4 32.7 1.74 0.98 Positive Yes

6 2002 2 5 96 84 88 89 92 4.9 94.4 -0.42 0.03

6 2004 2 4 93 105 116 134 15.4 -74.8 12.56 0.99 Positive Yes Yes

6 2038 2 5 72 67 73 72 74 3.5 59.5 0.62 0.18

6 2040 2 5 66 68 75 91 82 13.7 14.3 4.98 0.70 Positive Yes Yes

6 2041 2 5 81 82 89 91 107 11.8 -28.3 5.72 0.87 Positive Yes Yes

6 2051 2 5 58 65 63 66 65 4.9 48.1 1.37 0.55

6 2053 2 5 84 88 64 98 89 15.0 46.2 2.04 0.Q7 Yes

6 2647 2 5 57 60 92 60 57 23.3 68.1 -0.18 0.00 Yes

6 7452 2 5 87 89 73 93 93 9.5 59.3 1.41 0.09 Yes

6 7491 2 4 100 113 126 145 15.6 -227.3 14.48 0.98 Positive Yes Yes

6 8149 2 4 55 58 69 46 16.6 79.5 -1.12 0.02 Yes

6 8150 2 4 43 39 42 47 8.1 32.8 1.41 0.31 Yes

6 8201 2 4 84 83 88 90 3.8 43.7 2.11 0.76

6 8202 2 4 126 118 130 132 5.0 63.8 2.97 0.40

8 2008 2 4 131 147 164 159 9.9 -27.6 9.14 0.84 Positive Yes Yes

10 1450 2 6 71 76 78 73 78 81 4.7 48.7 1.75 0.61 Positive

12 4096 2 3 38 39 38 1.8 37.6 0.04 0.02

12 4097 2 3 44 45 55 12.1 31.2 2.56 0.84 Yes

12 4100 2 3 44 43 53 11:7 10.7 2.25 0.76 Yes

12 4108 2 3 45 47 52 7.8 37.7 1.71 0.91 Yes

13 4092 2 3 44 44 44 0.6

13 4093 2 3 44 47 47 3.2 42.4 0.58 0.62

13 4096 2 3 58 57 57 1.2 60.0 -0.34 0.90

13 4112 2 3 80 92 89 7.7 52.2 2.31 0.46 Yes

13 4113 2 3 54 57 65 10.1 13.9 2.99 0.96 Yes

13 4119 2 3 62 62 60 1.9 68.7 -0.53 0.75

18 2008 2 5 115 143 168 174 177 16.9 -18.9 14.65 0.82 Positive Yes Yes

18 2009 2 4 97 108 110 122 9.6 54.7 4.87 0.97 Positive Yes Yes

19 6150 2 4 80 77 86 83 4.6 40.6 1.57 0.35

22 3056 2 3 47 54 52 7.1 45.1 1.31 0.44 Yes

24 1632 2 6 66 49 55 55 54 57 10.1 59.5 -0.73 0.04 Yes

24 1634 2 7 60 63 65 65 65 59 60 4.3 70.4 -0.51 0.08
State GPS GPS Number Both-Wheel-Path IRI (in/mil COY Linear Re tre$sionResults Linear COY Slope

ID Expt Profile Profile SCCluence of Intercept Slope R2 Relationship > 6%? >3

Runs 1st 2nd 3rd 4th 5th 6th 7th IRI (%> Simificant?

24 2401 2 6 53 55 55 55 57 61 5.3 49.3 1.56 0.78 Positive

24 2805 2 4 80 83 93 100 10.1 48.2 9.35 0.93 Positive Yes Yes

28 1001 2 3 50 55 59 8.4 39.8 2.81 0.97 Yes

28 1016 2 7 66 69 68 68 67 67 68 1.5 65.6 0.35 0.11

28 1802 2 6 57 64 72 74 75 79 11.8 -15.9 8.88 0.98 Positive Yes Yes

28 2807 2 4 93 98 98 98 2.3 84.4 1.25 0.65

28 3081 2 3 48 49 55 7.6 33.7 2.29 0.91 Yes

28 3082 2 3 67 70 73 4.6 64.7 1.96 0.97

28 3083 2 3 93 93 88 2.8 110.7 -1.44 0.86

28 3085 2 3 94 111 109 8.5 51.6 3.91 0.50 Yes Yes

28 3087 2 3 64 80 85 14.6 13.5 6.49 0.83 Yes Yes

28 3089 2 3 69 74 80 7.8 40.2 3.57 0.99 Positive Yes Yes

28 3090 2 3 77 79 74 2.8 92.7 -0.86 0.38

28 3091 2 3 83 95 111 14.7 -6.8 7.64 1.00 Positive Yes Yes

28 3094 2 3 53 57 55 3.8 47.8 0.66 0.32

30 7076 2 4 45 47 50 63 15.7 13.3 7.11 0.70 Yes Yes

32 1030 2 3 59 56 59 3.1 52.6 0.34 0.06

32 7000 2 5 75 70 74 65 71 5.3 86.5 -1.13 0.25

34 1033 2 6 202 174 177 185 182 184 5.3 216.8 -1.89 0.10

34 1034 2 7 85 86 85 88 88 89 92 2.6 79.1 1.37 0.91 Positive

34 1638 2 6 57 60 63 58 58 61 4.0 57.3 0.35 0.06

35 2006 2 3 89 91 81 6.4 124.3 -2.97 0.74 Yes

35 2118 2 3 72 74 79 5.0 40.6 2.36 1.00 Positive

36 1643 2 7 84 93 82 96 114 134 165 27.9 -102.3 16.61 0.91 Positive Yes Yes

36 1644 2 7 60 61 60 77 63 67 68 9.5 49.2 1.42 0.16 Yes

37 1645 2 6 50 49 51 52 59 56 7.3 43.1 1.79 0.67 Positive Yes

37 2819 2 6 60 62 69 74 51 52 14.8 89.3 -2.66 0.27 Yes

37 2824 2 7 49 45 49 51 67 60 62 14.9 11.0 5.54 0.60 Positive Yes Yes

37 2825 2 7 103 102 105 102 101 106 107 2.2 100.0 0.69 0.28

38 2001 2 4 126 126 136 131 3.6 95.0 3.00 0.54

40 1015 2 3 96 100 101 2.7 82.1 1.26 0.44

40 1017 2 3 86 95 100 7.2 49.7 4.22 0.78 Yes Yes

40 4087 2 4 67 71 70 71 2.7 67.1 0.46 0.24
State GPS GPS Number Both-Wheel-Path IRI (in/mi) COY Linear Re ression Results Linear COY Slope

ID Expt Profile Prome Sequence of Intercept Slope R2 Relationship > 6% '1 >3

Runs 1st 2nd 3rd 4th 5th 6th 7th IRI (%) Si2Ilificant'1

40 4088 2 3 189 169 214 11.6 -27.6 13.43 0.67 Yes Yes

40 4154 2 3 78 81 81 2.3 76.9 0.93 0.55

40 4163 2 3 48 51 65 16.4 23.7 6.28 1.00 Positive Yes Yes

40 4164 2 3 60 80 84 17.3 -22.9 7.12 0.72 Yes Yes

40 4165 2 7 128 132 127 130 129 126 124 2.0 134.2 -0.68 0.20

41 2002 2 4 82 84 86 96 7.1 10.2 3.79 0.78 Yes Yes

47 1023 2 4 50 53 49 50 3.6 55.7 -0.26 0.06

47 1028 2 3 77 89 86 7.6 61.2 2.63 0.62 Yes

47 1029 2 3 45 55 48 10.4 42.0 0.74 0.08 Yes

47 2001 2 3 52 38 44 15.7 71.1 -2.07 0.32 Yes

47 2008 2 3 75 77 72 3.7 90.9 -0.86 0.34

47 3101 2 4 67 73 69 93 15.7 3.0 5.92 0.73 Yes Yes

47 3108 2 4 34 36 36 37 3.8 23.3 0.63 0.66

47 3109 2 3 73 74 74 0.5 72.6 0.09 0.20

47 3110 2 3 41 45 45 5.4 32.3 1.07 0.79

47 9024 2 3 92 91 84 4.8 120.5 -2.13 0.84

47 9025 2 3 95 100 117 10.8 34.9 5.63 0.90 Yes Yes

48 1048 2 4 104 115 113 127 8.1 27.8 4.84 0.85 Positive Yes Yes

48 1049 2 3 70 80 86 9.9 41.4 4.99 0.99 Positive Yes Yes

48 1069 2 4 84 95 91 94 5.3 68.1 1.55 0.38

48 1070 2 4 64 68 73 66 5.3 59.7 0.55 0.08

48 1109 2 3 88 101 107 9.6 55.2 5.86 0.98 Positive Yes Yes

48 2108 2 3 94 97 98 2.6 88.5 1.29 0.73

48 2133 2 3 52 54 53 2.3 49.3 0.58 0.52

48 2172 2 4 46 48 47 64 16.9 11.5 4.07 0.79 Yes Yes

48 2176 2 4 80 78 84 85 3.9 47.0 1.58 0.90 Positive

48 3559 2 4 56 55 54 72 14.7 -15.9 3.43 0.63 Yes Yes

48 3669 2 3 77 84 91 8.5 44.2 4.62 1.00 Positive Yes Yes

48 3679 2 3 114 148 173 20.4 79.2 18.99 0.99 Positive Yes Yes

48 3689 2 3 89 107 120 14.6 60.9 9.58 0.99 Positive Yes Yes

50 1681 2 7 76 76 84 68 54 51 66 18.1 232.3 -6.12 0.42 Yes

50 1683 2 4 134 140 146 141 3.4 0.0 5.21 0.47 Yes

51 2004 2 6 83 85 89 94 100 102 8.6 46.8 4.34 0.99 Positive Yes Yes
State GPS GPS Number Both-Wheel-Path IRI (in/mi) COY Linear Re ression Results Linear COY Slope

ID Expt Proflle Proflle Sequence of Intercept Slope R2 Relationship > 6%1 >3

Runs 1st 2nd 3rd 4th 5th 6th 7th IRI (%) Si~ificant1

51 2021 2 6 93 94 96 93 102 112 7.6 74.0 3.44 0.69 Positive Yes Yes

56 2015 2 7 108 104 110 116 121 117 121 5.7 74.1 2.88 0.84 Positive

56 2017 2 7 77 76 80 79 90 91 92 8.4 53.1 3.25 0.87 Positive Yes Yes

56 2018 2 5 59 57 64 67 67 6.6 44.1 2.31 0.78 Positive Yes

56 2019 2 6 84 89 95 98 103 106 8.5 67.4 4.18 0.98 Positive Yes Yes

56 2020 2 7 61 70 73 84 105 115 130 28.4 -3.8 14.57 0.98 Positive Yes Yes

56 2037 2 6 86 87 85 87 84 83 1.9 88.7 -0.49 0.35

56 7772 2 6 111 105 105 109 109 112 2.7 105.1 0.59 0.14

56 7773 2 6 70 71 73 73 73 74 2.1 68.6 0.73 0.86 Positive

81 2812 2 5 82 86 87 94 93 5.8 68.6 2.75 0.87 Positive

81 8529 2 5 49 51 50 49 52 2.3 45.1 0.24 0.12

82 9017 2 3 67 68 69 1.4 65.5 0.73 0.91

84 1802 2 6 95 99 94 86 91 101 5.8 95.5 -0.10 0.00

87 1680 2 6 82 80 83 79 81 81 1.7 82.3 -0.20 0.07

87 2811 2 6 71 74 76 75 80 75 4.2 57.8 1.18 0.49

87 2812 2 6 58 66 65 72 82 101 21.0 -7.6 7.67 0.86 Positive Yes Yes

88 1647 2 6 79 89 95 98 100 98 8.5 72.9 3.76 0.78 Positive Yes Yes

89 2011 2 6 72 72 74 75 95 74 11.7 46.8 2.44 0.25 Yes

1 3028 3 3 202 217 214 3.8 144.0 3.22 0.58 Yes

5 3011 3 4 72 70 74 77 4.5 57.4 1.68 0.83 Positive

6 3005 3 5 149 164 177 196 210 13.6 -69.2 14.03 0.99 Positive Yes Yes

6 3010 3 4 86 78 81 80 4.5 100.5 -1.42 0.29

6 3013 3 4 116 105 93 95 10.4 169.4 -7.34 0.86 Negative Yes

6 3017 3 4 95 91 98 95 3.0 85.5 0.69 0.11

6 3019 3 4 93 91 60 94 19.7 116.6 -2.74 0.05 Yes

6 3021 3 4 89 87 89 90 1.4 83.9 0.27 0.09

6 3024 3 4 96 96 74 102 13.3 97.3 -0.47 0.00 Yes

6 3030 3 5 81 81 81 82 83 1.0 74.7 0.36 0.55

6 3042 3 5 62 60 109 61 59 31.1 79.7 -0.74 0.00 Yes

6 7456 3 5 140 146 132 140 133 4.1 172.1 -1.67 0.25

6 7493 3 4 89 91 87 88 1.9 95.5 -0.83 0.45

8 3032 3 4 94 94 94 89 2.9 113.8 -1.44 0.64

8 7776 3 4 88 95 97 93 4.1 87.4 1.62 0.42
State GPS GPS Number Both-Wheel-Path IRI (in/mi) COY Linear Re cession Results Linear COY Slope

ID Expt Prome Profile Sequence of Intercept Slope R2 Relationship > 6'10? >3

Runs 1st 2nd 3rd 4th 5th 6th 7th IRI (%) Sie:nificant?

12 3804 3 3 97 112 125 13.0 64.2 6.88 0.99 Positive Yes Yes

12 3811 3 4 106 117 112 118 5.0 77.8 2.24 0.52

12 4000 3 4 104 105 98 87 8.4 181.7 -4.78 0.93 Nee:ative Yes

12 4057 3 3 48 48 53 5.7 43.5 1.10 0.67

12 4059 3 3 63 65 63 2.5 63.8 -0.06 0.01

12 4109 3 3 120 125 117 3.3 123.9 -0.98 0.22

12 4138 3 3 169 175 182 3.7 116.8 3.31 1.00 Positive Yes

13 3007 3 3 112 115 113 1.6 108.9 0.44 0.20

13 3011 3 3 72 72 67 4.2 91.7 -1.31 0.76

13 3015 3 3 77 80 78 2.1 73.0 0.37 0.18

13 3016 3 3 88 83 84 2.9 100.1 -1.05 0.46

13 3017 3 3 79 81 76 3.4 93.7 -0.82 0.34

13 3018 3 3 62 63 64 1.8 52.2 0.58 0.93

13 3019 3 6 92 97 93 95 100 92 3.5 92.3 0.26 0.01

13 3020 3 3 87 83 88 3.3 83.8 0.34 0.05

16 3017 3 6 100 94 101 102 100 106 3.6 94.3 1.16 0.37

16 3023 3 7 96 98 98 96 103 94 94 3.0 99.8 -0.34 0.03

18 3002 3 5 111 113 116 111 110 2.3 119.9 -0.50 0.11

18 3003 3 4 107 107 105 113 3.4 75.4 2.02 0.50

18 3030 3 6 98 95 94 97 97 109 5.6 72.4 2.38 0.59 Positive

18 3031 3 5 98 99 96 98 96 1.5 106.1 -0.57 0.49

19 3006 3 5 176 180 201 200 207 7.1 75.4 7.02 0.82 Positive Yes Yes

19 3009 3 4 144 147 143 145 1.3 146.9 -0.14 0.01

19 3028 3 5 107 108 105 107 110 1.7 104.6 0.41 0.15

19 3033 3 5 107 110 106 103 106 2.3 113.6 -0.85 0.35

19 3055 3 4 108 112 117 114 3.1 61.7 2.29 0.62

20 3013 3 5 101 96 100 101 96 3.0 103.1 -0.52 0.08

20 3015 3 5 70 71 69 69 70 1.2 70.2 -0.05 0.01

20 3060 3 5 70 76 79 83 84 7.0 54.3 3.26 0.91 Positive Yes Yes

21 3016 3 5 97 88 98 94 99 4.4 91.3 0.60 0.06

23 3013 3 6 129 130 141 133 135 138 3.4 107.7 1.46 0.34

23 3014 3 6 91 91 102 93 99 97 4.8 71.0 1.33 0.28

26 3068 3 5 141 134 129 139 127 4.6 171.4 -2.17 0.26
State GPS GPS Number Both-Wheel-Path IRI (in/mi) COY Linear Re treSsionResults Linear COY Slope

ID Expt Profile Profile Sequence of Intercept Slope R2 Relationship > 69'0? >3

Runs 1st 2nd 3rd 4th 5th 6th 7th IRI (%) Si2llificant?

26 3069 3 6 86 84 86 82 83 83 2.2 97.4 -0.75 0.48

27 3003 3 4 123 131 132 99 12.4 163.4 -6.58 0.42 Yes

27 3013 3 4 81 82 78 84 3.2 78.1 0.54 0.09

28 3018 3 3 107 113 108 3.0 108.8 0.06 0.00

28 3019 3 3 100 110 107 4.9 92.9 1.72 0.29

31 3018 3 4 82 98 94 104 9.7 58.0 5.49 0.82 Positive Yes Yes

31 3023 3 5 72 73 73 73 73 0.8 70.6 0.32 0.49

31 3024 3 5 92 91 91 91 95 2.1 87.7 0.61 0.30

31 3028 3 4 76 68 72 68 5.3 91.6 -1.89 0.57

31 3033 3 5 53 60 56 71 63 11.4 43.7 2.83 0.60 Yes

32 3010 3 5 141 144 150 165 151 6.0 115.1 3.76 0.48 Yes Yes

32 3013 3 5 111 112 115 123 124 5.3 80.7 3.55 0.90 Positive Yes

35 3010 3 3 88 90 81 5.3 116.9 -3.75 0.86

37 3008 3 7 113 108 105 104 103 111 112 3.9 108.0 0.00 0.00

37 3011 3 6 105 111 112 100 108 106 4.2 115.4 -0.59 0.06

37 3044 3 6 117 121 126 122 125 128 3.0 83.3 1.56 0.59 Positive

37 3807 3 7 115 124 114 113 113 114 113 3.5 128.8 -1.14 0.24

37 3816 3 7 137 137 136 146 144 129 139 4.0 134.2 0.22 0.00

38 3005 3 5 85 86 50 71 69 20.2 102.3 -4.83 0.23 Yes

38 3006 3 5 62 83 64 59 60 15.0 80.4 -2.34 0.21 Yes

39 3013 3 4 228 251 252 251 4.7 128.6 5.62 0.49 Yes

39 3801 3 5 120 141 124 122 139 7.7 119.0 1.29 0.06 Yes

40 3018 3 3 122 131 133 4.4 80.0 3.14 0.73 Yes

40 4157 3 3 68 78 70 7.4 74.8 -0.43 0.01 Yes

40 4160 3 3 111 107 111 2.2 101.6 0.62 0.09

40 4162 3 3 106 113 108 3.5 109.9 -0.15 0.00

42 1623 3 6 84 82 80 79 81 84 2.4 83.2 -0.19 0.03

42 3044 3 6 151 146 144 144 142 147 2.0 151.0 -0.85 0.30

45 3012 3 3 71 77 75 3.9 63.2 1.08 0.48

46 3009 3 4 183 179 190 195 3.8 137.0 3.27 0.61 Yes

46 3010 3 4 131 117 138 144 8.8 98.6 4.12 0.34 Yes Yes

46 3012 3 5 180 185 187 179 180 2.0 186.7 -0.56 0.05

46 3013 3 5 107 109 92 93 105 8.2 134.3 -2.20 0.14 Yes
State GPS GPS Number Both-Wheel-Path IRI (in/mi) COY Linear Re ression Results Linear COY Slope

10 Expt Profile Profile SeQuence of Intercept Slope R2 Relationship > 6% '1 > 3

Runs 1st 2nd 3rd 4th 5th 6th 7th IRI (%) Si2nificant'1

46 3014 3 4 128 123 127 137 4.4 113.3 2.10 0.38

46 3052 3 4 80 60 61 60 15.3 82.9 -5.11 0.72 Yes

46 3053 3 5 73 76 71 71 79 4.7 69.0 0.84 0.11

48 3003 3 4 133 135 128 139 3.5 122.2 0.72 0.09

48 3010 3 3 138 146 139 3.1 141.7 -0.16 0.00

48 3589 3 5 157 150 146 145 148 3.2 210.7 -1.99 0.48

49 3010 3 5 88 94 114 110 108 11.1 32.3 5.40 0.63 Yes Yes

49 3011 3 7 85 103 103 113 125 121 107 12.2 33.2 6.10 0.71 Positive Yes Yes

49 3015 3 6 125 125 125 128 125 130 1.6 121.2 0.77 0.55 Positive

49 7082 3 4 58 69 64 58 8.9 62.7 -0.19 0.00 Yes

49 7083 3 3 80 71 74 6.2 84.8 -2.89 0.42 Yes

50 1682 3 4 146 158 158 154 3.7 16.7 5.10 0.30 Yes

53 3011 3 5 91 98 99 100 100 3.7 73.5 1.62 0.74 Positive

53 3013 3 6 118 110 98 103 97 97 8.0 187.9 -4.00 0.72 Nee:ative Yes

53 3014 3 6 62 72 57 66 58 63 9.0 66.9 -0.79 0.06 Yes

53 3019 3 7 75 76 57 63 67 58 68 11.4 78.2 -2.10 0.20 Yes

53 3812 3 6 75 78 77 77 79 82 3.0 47.1 1.14 0.69 Positive

53 3813 3 4 114 110 116 125 5.4 37.4 3.15 0.55 Yes

53 7409 3 6 76 65 61 77 68 75 9.8 67.5 0.72 0.03 Yes

55 3008 3 5 217 240 236 241 240 4.2 157.2 4.67 0.58 Yes

55 3009 3 5 159 207 223 220 259 17.0 53.1 20.62 0.86 Positive Yes Yes

55 3010 3 5 119 129 143 148 155 10.5 18.4 8.73 0.96 Positive Yes Yes

55 3012 3 4 107 104 102 110 3.1 97.9 0.55 0.07

55 3014 3 4 206 221 238 208 6.9 175.1 2.84 0.06 Yes

55 3015 3 5 124 135 128 134 144 5.6 104.3 3.71 0.65 Positive Yes

55 3016 3 5 79 84 79 82 87 4.0 74.7 1.27 0.39

55 3019 3 5 69 77 78 79 72 5.6 59.6 0.97 0.19

55 6351 3 4 126 122 120 126 2.6 123.4 -0.01 0.00

55 6352 3 4 78 78 79 79 0.9 77.5 0.25 0.28

55 6353 3 4 95 91 92 103 5.7 85.5 2.38 0.46

55 6354 3 4 78 79 78 77 1.1 79.9 -0.37 0.49

55 6355 3 4 107 105 94 104 5.8 107.3 -1.19 0.09

56 3027 3 6 143 164 163 191 197 192 12.2 59.2 10.74 0.90 Positive Yes Yes
State GPS GPS Number Both-Wheel-Path IRI (in/mi) COY Linear Re ressionResults Linear COY Slope

ID Expt Proflle ProflleSeQuence of Intercept Slope R2 Relationship > 6% '1 >3

Runs 1st 2nd 3rd 4th 5th 6th 7th IRI (%) Si2nificant'1

83 3802 3 5 117 109 123 142 176 19.9 46.2 14.84 0.87 Positive Yes Yes

84 3803 3 6 152 151 177 179 180 191 9.5 74.9 8.24 0.87 Positive Yes Yes

89 3001 3 7 153 161 153 160 151 152 157 2.6 160.8 -0.42 0.03

89 3002 3 6 221 226 243 233 242 241 3.9 184.3 3.95 0.63 Positive Yes

89 3015 3 6 74 95 96 106 105 141 21.6 34.8 10.71 0.80 Positive Yes Yes

89 3016 3 7 191 184 187 187 182 187 183 1.7 193.2 -0.97 0.29

1 4007 4 3 94 103 98 4.7 77.3 0.97 0.16

1 4084 4 3 187 209 213 7.0 52.5 6.91 0.86 Yes Yes

5 3059 4 4 105 104 110 115 4.9 72.2 2.72 0.97 Positive

5 3073 4 4 120 129 127 124 3.0 114.8 0.37 0.03

5 3074 4 4 120 134 146 156 11.1 -62.3 7.76 0.98 Positive Yes Yes

5 4019 4 4 108 111 103 106 3.2 129.6 -1.18 0.35

5 4021 4 4 116 121 118 119 1.7 114.2 0.20 0.03

5 4023 4 4 163 168 163 158 2.6 193.3 -1.76 0.56

5 4046 4 4 101 108 109 105 3.8 95.5 0.78 0.12

9 4008 4 6 99 94 92 84 87 96 6.0 100.1 -1.22 0.16 Yes

10 1201 4 6 127 116 116 110 119 118 4.8 148.2 -1.18 0.11

10 4002 4 6 135 130 131 119 120 136 5.6 164.2 -2.44 0.19

17 4074 4 4 108 109 105 112 2.7 105.2 0.60 0.11

17 4082 4 5 90 90 87 103 81 9.2 95.4 -0.97 0.03 Yes

18 4021 4 5 140 135 156 127 138 7.9 166.0 -1.59 0.06 Yes

18 4042 4 4 141 152 138 147 4.5 138.4 0.34 om

18 5518 4 3 84 85 86 1.0 65.2 0.98 0.55

20 4016 4 4 89 89 91 90 1.1 82.3 0.61 0.64

20 4052 4 5 94 120 102 104 105 8.8 100.4 0.52 0.01 Yes

20 4053 4 5 87 88 91 100 101 6.9 75.2 3.73 0.87 Positive Yes Yes

20 4054 4 4 100 112 106 114 5.7 88.6 3.31 0.48 Yes

20 4063 4 5 123 129 131 125 130 2.7 117.8 0.92 0.18

21 4025 4 4 160 167 170 177 4.2 73.5 5.14 0.99 Positive Yes

22 4001 4 3 111 122 126 6.5 37.6 3.96 0.84 Yes Yes

26 4015 4 7 102 103 103 104 103 102 103 0.8 102.5 0.06 0.02

27 4033 4 4 79 89 82 95 8.2 45.4 3.66 0.56 Yes Yes

27 4034 4 4 106 113 112 111 2.6 86.5 1.25 0.41
State GPS GPS Number Both-Wheel-PathIRI (inImi) COY Linear Re ression Results Linear COY Slope

ID Expt Proflle Proflle Seauence of Intercept Slope R2 Relationship > 6% '1 >3

Runs 1st 2nd 3rd 4th 5th 6th 7th IRI (%) Silmificant'1

27 4037 4 4 87 97 89 99 6.3 65.8 2.62 0.44 Yes

27 4040 4 4 109 120 111 114 4.4 104.3 0.76 0.07

27 4050 4 4 86 82 87 94 5.7 49.7 1.87 0.44

27 4054 4 4 111 116 123 122 4.8 56.1 3.18 0.81 Yes

27 4055 4 4 74 75 68 76 5.1 74.8 -0.10 0.00

27 4082 4 3 113 125 130 7.2 -76.5 9.15 0.99 Positive Yes Yes

28 4024 4 3 96 97 93 2.4 127.3 -1.73 0.47

29 4036 4 5 83 82 83 87 86 2.7 75.3 1.04 0.57

29 5000 4 5 128 126 128 132 133 2.0 108.8 1.44 0.81 Positive

29 5058 4 5 101 100 102 103 100 1.3 100.5 0.06 0.01

29 5081 4 5 115 113 118 127 131 6.6 56.0 4.53 0.88 Positive Yes Yes

29 5091 4 5 99 97 98 100 104 2.7 82.6 1.19 0.54

29 5473 4 5 100 68 70 68 69 18.6 256.8 -5.77 0.46 Yes

29 5503 4 4 80 90 86 77 6.8 97.7 -1.55 0.14 Yes

31 4019 4 4 108 124 119 131 8.0 39.3 5.38 0.86 Positive Yes Yes

34 4042 4 6 117 161 121 109 126 121 14.4 187.3 -2.50 0.06 Yes

36 4017 4 6 129 123 136 120 131 127 4.4 128.0 -0.02 0.00

36 4018 4 6 113 109 110 106 117 109 3.6 110.3 0.01 0.00

39 4018 4 5 107 108 114 106 105 3.2 116.9 -0.52 0.08

39 4031 4 4 123 126 126 128 1.6 103.9 0.98 0.94 Positive

42 1606 4 7 90 93 93 87 93 97 94 3.4 81.5 0.89 0.26

42 1690 4 7 112 114 105 114 111 109 112 2.8 112.0 -0.06 0.00

48 3699 4 3 99 105 107 4.0 60.0 2.37 0.86

48 4142 4 7 121 123 125 123 126 133 124 2.9 105.5 1.24 0.36

48 4143 4 7 141 146 140 141 142 149 149 2.6 122.7 0.96 0.20

48 4146 4 3 136 137 135 0.8 141.6 -0.55 0.66

48 4152 4 4 164 176 166 172 3.4 160.5 0.86 0.08

54 4003 4 6 100 110 100 96 105 101 4.7 105.9 -0.40 0.02

54 4004 4 6 185 171 182 182 196 219 8.8 133.3 7.04 0.63 Positive Yes Yes

1 5008 5 3 60 59 59 1.4 64.6 -0.38 0.53

5 5803 5 4 95 97 94 92 2.1 110.7 -0.84 0.54

5 5805 5 4 79 86 81 83 3.8 75.5 0.39 0.06

6 7455 5 5 79 76 61 75 74 9.5 91.6 -0.91 0.05 Yes
State GPS GPS Number Both-Wheel-Path IRI (in/mi) COY Linear Re ression Results Linear COY Slope

ID Expt Prome Prome SCQuence of Intercept Slope R2 Relationship > 690? >3

Runs 1st 2nd 3rd 4th 5th 6th 7th IRI (%) Shmificant?

9 5001 5 6 118 127 118 112 118 118 4.0 128.3 -0.94 0.12

10 5004 5 7 79 68 76 72 77 76 85 7.0 51.6 1.64 0.23 Yes

10 5005 5 7 69 67 68 68 64 70 74 4.5 43.6 1.21 0.35

13 5023 5 3 90 89 80 6.0 130.9 -2.46 0.84 Yes

16 5025 5 6 140 137 140 143 137 150 3.5 114.5 1.36 0.28

17 5020 5 4 76 78 75 74 2.3 79.8 -0.79 0.58

17 5843 5 4 77 82 93 86 8.1 56.2 2.98 0.48 Yes

17 5849 5 4 96 87 86 86 5.5 140.2 -2.53 0.72

17 5854 5 4 133 140 146 147 4.6 105.8 3.43 0.98 Positive Yes

17 5869 5 4 110 104 105 105 2.6 117.5 -0.92 0.38

17 5908 5 4 127 128 128 128 0.4 122.9 0.24 0.48

17 9267 5 4 70 70 75 72 3.1 51.6 0.78 0.40

18 5022 5 3 136 133 138 1.8 133.0 0.14 0.00

18 5043 5 4 136 151 150 138 5.4 126.6 0.74 0.03

19 5042 5 5 108 106 107 107 110 1.4 99.2 0.49 0.28

19 5046 5 5 97 99 100 99 98 0.9 97.6 0.06 0.01

26 5363 5 5 111 114 116 114 116 1.8 99.4 1.02 0.53

28 5006 5 3 92 93 89 2.2 102.9 -0.87 0.44

28 5803 5 3 100 103 98 2.7 113.2 -1.02 0.35

28 5805 5 3 83 88 79 5.7 103.8 -1.23 0.22

29 5047 5 5 99 96 98 97 98 1.3 99.7 -0.11 0.02

31 5052 5 5 68 68 70 68 68 1.2 66.3 0.10 0.06

37 5037 5 6 74 76 71 65 71 67 5.9 104.2 -1.71 0.52

37 5826 5 7 84 75 77 73 74 80 79 5.0 85.3 -0.52 0.06

37 5827 5 6 61 65 60 57 60 62 4.4 68.4 -0.40 0.07

38 5002 5 4 80 82 79 77 2.5 91.9 -0.68 0.33

39 5003 5 5 69 66 65 72 71 4.6 65.4 0.95 0.29

40 4158 5 3 57 65 68 9.0 54.0 3.44 0.73 Yes Yes

40 5021 5 3 58 61 59 2.4 59.4 -0.07 0.01

41 5005 5 4 84 82 79 79 2.9 89.4 -1.46 0.87 Ne2ative

41 5006 5 6 86 83 87 87 86 85 1.7 86.2 -0.02 0.00

41 5008 5 6 59 58 59 60 61 60 1.7 52.2 0.36 0.38

41 5021 5 4 71 64 66 68 4.7 70.7 -0.72 0.11
State GPS GPS Number Both-Wheel-Path IRI (in/mi) COY Linear Re ression Results Linear COY Slope

ID Expt ProfUe ProfUeSeQuence of Intercept Slope R2 Relationship > 6% '1 >3

Runs 1st 2nd 3rd 4th 5th 6th 7th IRI (%) Si2nificant'1

41 5022 5 4 64 59 64 64 -3.7 61.1 0.24 0.02

41 7081 5 7 48 50 53 47 48 49 47 4.2 50.1 -0.36 0.11

42 1598 5 6 106 107 103 99 108 109 3.3 101.2 0.23 0.02

42 5020 5 7 119 119 116 114 115 117 120 2.0 116.1 0.08 0.00

45 5017 5 3 125 130 129 2.0 113.7 1.08 0.62

45 5034 5 3 96 90 91 3.5 114.2 -1.29 0.55

45 5035 5 3 81 80 78 1.9 92.5 -0.77 0.92

46 5020 5 5 60 62 62 61 62 1.2 57.0 0.25 0.22

46 5025 5 5 82 79 82 80 81 1.8 80.9 0.01 0.00

46 5040 5 4 126 135 135 132 3.2 89.1 1.51 0.35

48 3719 5 3 154 152 151 1.0 177.6 -0.93 1.00 Ne2ative

48 3779 5 4 152 136 134 139 5.6 166.2 -1.86 0.19

48 5024 5 3 156 163 162 2.3 147.2 1.35 0.33

48 5026 5 3 108 106 106 0.9 108.6 -0.54 0.73

48 5035 5 4 112 116 109 118 3.6 108.8 0.40 0.04

48 5154 5 3 103 98 98 2.7 122.6 -1.15 0.49

48 5274 5 4 101 109 103 105 3.2 96.9 0.39 0.06

48 5278 5 4 108 105 105 106 1.2 114.7 -0.50 0.50

48 5283 5 4 72 74 73 75 1.6 71.6 0.43 0.58

48 5284 5 4 124 139 125 153 10.3 114.1 4.80 0.50 Yes Yes

48 5287 5 4 118 123 119 128 3.9 88.3 1.78 0.60

48 5301 5 4 102 107 103 106 2.4 98.6 0.55 0.19

48 5310 5 4 123 133 122 127 3.9 127.3 -0.22 0.01

48 5317 5 4 138 143 144 149 3.0 122.2 2.06 0.96 Positive

48 5323 5 4 113 113 110 115 1.8 110.3 0.20 0.03

48 5328 5 4 106 104 101 103 2.0 113.2 -0.59 0.28

48 5334 5 3 70 74 68 4.1 97.9 -1.26 0.30

48 5335 5 4 129 132 127 128 1.8 137.9 -0.68 0.27

48 5336 5 4 89 93 92 91 1.9 90.0 0.14 0.03

51 2564 5 6 61 61 62 57 62 64 3.9 53.0 0.35 0.07

51 5008 5 6 138 150 132 128 122 127 7.6 192.4 -4.04 0.54 Ne2ative Yes

51 5009 5 6 143 138 141 140 142 144 1.7 134.7 0.57 0.17

51 5010 5 6 98 93 101 99 102 106 4.4 93.3 2.03 0.61 Positive
State GPS GPS Number Both-Wheel-PathIRI (in/mi) COY Linear Re ression Results Linear COY Slope

ID Expt Prome Prome Sequence of Intercept Slope R2 Relationship > 690? >3

Runs 1st 2nd 3rd 4th 5th 6th 7th IRI (%) Sienificant?

54 5007 5 3 144 155 160 5.4 18.1 10.14 0.95 Yes

55 5037 5 4 73 77 71 71 3.7 86.8 -0.76 0.31

55 5040 5 5 147 150 148 149 151 1.2 140.6 0.72 0.43

1 4127 6B 3 57 59 55 3.2 59.5 -0.70 0.36

1 6012 6A 3 76 102 121 23.0 0.6 12.02 1.00 Positive Yes Yes

1 6019 6A 3 42 50 45 8.8 38.0 0.65 0.12 Yes

4 6053 6A 3 77 86 88 6.8 71.6 3.70 0.96 Yes Yes

4 6054 6A 4 48 51 59 63 12.4 43.6 3.34 0.93 Positive Yes Yes

4 6055 6A 3 40 43 45 5.8 37.0 - 1.66 0.99 Positive

6 6044 6A 5 54 56 87 56 58 22.6 59.6 0.66 0.01 Yes

8 6002 6A 4 154 165 169 190 9.0 134.7 9.37 0.86 Positive Yes Yes

8 6013 6A 4 115 121 147 139 11.7 98.9 8.51 0.69 Yes Yes

8 7781 6B 5 71 76 80 82 84 6.7 70.0 5.94 0.91 Positive Yes Yes

8 7783 6A 4 63 70 72 75 7.3 60.8 3.15 0.97 Positive Yes Yes

11 1400 6B 6 199 185 195 184 194 197 3.3 192.3 -0.01 0.00

16 6027 6A 6 82 83 87 87 104 84 9.3 79.6 2.17 0.26 Yes

17 6050 6A 3 47 53 53 6.5 27.3 1.67 0.95 Yes

18 6012 6A 3 80 103 93 12.1 66.0 2.40 0.20 Yes

19 6049 6A 5 95 103 89 109 108 8.6 49.3 3.10 0.38 Yes Yes

20 1006 6A 5 93 94 95 102 98 3.7 62.8 1.59 0.50

21 6040 6A 4 96 94 98 106 5.4 68.7 3.12 0.63 Yes

21 6043 6A 5 63 67 63 66 66 2.7 58.6 0.48 0.22

26 6016 6A 5 75 54 56 56 66 14.5 68.8 -1.08 0.04 Yes

28 3093 6B 3 62 63 60 2.4 63.6 -0.58 0.49

29 5403 6B 5 66 65 65 78 70 7.8 63.6 2.02 0.35 Yes

29 5413 6B 5 59 62 60 73 60 9.3 59.5 1.34 0.13 Yes

-29 6067 6A 5 91 92 95 95 103 5.1 64.3 2.77 0.80 Positive

30 6004 6A 6 104 99 105 126 116 120 9.5 95.4 4.37 0.57 Positive Yes Yes

30 7066 6B 3 51 54 56 4.5 51.1 2.59 0.94

30 7075 6A 6 57 57 56 62 62 64 5.9 54.3 1.72 0.79 Positive

30 7076 6B 3 63 62 59 3.3 64.4 -2.08 0.94

30 7088 6B 4 40 43 44 44 4.7 40.7 1.46 0.75

31 6700 6B 4 82 92 88 100 8.2 75.6 4.55 0.85 Positive Yes Yes
State GPS GPS Number Both-Wheel-Path IRI (inlmil COY Linear Reuession Results Linear COY Slope

ID Expt Profile Profile Sequence of Intercept Slope R2 Relationship > 6%? >3

Runs 1st 2nd 3rd 4th 5th 6th 7th IRI (%) Significant?

34 6057 6A 5 96 95 96 96 96 0.5 94.6 0.09 0.09

35 1002 6A 3 43 52 67 21.9 -4).2 7.49 1.00 Positive Yes Yes

35 2007 6A 3 30 33 35 8.0 14.5 1.59 0.93 Yes

35 6033 6A 3 80 83 93 7.6 39.6 4.08 0.98 Positive Yes Yes

35 6035 6A 3 71 74 91 13.4 31.7 6.56 0.95 Yes Yes

35 6401 6A 3 37 45 68 32.0 -33.2 10.17 0.99 Positive Yes Yes

36 1008 6B 5 78 75 62 74 66 9.5 79.2 -2.74 0.45 Yes

37 1803 6B 5 68 52 53 53 51 12.3 62.5 -3.13 0.58 Yes

38 6004 6A 3 166 182 204 10.4 -59.0 20.65 0.96 Yes Yes

39 6019 6A 3 97 63 64 25.4 105.0 -13.57 0.62 Yes

40 4086 6B 3 50 64 71 17.4 43.8 6.89 0.77 Yes Yes

40 4161 6B 3 80 90 104 13.4 70.9 7.76 0.99 Positive Yes Yes

40 6010 6A 3 94 105 103 5.7 86.0 2.04 0.33

41 6011 6A 4 61 54 58 75 14.8 49.6 3.95 0.38 Yes Yes

41 6012 6A 3 56 51 58 6.7 55.5 -4).26 0.00 Yes

42 1608 6A 6 102 103 105 104 110 110 3.4 99.4 1.70 0.82 Positive

42 1618 6B 6 105 108 105 104 110 110 2.6 106.2 0.73 0.32

46 9106 6B 4 58 61 54 59 5.1 59.6 -4).64 0.06

46 9197 6B 4 52 59 58 66 9.4 50.9 3.15 0.89 Positive Yes Yes

47 6015 6A 3 56 50 54 5.7 56.6 -4).47 0.09

47 6022 6A 3 64 79 77 11.5 3.7 5.36 0.83 Yes Yes

48 1046 6A 4 84 91 95 95 5.5 37.8 2.46 0.78

48 1093 6B 3 45 48 49 5.1 43.5 1.29 0.86

48 1119 6B 4 64 64 65 64 1.0 64.4 0.07 0.04

48 6079 6A 4 155 164 188 184 9.2 110.8 7.98 0.81 Positive Yes Yes

48 6086 6A 3 49 46 48 3.2 49.3 -4).22 0.06

48 6160 6A" 3 112 121 136 10.0 19.3 9.39 0.99 Positive Yes Yes

48 6179 6A 4 61 66 69 90 17.9 -44.9 6.55 0.84 Positive Yes Yes

49 1004 6A 5 132 146 170 174 194 14.9 117.4 14.03 0.97 Positive Yes Yes

49 1005 6A 6 41 42 39 59 59 56 19.6 33.8 4.15 0.67 Positive Yes Yes

49 1006 6A 5 45 51 51 51 52 6.0 45.5 1.38 0.61 Yes

49 1007 6A 5 66 67 67 69 74 5.0 62.6 1.83 0.82 Positive

51 1417 6B 5 69 73 71 74 77 4.2 69.0 1.88 0.81 Positive
State GPS GPS Number Both-Wheel-Path IRI (inImD COV Linear Re cession Results Linear COV Slope

10 Expt ProfUe ProfUe Seauence of Intercept Slope R2 Relationship > 6%? >3

Runs 1st 2nd 3rd 4th 5th 6th 7th IRI (%) Si20ificant?

51 1419 68 6 85 88 86 86 87 89 1.8 85.9 0.46 0.28

51 1423 68 6 115 117 114 116 124 131 5.7 112.0 2.90 0.66 Positive

53 1005 68 5 46 41 45 53 48 9.6 40.5 1.97 0.39 Yes

53 6020 6A 6 43 42 43 43 43 41 1.9 43.3 -0.23 0.25

53 6048 6A 6 79 76 86 62 63 62 14.5 86.4 -3.99 0.44 Yes

53 6049 6A 5 73 82 77 78 85 5.8 73.0 1.61 0.43

53 6056 6A 5 62 59 62 63 67 4.7 57.2 1.76 0.66 Positive

53 7322 6A 5 47 43 47 48 51 6.0 44.5 1.36 0.42

56 6029 6A 6 74 76 75 83 79 78 3.9 73.5 1.04 0.41

56 6031 6A 6 96 106 117 115 121 127 9.8 92.2 5.73 0.90 Positive Yes Yes

56 6032 6A 6 70 71 74 79 77 86 7.7 65.5 2.85 0.82 Positive Yes

82 6006 6A 4 80 82 84 82 1.8 79.0 0.89 0.59

82 6007 6A 3 112 119 117 2.9 110.0 1.94 0.45

83 6450 68 6 52 51 52 51 52 51 1.2 51.5 -0.06 0.04

83 6451 68 6 69 67 63 70 64 64 4.5 67.9 -0.73 0.22

84 6804 6A 6 60 61 60 52 61 65 7.0 53.2 0.51 0.05 Yes

86 6802 6A 6 85 92 108 116 129 122 15.9 -36.5 8.97 0.90 Positive Yes Yes

90 6400 6A 6 76 80 91 94 98 111 13.6 18.5 6.60 0.96 Positive Yes Yes

90 6410 68 5 90 66 52 76 70 19.5 80.5 -3.51 0.16 Yes

90 6412 68 5 66 54 70 62 56 11.0 64.5 -1.16 0.08 Yes

90 6801 6A 5 93 107 111 114 132 12.5 22.8 8.35 0.86 Positive Yes Yes

8 7035 7A 5 89 87 98 95 93 5.2 85.1 1.76 0.35

8 7036 7A 4 100 96 117 106 8.8 92.5 3.34 0.29 Yes Yes

9 4020 78 4 97 98 97 102 2.5 95.3 1.36 0.55

13 7028 7A 3 64 64 70 5.5 55.2 1.70 0.78

17 5151 78 3 55 77 75 18.0 53.6 7.01 0.96 Yes Yes

17 5453 7A 4 78 75 75 73 2.7 84.9 -1.19 0.96 Ne2ative

17 7937 7A 3 104 110 129 11.6 57.1 7.09 1.00 Positive Yes Yes

18 5528 78 3 57 58 69 10.5 51.1 5.02 0.80 Yes Yes

18 5538 7B 3 53 52 52 1.2 52.8 -0.42 0.64

19 9116 78 4 53 51 56 53 3.8 52.5 0.21 0.04

19 9126 78 5 76 78 79 81 83 3.3 75.2 1.36 0.92 Positive

20 7073 7A 3 75 79 106 19.4 -88.7 15.50 0.85 Yes Yes
State GPS GPS Number Both-Wheel-PathIRI (in/mi) COy Linear Re ression Results Linear COY Slope

ID Expt Prof11e Profile Sequence of Intercept Slope R2 Relationship > 6~? >3

Runs 1st 2nd 300 4th 5th 6th 7th IRI (~) Simificant?

20 7085 7A 3 104 99 104 2.8 100.1 0.32 0.01

23 7023 7A 6 56 63 57 53 53 63 7.7 56.9 0.06 0.00 Yes

26 7072 7A 5 48 52 59 56 68 13.7 29.5 3.96 0.87 Positive Yes Yes

27 5076 7B 3 60 52 56 7.4 59.4 -1.68 0.22 Yes

27 7090 7A 4 105 114 107 113 4.2 93.5 1.76 0.31

28 3097 7A 3 60 64 63 3.4 57.5 0.58 0.19

29 4069 7B 3 86 80 77 5.7 87.0 -3.99 0.99 Negative

29 5393 7B 4 84 86 86 84 1.4 85.7 -0.26 0.09

29 5483 7B 3 57 58 54 3.3 57.9 -1.01 0.38

29 7054 7A 4 61 60 63 64 3.1 38.8 1.24 0.67

29 7073 7A 5 72 73 75 76 82 5.1 50.2 2.25 0.94 Positive

31 6702 7B 4 44 56 45 44 12.6 48.8 -0.51 0.02 Yes

31 7005 7A 4 105 III 109 113 3.1 92.4 1.91 0.73

31 7017 7A .4 109 133 132 133 9.7 64.6 6.27 0.69 Yes Yes

31 7050 7A 4 85 91 86 85 3.2 88.5 -0.27 0.02

39 5010 7B 4 62 59 52 48 11.2 63.0 -3.97 0.97 Ne211tive Yes

39 7021 7A 5 90 99 115 105 118 10.9 68.0 5.38 0.74 Positive Yes Yes

40 7024 7A 3 93 109 112 9.9 52.1 5.82 0.69 Yes Yes

41 7018 7A 5 77 77 63 44 54 22.6 91.2 -9.16 0.65 Yes

41 7019 7A 5 99 87 97 97 96 5.1 94.1 0.32 0.01

41 7025 7A 4 100 96 114 165 27.1 60.8 18.38 0.71 Yes Yes

42 1610 7A 6 71 66 66 61 64 64 4.8 72.0 -1.27 0.59 Negative

42 1613 7B 5 67 76 67 65 63 7.4 72.2 -1.97 0.37 Yes

42 1614 7B 6 64 6S 69 68 69 74 5.3 63.6 1.77 0.88 Positive

42 1617 7B 4 52 51 54 54 2.8 51.5 0.86 0.52

42 7025 7A 6 95 94 89 . 95 93 97 2.9 92.0 0.46 0.09

42 7037 7A 6 59 60 65 66 54 56 8.4 65.9 -0.82 0.09 Yes

44 7401 7A 6 71 73 70 70 71 71 1.5 71.4 -0.11 0.04

45 7019 7A 3 86 96 89 5.5 85.9 0.62 0.05

46 7049 7A 4 86 108 105 107 10.2 83.7 5.10 0.66 Yes Yes

48 3629 7A 3 56 50 48 7.6 70.5 -2.22 0.81 Yes

54 7008 7A 4 122 132 142 128 6.4 103.6 4.37 0.33 Yes Yes

55 7030 7A 3 145 163 165 6.9 26.3 9.76 0.88 Yes Yes
State GPS GPS Number Both-Wheel-Path IRI (inlmi) COy Linear Re tJ'CSsionResults Linear COY Slope

ID Expt Profile Profile Sequence of Intercept Slope R2 Relationship > 6%? >3

Runs 1st 2nd 3rd 4th 5th 6th 7th IRI (%) Sie:nificant?

83 6452 7B 6 73 79 81 85 83 91 7.0 75.5 2.69 0.79 Positive Yes

6 9048 9 4 112 103 114 109 4.6 109.1 0.08 0.00

6 9049 9 5 71 69 67 88 71 11.4 67.3 1.62 0.11 Yes

6 9107 9 5 75 81 87 91 110 14.8 62.0 7.20 0.90 Positive Yes Yes

8 9019 9 4 87 93 115 106 12.4 75.3 6.77 0.66 Yes Yes

8 9020 9 4 6S 80 110 88 21.8 52.8 8.83 0.50 Yes Yes

13 4118 9 3 36 38 38 2.2 29.7 0.36 0.67

18 9020 9 4 96 99 97 97 1.3 96.3 0.16 0.09

20 9037 9 5 113 121 130 128 131 6.1 63.8 4.26 0.80 Positive Yes Yes

26 9029 9 5 116 119 118 83 117 14.2 141.5 -4.05 0.15 Yes

26 9030 9 6 115 122 121 120 115 115 2.8 126.2 -1.11 0.21

27 6300 9 3 85 94 104 9.8 62.1 5.43 0.97 Yes Yes

27 9075 - 9 3 122 125 121 1.8 128.2 -0.36 0.08

28 9030 9 3 65 69 60 7.3 70.2 -1.38 0.31 Yes

31 6701 9 5 122 133 136 150 153 9.2 105.8 7.23 0.99 Positive Yes Yes

39 5569 9 3 141 144 144 1.3 114.5 1.31 0.70

39 9006 9 4 94 95 99 97 2.1 90.3 0.95 0.47

39 9022 9 5 86 87 97 96 90 5.8 85.2 1.55 0.29

40 4155 9 3 64 63 61 2.6 65.5 -1.18 1.00 Nee:ative

42 1627 9 6 118 121 116 124 109 116 4.5 122.2 -1.18 0.17

42 9027 9 6 181 175 171 172 169 174 2.3 183.6 -1.36 0.40

48 3569 9 3 76 84 83 5.2 70.7 2.17 0.62

48 3845 9 3 106 114 109 3.7 108.1 0.77 0.06

48 9167 9 3 111 116 112 2.3 112.6 0.14 0.01

89 9018 9 5 125 122 144 146 164 12.4 92.4 10.36 0.89 Positive Yes Yes
1. Spangler, E. B. and Kelly, W. J., Long-Term Time Stability of Pavement Ride Quality Data.

Federal Highway Administration Report FHW A/OH-91/001, 1990, 99p.

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Highway Research Program, National Research Council, Washington, D.C., 1994, BOp.

3. Sayers, M. W., Gillespie, T. D., Queiroz, A.V., The International Road Roughness

Experiment, World Bank Technical Paper Number 45, World Bank, 1986,453 p.

4. Gillespie, T. D., Sayers, M. W., and Segel, L., Calibration of Response Type Road

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Calibrating Road Roughness Measurements, World Bank Technical Paper Number 46,

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Measuring and Interpreting Road Profiles, University of Michigan Transportation

Research Institute, 1996, 101p.

7. Spangler, E. B., and Kelly, W. J., Integration of the Inertial Profilometer in the Ohio DOT

Pavement Management System, Ohio Department of Transportation Report FHWA/OH87/005, 1987, 122p.

8. Janoff, M. S., Pavement Roughness and Rideability, National Cooperative Highway

Research Program Report 275, 1985, 69p.

9. Janoff, M. S. Pavement Roughness and Rideability Field Evaluation. National Cooperative

Highway Research Program Report 308, 1988, 54p.

10. Sayers, M. W. and Karamihas, S. M., Interpretation of Road Roughness Profile Data,

Federal Highway Administration, Report No. FHWA-RD-96-101, University of Michigan

Transportation Research Institute, June 1996, 166p.

11. Long Term Pavement Performance Information Management System, Data User's Guide,

Report No. FHWA-RD-93-094, 41p.

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Pendleton, O. J., and Lee, Y. Sensitivity Analysis for Selected Pavement Distresses, SHRPP-393, Strategic Highway Research Program, National Research Council, Washington,

D.C., 1994, 341p.

13. Paterson, W.D.O., Road Deterioration and Maintenance Effects, Models for Planning and

Management, World Bank, 1987, 453p.

14. Chaparral Systems Corporation, Monitored Traffic Data Status Report, October 1995, 95p.

15. Lasdon, L. S., Waren, A., Jain, A., and Ratner, M., Design and Testing of a Generalized

Reduced Gradient Code for Nonlinear Programming, ACM Transactions on Mathematical

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