Improving Pavements With 

Long-Term Pavement Performance: 

Products for Today and Tomorrow

Papers From the 2003–2004 International Contest on 

Long-Term Pavement Performance Data Analysis

Sponsored by the Federal Highway Administration 

and the American Society of Civil Engineers

PUBLICATION NO. HRT-06-109 

SEPTEMBER 2006
FOREWORD 

In 1998, the Federal Highway Administration (FHWA), Long-Term Pavement Performance (LTPP) Program 

and the Highway Division Pavements Committee of the American Society of Civil Engineers (ASCE) initiated 

a program to organize an international contest on the use of LTPP data. The competition was designed to 

promote the use of LTPP data and involve the future pavement engineers in university in the analysis of data 

from the LTPP database. The program has been in operation for 5 years with four contests completed. The 

papers contained in this document are the result of the 2003–2004 contest. 

Gary L. Henderson 

Director, Office of Infrastructure 

 Research and Development 

Notice 

This document is disseminated under the sponsorship of the U.S. Department of Transportation in the interest 

of information exchange. The U.S. Government assumes no liability for the use of the information contained in 

this document. This report does not constitute a standard, specification, or regulation. 

The U.S. Government does not endorse products or manufacturers. Trademarks or manufacturers' names 

appear in this report only because they are considered essential to the objective of the document. 

Quality Assurance Statement 

The Federal Highway Administration (FHWA) provides high-quality information to serve Government, 

industry, and the public in a manner that promotes public understanding. Standards and policies are used to 

ensure and maximize the quality, objectivity, utility, and integrity of its information. FHWA periodically 

reviews quality issues and adjusts its programs and processes to ensure continuous quality improvement. 
 Technical Report Documentation Page 

1. Report No. 

FHWA-HRT-06-109

2. Government Accession 

No. 

 

3. Recipient’s Catalog No. 

 

5. Report Date 

September 2006 

4. Title and Subtitle 

Improving Pavements With Long-Term Pavement 

Performance: Products for Today and Tomorrow 

Papers From the 2003–2004 International Contest on LongTerm Pavement Performance Data Analysis

6. Performing Organization Code 

 

7. Author(s) 

J.Arellano, M.M. de Farias, R.O. de Souza, V.P.K. Ganesan, 

M.P. McDonald, D. Morian, S.D. Neto, J.T. Smith, S.M. 

Stoffels, S.L. Tighe 

8. Performing Organization Report 

No. 

 

10. Work Unit No. 

 

9. Performing Organization Name and Address 

Office of Infrastructure Research and Development 

Federal Highway Administration 

6300 Georgetown Pike, HRDI-13 

McLean, VA 22101-2296

11. Contract or Grant No. 

 

13. Type of Report and Period 

Covered 

2003–2004 

12. Sponsoring Agency Name and Address 

American Society of Civil Engineers (ASCE) 

Transportation and Development Institute (T&DI) 

Federal Highway Administration Long-Term Pavement 

Performance (LTPP) Program

14. Sponsoring Agency Code 

 

15. Supplementary Notes 

The Contract Officer’s Technical Representative (COTR) was Antonio Nieves—HRDI-13. 

16. Abstract 

This report is a compilation of award-winning technical papers from the Third Annual 

International Contest on LTPP Data Analysis 2003–2004, various authors. 

17. Key Words 

LTPP, pavement performance, DataPave 

contest, DataPave 

18. Distribution Statement 

No restrictions. This document is available 

to the public through the National Technical 

Information Service, Springfield, VA 22161. 

19. Security Classif. (of this report) 

Unclassified 

20. Security 

Classif. (of this 

page) 

Unclassified 

21. No of Pages 

100 

22. Price 

 
ii 

SI* (MODERN METRIC) CONVERSION FACTORS 

APPROXIMATE CONVERSIONS TO SI UNITS

Symbol When You Know Multiply By To Find Symbol 

LENGTH 

in inches 25.4 millimeters mm 

ft feet 0.305 meters m 

yd yards 0.914 meters m 

mi miles 1.61 kilometers km 

AREA 

in2 square inches 645.2 square millimeters mm2

ft2 square feet 0.093 square meters m2

yd2 square yard 0.836 square meters m2

ac acres 0.405 hectares ha

mi2 square miles 2.59 square kilometers km2

VOLUME 

fl oz fluid ounces 29.57 milliliters mL 

gal gallons 3.785 liters L 

ft3 cubic feet 0.028 cubic meters m3 

yd3 cubic yards 0.765 cubic meters m3 

NOTE: volumes greater than 1000 L shall be shown in m3

MASS 

oz ounces 28.35 grams g

lb pounds 0.454 kilograms kg

T short tons (2000 lb) 0.907 megagrams (or "metric ton") Mg (or "t") 

TEMPERATURE (exact degrees) o

F Fahrenheit 5 (F-32)/9 Celsius oC 

or (F-32)/1.8 

ILLUMINATION 

fc foot-candles 10.76 lux lx 

fl foot-Lamberts 3.426 candela/m2 cd/m2

FORCE and PRESSURE or STRESS 

lbf poundforce 4.45 newtons N 

lbf/in2 poundforce per square inch 6.89 kilopascals kPa 

APPROXIMATE CONVERSIONS FROM SI UNITS 

Symbol When You Know Multiply By To Find Symbol 

LENGTH

mm millimeters 0.039 inches in 

m meters 3.28 feet ft 

m meters 1.09 yards yd 

km kilometers 0.621 miles mi 

AREA mm2

 square millimeters 0.0016 square inches in2 

m2 square meters 10.764 square feet ft2 

m2 square meters 1.195 square yards yd2 

ha hectares 2.47 acres ac

km2 square kilometers 0.386 square miles mi2 

VOLUME 

mL milliliters 0.034 fluid ounces fl oz 

L liters 0.264 gallons gal 

m3 cubic meters 35.314 cubic feet ft3 

m3 cubic meters 1.307 cubic yards yd3 

MASS 

g grams 0.035 ounces oz

kg kilograms 2.202 pounds lb

Mg (or "t") megagrams (or "metric ton") 1.103 short tons (2000 lb) T 

TEMPERATURE (exact degrees) o

C Celsius 1.8C+32 Fahrenheit oF 

ILLUMINATION 

lx lux 0.0929 foot-candles fc 

cd/m2 candela/m2 0.2919 foot-Lamberts fl

FORCE and PRESSURE or STRESS 

N newtons 0.225 poundforce lbf 

kPa kilopascals 0.145 poundforce per square inch lbf/in2

*SI is the symbol for th International System of Units. Approp e riate rounding should be made to comply with Section 4 of ASTM E380. 

(Revised March 2003)
iii 

Table of Contents 

PAPER 1: STATISTICAL ANALYSIS BETWEEN ROUGHNESS INDICES AND 

ROUGHNESS PREDICTION MODEL USING NEURAL NETWORKS 

ABSTRACT.....................................................................................................................................1 

INTRODUCTION ...........................................................................................................................2 

LONGITUDINAL PROFILE STATISTICS...................................................................................3 

International Roughness Index (IRI) .........................................................................................3 

Standard Deviation of Longitudinal Roughness (σ)..................................................................5 

NEURAL NETWORKS ..................................................................................................................6 

THE LTPP PROFILE DATABASE................................................................................................8 

DATA ANALYSES.........................................................................................................................9 

International Roughness Index (IRI) Versus Standard 

Deviation of Longitudinal Roughness (σ) .................................................................................9 

Comparative Analysis Between the Rejected Sections Using the IRI and σ Statistics...........11 

Prediction of Longitudinal Roughness.....................................................................................13 

CONCLUSIONS............................................................................................................................16 

ACKNOWLEDGMENTS .............................................................................................................17 

REFERENCES ..............................................................................................................................17 
iv 

PAPER 2: USE OF LTPP DATA TO VERIFY THE ACCEPTANCE LIMITS EVELOPED 

FOR PENNDOT PAVEMENT DISTRESS DATA 

ABSTRACT...................................................................................................................................21 

INTRODUCTION .........................................................................................................................22 

Background..............................................................................................................................22 

Motivation................................................................................................................................22 

LITERATURE REVIEW ..............................................................................................................23 

Types of Pavement Condition Data .........................................................................................23 

Methods of Pavement Data Collection ....................................................................................23 

Distress Variability ..................................................................................................................23 

PENNDOT QA PLAN...................................................................................................................24 

Sensitivity Analysis .................................................................................................................25 

CONTROL SECTIONS.................................................................................................................27 

Recommended Use of Control Sections ..................................................................................27 

Blind Verification Sites............................................................................................................27 

Acceptance Testing..................................................................................................................28 

Proposed Initial Values for Acceptance Criteria .....................................................................28 

Discussion of Proposed PennDOT QA Plan............................................................................30 

LTPP DATA ANALYSIS .............................................................................................................30 

LTPP Data Collection..............................................................................................................30 

LTPP Data Analysis.................................................................................................................31 

RESULTS ......................................................................................................................................36 

CONCLUSIONS AND RECOMMENDATIONS ........................................................................36 

REFERENCES ..............................................................................................................................37 

BIBILIOGRAPHY ........................................................................................................................37 
v 

PAPER 3: INVESTIGATION OF SEASONAL VARIATION IN PAVEMENT FRICTION 

USING THE DATAPAVE 3.0 DATABASE 

ABSTRACT...................................................................................................................................39 

INTRODUCTION .........................................................................................................................40 

BACKGROUND AND LITERATURE REVIEW .......................................................................40 

CONCEPTUAL MODEL..............................................................................................................42 

DATA COLLECTION AND MANAGEMENT...........................................................................43 

RESEARCH METHODOLOGY...................................................................................................45 

VISUAL ANALYSIS ....................................................................................................................46 

NUMERICAL RESULTS .............................................................................................................50 

MODEL VALIDATION ...............................................................................................................51 

CONCLUSIONS AND SUGGESTIONS......................................................................................55 

REFERENCES ..............................................................................................................................55 
vi 

PAPER 4: ASSESSMENT OF OVERLAY ROUGHNESS IN THE LTPP—A CANADIAN 

CASE STUDY 

ABSTRACT...................................................................................................................................57 

INTRODUCTION .........................................................................................................................58 

SCOPE AND OBJECTIVES.........................................................................................................59 

IMPORTANCE OF IRI AS A PAVEMENT 

INDICATOR............................................................................................................................60 

METHODOLOGY FOR ANALYSIS...........................................................................................60 

NATIONAL ROUGHNESS TREND............................................................................................60 

PROVINCIAL ROUGHNESS TREND........................................................................................61 

OVERLAY THICKNESS EFFECTS............................................................................................62 

CLIMATIC ZONE EFFECTS.......................................................................................................63 

SUBGRADE EFFECTS ................................................................................................................64 

ROUGHNESS AND C–SHRP ......................................................................................................65 

CONCLUSIONS............................................................................................................................67 

RECOMMENDATIONS...............................................................................................................68 

REFERENCES ..............................................................................................................................68 
vii 

PAPER 5: ASSIGNMENTS WITH PURPOSE: USING LTPP FOR EDUCATING 

TOMORROW’S ENGINEER 

ABSTRACT...................................................................................................................................69 

INTRODUCTION .........................................................................................................................70 

Key Issues and Questions ........................................................................................................70 

Scope and Objectives...............................................................................................................70 

BACKGROUND: UNIVERSITIES AS THE 

UPSTREAM COMPONENT ..................................................................................................71 

Supply and Demand Chain ......................................................................................................71 

Required Skill Sets for Transportation Professionals ..............................................................71 

EDUCATOR TOOLS....................................................................................................................74 

ASSIGNMENTS WITH PURPOSE..............................................................................................74 

Undergraduate Transportation Course.....................................................................................74 

Undergraduate Challenge Problems ........................................................................................77 

Undergraduate Project Courses................................................................................................79 

Senior-Level Pavement Course................................................................................................80 

Graduate-Level Pavement Course ...........................................................................................81 

CONTEXT.....................................................................................................................................84 

CONCLUSIONS............................................................................................................................85 

ACKNOWLEDGMENTS .............................................................................................................85 

REFERENCES ..............................................................................................................................85

PROGRAM ACKNOWLEDGMENTS.........................................................................................87 
viii 

List of Figures 

Paper 1

Figure 1. Quarter car simulation. .....................................................................................................3 

Figure 2. IRI thresholds adopted in different countries. ..................................................................4 

Figure 3. Roughness measurement. ................................................................................................5 

Figure 4. Three-meter-long straightedge profilometer. ...................................................................5 

Figure 5. Regression analysis between the IRI and σ....................................................................10 

Figure 6. Adopted neural network model. .....................................................................................14 

Figure 7. Learning stage. ...............................................................................................................14 

Figure 8. Validation stage..............................................................................................................15 

Figure 9. Computed-versus-target IRI values for both statistical models. ....................................16 

Paper 2

Figure 1. Recommended use of statistical sampling for acceptance testing of 

each deliverable .............................................................................................................29 

Figure 2. Within limits, total joint spalling, all regions, 1–yes, -1–no. .........................................32 

Figure 3. Within initial limits, total fatigue cracking, all regions, 1–yes, -1–no. ..........................33 

Figure 4. Percent within limits, all regions, GPS sections, JPC pavements (all severities). .........33 

Figure 5. Percent within limits, all regions, GPS sections, JPC pavements (medium 

and high severities). .......................................................................................................34 

Figure 6. Percent within limits, all regions, GPS sections, individual distresses, 

AC pavements................................................................................................................34 

Figure 7. Percent within limits, all regions, GPS sections, total distress values, 

AC pavements................................................................................................................35 

Figure 8. PWL for limits 20 percent and 40 percent, AC, all regions. ..........................................36 

Paper 3

Figure 1. Conceptual model of frictional processes.......................................................................43

Figure 2. Skid number frequency distribution. ..............................................................................45

Figure 3. Contour plot of skid number versus air temperature and 180-day snowfall. .................47 

Figure 4. Skid number versus 180-day snowfall. ..........................................................................48 

Figure 5. Skid number versus air temperature...............................................................................48 

Figure 6. Post plot of skid number versus air temperature and minimum temperature 

minus air temperature. ...................................................................................................49 

Figure 7. Contour plot of skid number versus air temperature and minimum temperature 

minus air temperature. ...................................................................................................49 

Figure 8. “Temperature alone” structural equations model. ..........................................................50 

Figure 9. “Temperature and snowfall” structural equations model. ..............................................50 
ix 

List of Figures, continued

Paper 4

Figure 1. Distribution of LTPP test sites. ......................................................................................59 

Figure 2. National IRI trends. ........................................................................................................61 

Figure 3. Ontario IRI trends...........................................................................................................62 

Figure 4. Effect of overlay thickness on roughness progression in wet-freeze climatic 

zones with fine-grained subgrades.................................................................................62 

Figure 5. Effect of climatic zone on roughness progression in thin overlays with 

coarse-grained subgrades...............................................................................................63 

Figure 6. Effect of subgrade type on roughness progression in medium overlays with 

wet-freeze climatic zones...............................................................................................65 

Figure 7. Roughness progression of thin overlays in wet-freeze climatic zones with 

fine-grained subgrades...................................................................................................66 

Figure 8. Roughness progression of medium overlays in wet-freeze climatic zones 

with fine-grained subgrades...........................................................................................67

Figure 9. roughness progression of thick overlays in wet-freeze climatic zones with 

fine-grained subgrades...................................................................................................67

Paper 5

Figure 1. Effect of overlay thickness. ............................................................................................75 

Figure 2. Effect of climatic zone....................................................................................................76 

Figure 3.Roughness trends for thin overlays (30 to 60 m) in high traffic. ..................................76 

Figure 4. Roughness trends for thin overlays in wet, low-freeze zones. .......................................77 

Figure 5. Observed roughness trends on sections in Saskatchewan. .............................................79 

Figure 6. Transverse crack analysis...............................................................................................80

Figure 7. Rut depth analysis...........................................................................................................81 

Figure 8. Needs analysis based on LTPP data. ..............................................................................81 
x 

List of Tables 

Paper 1

Table 1. Summary of database values............................................................................................. 9 

Table 2. Threshold values for σ as a function of the IRI.............................................................. 10 

Table 3. Pavement sections rejected. ............................................................................................ 12 

Table 4. Relative contribution of input parameters for the final IRI value................................... 15 

Table 5. Linear and nonlinear multivariate statistical models. ..................................................... 16 

Paper 2

Table 1. Proposed initial values for acceptance criteria and suggested actions............................ 30 

Table 2. LTPP data collection....................................................................................................... 31 

Table 3. Comparison of LTPP and PennDOT distress data.......................................................... 32 

Table 4. Initial criteria and PWL for JPC pavements all regions. ................................................ 36 

Paper 3

Table 1. “Temperature alone” structural equations model. .......................................................... 52 

Table 2. “Temperature and snowfall” structural equations model. .............................................. 53 

Table 3. Calculated skid number reductions................................................................................. 54 

Paper 4

Table 1. LTPP test site identification numbers............................................................................. 59 

Table 2. Overlay regression analysis. ........................................................................................... 63 

Table 3. Overlay thickness ANOVA analysis. ............................................................................. 63 

Table 4. Climatic zone regression analysis................................................................................... 64 

Table 5. Climatic zone ANOVA analysis..................................................................................... 64 

Table 6. Subgrade regression analysis.......................................................................................... 65 

Table 7. Subgrade ANOVA analysis............................................................................................ 65 

Paper 5

Table 1. Required skills for transportation professionals. ............................................................ 72

Table 2. Changes in average IRI for individual Provinces. .......................................................... 78 

Table 3. Comparison of predicted and observed IRI using mean values...................................... 79 

Table 4. Senior-level student ANOVA comparison. .................................................................... 80 

Table 5. Students responsible for Provinces and States................................................................ 84 
xi 

PREFACE 

BACKGROUND 

In 1998, the Federal Highway Administration (FHWA), the Long-Term Pavement Performance 

(LTPP) program, and the Transportation and Development Institute (T&DI) of the American 

Society of Civil Engineers (ASCE) initiated a program to organize an international contest on the 

use of LTPP pavement performance data. The competition was designed to promote the use of 

LTPP data and involve students, academicians, and pavement engineers in the analysis of data 

from the LTPP database. The program has been in operation for 5 years with 4 contests. The 

papers in this document are the winners of the 2003–2004 contest. 

DATA ANALYSIS CONTEST 

The contest has been expanded to cover four categories: 

Category 1—Undergraduate students (individual or team entry). 

Category 2—Graduate students (individual or team entry). 

Category 3—Partnership. 

Category 4—Curriculum. 

The contest involves participants from universities, State highway agencies, and industry. T&DI 

continues to provide oversight of the contest with strong support from FHWA. 

Experts from academia, industry, and highway agencies reviewed the 20 papers submitted for the 

2003–2004 contest and gave awards for the best paper in each category. The following are the 

winners: 

Category 1 

Prize Name Faculty Advisor 

1st Place James T. Smith Susan L. Tighe 

2nd Place Ricardo Oliveira de Souza

Silvrano Dantas Neto Márcio Muniz de Farias 

Category 2 

Prize Name Faculty Advisor 

1st Place Mark P. MacDonald Larry C. Crowley 

Rod E. Turochy 
xii 

Category 3 

Prize Name Faculty Advisor 

1st Place Venkatesa Prasanna Kumar 

Ganesan 

Shelly M. Stoffels 

Janice Arellano 

Dennis Morian 

Category 4 

Prize Name 

1st Place Susan L. Tighe 

For more information on the contest, visit the LTPP Data Analysis Contest Web site at 

http://www.tfhrc.gov/pavement/ltpp/contest.htm. 

The people photographed are (left to right) T. Paul Teng, 

FHWA; Kumares C. Sinha, ASCE/T&DI; James T. Smith, 

first place winner, category 1; Susan L. Tighe, first place 

winner, category 4; Venkatesa Prasanna Kumar Ganesan, first 

place winner, category 3; and Ricardo Oliveira de Sousa, 

second place winner, category 1. 
1 

PAPER 1: STATISTICAL ANALYSIS BETWEEN ROUGHNESS INDICES 

AND ROUGHNESS PREDICTION MODEL USING NEURAL NETWORKS 

Ricardo Oliveira de Souza 

University of Brasilia, 

Brasilia, Distrito Federal Brazil 

Silvrano Dantas Neto 

University of Brasilia, 

Brasilia, Distrito Federal Brazil 

Faculty Advisor 

Márcio Muniz de Farias 

University of Brasilia, Brasilia, 

Distrito Federal Brazil 

ABSTRACT 

This paper presents an analysis between the International Roughness Index (IRI) and the 

standard deviation of longitudinal roughness (σ), as well as a neural network study developed to 

predict the critical level of roughness. Measured longitudinal profiles available in the Long-Term 

Pavement Performance (LTPP) program database were used. A total of 207 pavement sections in 

42 States of the United States were used to do this analysis. Using a suitable software, the 

International Roughness Index (IRI) and the standard deviation of longitudinal roughness (σ) 

values were computed for every longitudinal pavement profile measured. Afterwards, these 

values were used in regression analysis and a high correlation was found between them 

(R2=0.93). Neural network analysis correlated the IRI-computed values with the type of subgrade 

soil, pavement structure (layer thickness), climate, and traffic data of 157 pavement sections. The 

neural network could forecast the IRI with an extremely high correlation factor (R2

=0.99). 

Besides, the neural network provided a sensitivity analysis indicating the relative contribution of 

factors related to the structural number (49 percent), climate (31 percent), and traffic (20 

percent). Multivariate linear and nonlinear statistic regressions were also performed to predict 

IRI, but no correlation was found. 
2 

INTRODUCTION 

Longitudinal surface roughness is defined as the deviations over the pavement surface compared 

to the designed surface grade. These deviations affect the ride quality, the vehicle dynamics, and 

the effect of dynamic loads over the surface. The difference between the theoretical surface 

heights and actual surface heights in a longitudinal profile may occur as a result of the 

construction process, road use, or in some cases a combination of both factors.(1)

The importance of longitudinal surface roughness in users’ comfort perception has been 

considered since 1960. During the American Association of State Highway Officials (AASHO) 

Road Test, it was observed that 95 percent of pavement serviceability was related exclusively to 

the deviations of surface profiles.(2) 

The longitudinal roughness is composed of waves whose wavelengths vary as a result of the 

permanent deformation of the pavement or subgrade under the action of repeated traffic loads. 

These deviations are presented as waves with intermediate length and amplitude, between 0.5 

and 50 meters (m). They are the main source of vehicle dynamic excitation and are responsible 

for the user’s feeling of discomfort. 

Statistics have been developed to represent the longitudinal roughness for measured pavement 

profiles. A statistic is a numeric value representing the surface deviations for a pavement section. 

Among them are the slope variance (SV), root-mean square of vertical accelerations (RMSVA), 

International Roughness Index (IRI), and standard deviation of longitudinal roughness (σ).(3)
3 

LONGITUDINAL PROFILE STATISTICS 

The longitudinal profiles can be represented by statistics. Some statistics, such as the slope 

variance (SV) and the standard deviation of longitudinal roughness (σ), can be applied directly to 

the measured longitudinal profile. In other cases, these statistics may apply to imaginary profiles. 

These imaginary profiles try to represent the relative movement between the rear axle and the 

vehicle body while the vehicle moves at a certain speed. The longitudinal profiles measured in 

the field are the input (or dynamic response agent). Through the use of mathematical simulations 

for the dynamic problem, imaginary profiles can be determined. 

Different simulators were developed over the years, such as the quarter car simulator, half car 

simulator, and whole car simulator. The quarter car simulator, also known as the quarter car 

system, is the most widely used.(4)

International Roughness Index (IRI) 

The IRI is a statistic index that summarizes the surface deviations for just one wheel track. This 

mathematical simulation uses the quarter car system to generate an imaginary profile. As shown 

in figure 1, the quarter car system is composed of two parts: a sprung mass representing the 

vehicle body (where the user is seated) and an unsprung mass representing the set of wheel/tire 

and half axle/suspension. The sprung mass is connected to the unsprung mass by the suspension, 

which is simulated by a damper and a spring. The sprung mass is in contact with the real 

pavement surface by another spring. 

Figure 1. Quarter car simulation.(5)

During the simulation, the quarter car system runs over the longitudinal profile, measured in the 

field at a constant speed of 80 kilometers per hour (km/h). The roughness over this surface 

induces dynamic excitation to the quarter car system, generating different vertical speeds ( zs

•

and z u

•

) or accelerations ( zs

••

 and ••

u z ) in the sprung and unsprung masses. As a result, a relative 

movement is produced between the chassis and the axle of the imaginary vehicle. The IRI value 

for a given section length (e.g., 100 m) is computed according to equation 1. 
4 

∫ = −

x/V

zs zu dt

L

IRI

0

1 & & (1) 

Where: 

IRI = International Roughness Index (in mm/m or m/km). 

L = length of the section (m). 

x = longitudinal distance (m). 

V = speed of the quarter-car model (m/s). 

x/V = time the model takes to run a certain distance x.

dt = time increment. 

s z& = vertical speed of the sprung mass. 

u z& = vertical speed of the unsprung mass. 

The IRI represents the rectified average slope, or the absolute sum of the relative vertical 

displacement experienced by the user when driving a fictitious model car over a section (L) of 

the road at a constant speed of 80 km/h. 

A perfectly smooth road results in an IRI value of 0, roads with moderate roughness give IRI 

values of around 6 meters per kilometer (m/km), and in extreme cases a very bumpy unpaved 

road can result in IRI values up to 20 m/km.(6) Maintenance intervention threshold varies 

according to the country, road type, etc. For instance, limit values of 2.7 m/km in the United 

States, 3.5 m/km in Brazil, 4.0 m/km in Chile, Uruguay, and Spain, and 6.0 m/km in Honduras 

have been reported.(7) Figure 2 illustrates these threshold differences. 

Figure 2. IRI thresholds adopted in different countries. 

0

0.9

10

USA 

From: 

AASHTO(8) 

Brazil

From: 

Pinto and 

Preussler(9) 

Spain

From: 

Río(10)

Chile

From: Patiño and Anguas(11)

Uruguay Honduras

Poor

Fair

Good

Excellent 1.5

2.7

1.9

2.7

3.5

1.5

2.5

4.0

3.0

4.0

3.2

3.9

4.6

3.5

6.0

IRI (m/km) 
5 

Standard Deviation of Longitudinal Roughness (σ) 

In Japan, the standard deviation of longitudinal roughness (σ) is used to summarize surface 

deviations. Initially, as illustrates figure 3, the relative difference height “di” (roughness) is 

measured every 1.5 m, considering an imaginary reference line.(12)

1.5m 1.5m 1.5m

hi-1 hi

hi+1

di A B

C

D Pavement 

Surface

Straightedge

(3.0m)

Reference Line

Figure 3. Roughness measurement.(12)

The surface roughness can be measured using a 3-m-long straightedge profilometer (figure 4). 

Nowadays, laser profilometers are the most used devices. The laser readings are coincident with 

the desired intervals. 

Figure 4. Three-meter-long straightedge profilometer.(13)

During the measurement, hundreds of elevations are registered, thus the heights “di” can be 

computed according to equation 2. 

 

( ) 1 1 2

1 i = i − i− + i+ d h h h (2)

Where: 

di = Registered profile heights. 

hi, hi-1, hi+1 = Surface elevations. 

According to figure 3a, after measuring the height “di” at point B, the beam is displaced to point 

D, where the heights hi, hi-1, hi+1 are measured at points B, C, and D, respectively. The reference 

line becomes the line linking points B, C, and D. Using the equation 2 once more, the height “di” 

at point C can be calculated. When a pavement section is measured continuously, a profile with 
6 

positive and negative elevations is obtained. These elevations are the relative difference height 

“di” considering the reference line every 1.5 m.(12)

The longitudinal roughness (σ) is computed through the standard deviation of “di” values, as 

shown in equation 3. 

( )

( ) 1

2 2

−

− = ∑ ∑

r r

r i i

n n

n d d

σ (3)

Where: 

σ = Standard deviation of longitudinal roughness (mm). 

di = Registered profile heights. 

nr = Number of registered data. 

The Japan Road Association recommends pavement sections 100 m long to calculate σ, whereas 

the Japan Highway Public Corporation suggests pavement sections 150 m long.(14) 

NEURAL NETWORKS 

According to Anderson, the neural network concept started with the work of McCulloch and 

Pitts in 1943.(15) They developed computational elements based on the physiological properties 

of biological neurons. Later, Widrow-Hoff developed a linear model called ADALINE 

(ADAptive LINear Element), which was then generalized for multiple layers and called 

MADALINE (Multiple ADALINE).(16) The next important development came in 1950 with the 

work of Rosenblatt, who proposed neural networks known as perceptrons. In this model, the 

network can learn when fed with examples and the responses can assume continuous values, 

whereas the original neurons of McCulloch-Pitts operated with only binary numbers.(17) 

Artificial neural networks (NN) can be understood as a computational technique that helps to 

develop nonparametric mathematical models. Different from usual statistics techniques, the 

models do not explicitly exhibit a set of fitting coefficients or parameters (although they are 

somehow embedded in the model). 

The term “neural network” is a little presumptuous and derives from the fact that earlier models 

were inspired in the neuronal structure of intelligent organisms. However, the technique is rather 

simple and easy to use. From a mathematical point of view, a neural network is a simple set of 

points, called nodes or neurons, arranged in a few consecutive layers. There is necessarily an 

input layer, one or more intermediate layers, and an output layer. The number of input and output 

neurons depends on the available data and type of problem, whereas the number of intermediate 

layers and nodes (the NN architecture) is generally a matter of empirical investigation for each 

case under study. However, there are recent developments in adaptive NN in which the network 

architecture is iteratively modified during the learning stage. 

The neurons of a given layer are generally linked to all neurons of the next layer, although some 

connections (called synapses) may be disabled and layers may be bypassed. Information is 
7 

generally processed from the input layer to the output layer in the feed-forward process. A node 

or neuron (i) of a given layer (t+1) performs very simple mathematical operations. It multiplies 

each entry Sj(t) from a neuron (j) of the previous (t) layer by some coefficient (wij) and computes 

the sum (S) of all entries thus modified. 

S = ∑w S (t) ij j (4) 

The coefficients (wij) are known as synaptic weights and store the main model characteristics 

during the learning process. 

Later, the previous sum (S) is compared to limit value (θi), known as threshold, for each layer. 

= ∑ ij j( )− i x w S t θ (5) 

The value x above will be the argument of function, f(x), known as activation function. The 

output of this function will be the entry of the neuron for the next layer. 

( ) ( ( ) ) i ij j i S t +1 = f ∑w S t −θ (6) 

Different activation functions may be tried during the development of a model. The most 

common are the step function and the sigmoid function. 

The development of an NN model involves two stages: learning and validation. For that purpose, 

a sufficiently large number of experimental data must be available. The data with known input 

and output values are divided into two sets. The first and larger set is used to train the NN and 

the validation set is used to test the generalization capacity of the trained NN. 

The learning stage consists of finding the appropriate synaptic weights (wij) to reproduce the 

desired output values. The weights are initialized randomly and the computed output values are 

compared to the desired output values. A root-mean square (RMS) error between computed and 

desired output values is calculated. Neural networks generally use a learning algorithm known as 

backpropagation of error. In the backpropagation method, the weights are recalculated from the 

output layer to the input layer to minimize the RMS error. The process is repeated during several 

iterations, until a specified error is achieved. This algorithm, also known as the generalized delta 

rule, is a modification of Widrow-Hoff ́s ADALINE, but considers nonlinear activation 

functions and the entries may assume continuous values. It was developed by Rummerlhart, 

Hinton, and Williams, and is extremely efficient in minimizing the quadratic error, RMS.(18)

Other algorithms are also available. 

Once an NN is trained, the validation data set, which was not used in the learning stage, is used 

to test the forecasting capacity of the model developed. An NN has the property of 

generalization. 
8 

Neural networks are easy to implement, robust even when treating data with some noise, and 

very efficient, especially when dealing with problems for which a specific knowledge of the 

underlying mechanisms is not totally available and when analytical formulations are too 

complicated to be obtained. The use of NN depends on the ability to adapt it to the desired 

problem by means of appropriate changes in its synaptic weights to enhance its efficiency. 

Several commercial and academic programs are available to help develop neural network 

models. Basically, the user prepares the appropriate input and output files, decides the 

appropriate NN architecture, and defines a few other analysis parameters. For the analyses in this 

paper, the authors used a multilayered neural network program called Qnet. The program uses a 

backpropagation algorithm. 

THE LTPP PROFILE DATABASE 

The profile database of the Long-Term Pavement Performance (LTPP) program was used in this 

study. This program was established in 1984 by the U.S. government as part of the Strategic 

Highway Research Program (SHRP). The LTPP program consists of two study groups: General 

Pavement Studies (GPS) and Specific Pavement Studies (SPS). The main objective of GPS is to 

study the performance of existing pavements constructed by different State departments of 

transportation in the United States. The selected roads reflect typical materials and structures 

used in the current practice design in the United States and Canada.(19) The GPS program is 

divided into 10 subgroups according to the type of pavement structure. Subgroup GPS–1 studies 

pavements made of asphalt concrete surface course over granular bases. Its database was used 

for this paper. 

The GPS program divided the United States into four main regions and studied sections 152 m 

long in all States. The LTPP database includes inventory data, climatic data, materials data, 

maintenance data, rehabilitation data, traffic data, and pavement monitoring data. 

In 1992, responsibility for the LTPP program was transferred to the Federal Highway 

Administration (FHWA), which started to analyze the collected data in 1994. As a result, the first 

major report was published in 1997. Since then the database has been updated and is available in 

a CD-ROM software called DataPave, as well as online on the FHWA Web site. 

Souza studied the DataPave database, extracting information provided by GPS–1 on about 207 

pavement sections in 42 States in the United States.(7) Pavement profiles, obtained with laser 

profilometers between 1989 and 1996, were retrieved and roughness indices (e.g., IRI and σ) 

were computed using a modified version of the University of Michigan Transportation Research 

Institute’s (UMTRI) RoadRuf computer program. Each section was surveyed at least once a year 

and in each instance the measurements were repeated five times. Souza analyzed 6,329 of these 

profiles and computed thousands of roughness indices, which were later statistically related.(7)

The neural network analysis was done by selecting 57 pavement sections from the 207 sections 

studied by Souza.(7) These sections contained all of the relevant information, such as layer 

thickness, climate, and traffic data. Table 1 shows a summary of the minimum, maximum, 

average, and standard deviation values for the thickness of surface layer course, thickness of 
9 

binder layer, thickness of base layer, thickness of subbase layer, freezing index, average annual 

precipitation, number of days with temperature above 32 o

C, age of the pavement, average daily 

volume of heavy vehicles, and computed IRI values. On average, the pavements showed a total 

asphalt layer course of 15.2 centimeters (cm) and a total granular layer of 32.2 cm. The average 

climatic conditions show rainfall precipitation of 877 millimeters (mm) per year, with a freezing 

index of 235 days and 49 days with temperature above 32 o

C. The roads were about 18.3 years 

old and submitted to an average daily volume of 675 heavy vehicles. As to the subgrade 

conditions, 2 percent of the pavement structures were laid over rock ground, 44 percent on sandy 

soils, 24 percent on clay soils, 20 percent on gravel, and 10 percent on silt soils. 

Table 1. Summary of database values. 

Surface 

Course 

(cm) 

Binder 

(cm) 

Base 

(cm) 

Subbase

(cm) 

Freeze 

(F-days)

Rain 

(mm) 

T>32 o

C

(days) 

Age 

(years) 

Traffic 

Volume 

IRI 

(m/km)

Minimum 1.0 0.0 0.0 0.0 0.0 123.0 0.0 7.0 4.0 0.62 

Maximum 23.6 36.8 65.5 108.2 1530.0 2020.0 177.0 40.0 5498.0 3.20 

Average 5.6 9.6 21.5 10.7 235.4 877.0 48.8 18.3 675.0 1.45 

Deviation 4.3 9.4 12.4 18.3 323.8 415.0 44.4 6.1 827.2 0.55 

DATA ANALYSES 

International Roughness Index (IRI) Versus Standard Deviation of 

Longitudinal Roughness (σ) 

The analysis of pavement sections was done using a modified version of the UMTRI RoadRuf 

software. RoadRuf includes several tools developed to interpret the data obtained through 

longitudinal roughness field measurements. Among these tools are the International Roughness 

Index (IRI), ride number (RN), and standard filters.(13) Two subroutines were added to the 

original algorithm to compute the standard deviation of longitudinal roughness (σ) and the slope 

variance (SV).(7)

As illustrated in figure 5, when the computed values of IRI and σ were statistically related, a 

high correlation was observed between them (R2

=0.93). The regression model developed 

(equation 7) can explain 93 percent of the σ variation values. 
10 

IRI vs SIGMA PARA TODAS AS SEÇÕES

y = 0,7652x - 0,0644

R2

 = 0,93

0

1

2

3

4

012345

IRI (m/km)

SIGMA (mm)

Figure 5. Regression analysis between the IRI and σ. 

This correlation is impressive since the IRI is computed using a dynamic simulation, whereas σ

interprets just the geometric characteristics of the pavement surface. This fact refutes the 

increasing criticisms against the use of indices based exclusively on geometric characteristics. 

σ = 0.7652 x IRI – 0.0644 R2 = 0.93 (7) 

Where: 

σ = standard deviation of longitudinal roughness (mm). 

IRI = International Roughness Index (m/km). 

In Japan, the threshold values for the standard deviation of longitudinal roughness (σ) varies 

according to the highway agencies in charge of pavement maintenance. Table 2 illustrates these 

threshold values and their respective values converted to IRI. The IRI values were computed 

using the model shown by equation 7. 

To analyze the surface condition, IRI and σ values were computed for every pavement section to 

determine the unacceptability of each pavement stretch. 

Table 2. Threshold values for σ as a function of the IRI. 

 Pavement Condition 

Highway Agency Recently built pavements 

(just after construction) 

Pavements in operation 

(maintenance, conservation) 

Japan Highway 

Public Corporation 

1.3 mm (IRI=1.8 m/km) 2.4 mm (IRI=3.2 m/km) 

Prefectures (Provinces) 3.5 mm (IRI=4.7 m/km) 4.7 mm (IRI=6.2 m/km) 

From: Takashi(20) 

IRI versus σ

σ = 0.7652 x IRI – 0.0644

R2 = 0.93 
11 

Comparative Analysis Between the Rejected Sections Using the IRI and σ

Statistics 

According to figure 1, the IRI critical value used by the American Association of State Highway 

and Transportation Officials (AASHTO) is 2.7 millimeters per kilometer (mm/km), whereas the 

critical value σ adopted by Japan Highway Public Corporation is 2.4 mm (table 2). The IRI and 

σ values computed by the software to each pavement section were compared with admissible 

values of IRI and σ. It was found that of the 207 pavement sections, just 6 presented σ values 

greater than the σ critical value. Considering the IRI, it was observed that 8 pavement sections 

presented index values greater than the critical IRI. Table 3 illustrates the pavement sections 

whose statistics computed values were greater than the admissible values. 

A preliminary comparison between these rejected sections could suggest that the IRI statistic is 

the most severe because it rejected more pavement sections when compared to the σ statistic. 

However, the higher number of rejected sections may not reflect just the index influence itself, 

but a difference of strictness between the threshold values considered admissible in the United 

States (IRI<2.7 m/km) and Japan (σ<2.4 mm). 
12 

Table 3. Pavement sections rejected. 

State Section σ≥2.4 mm IRI≥2.7 

m/km 

σ≥2.0 mm 

Alaska 1004 2.45 2.51 2.51 

Arizona 1036 2.01 2.01 

1037 2.27 2.27 

Colorado 1029 2.50 2.47 

Illinois 1003 2.40 3.16 3.16 

Kansas 1005 2.40 3.00 3.00 

Kentucky 1014 2.23 2.23 

1016 2.26 2.26 

1018 2.08 2.08 

1023 2.28 2.28 

1028 2.44 2.44 

1029 2.02 2.02 

Minnesota 

1085 2.40 3.18 3.18 

Nevada 1021 2.00 2.00 

New Jersey 1033 2.40 3.20 3.20 

New York 1643 2.07 2.07 

Pennsylvania 1597 2.70 2.65 

1048 1.99 2.00 

1049 2.70 2.67 

1056 2.01 2.01 

1065 2.16 2.16 

1096 2.33 2.33 

1111 2.14 2.14 

1178 2.43 3.12 3.12 

1181 2.39 2.39 

Texas 

1183 2.44 2.44 

Utah 1004 2.94 2.94 

Vermont 1004 2.13 2.13 

Virginia 1002 2.41 2.41 

1423 2.07 2.07 

Sum total 6 8 30 

This kind of comparison is trustworthy only if the roughness statistics are analyzed considering 

the same admissible values references. Using the regression model developed (equation 7), the σ

computed values were converted to the IRI index. The admissible IRI (IRI=2.7 m/km) is 

equivalent to a critical σ value of 2 mm. The pavement sections whose σ values are greater than 

2 mm are also included in table 3. 

Of the 207 pavement sections, just 30 sections presented σ roughness values higher than 2 mm. 

Thus, 30 sections would be rejected according to this criterion. These rejected pavement sections 

now can be compared to the sections rejected by the IRI statistic, since both use the same 

reference (IRI=2.7 m/km and σ (converted to IRI)=2 mm). Since the number of σ rejected 
13 

sections is higher than the IRI rejected sections, the σ statistic is more strict or rigorous than the 

IRI statistic. 

Using the regression model presented by equation 7, an analysis of the σ acceptance threshold 

was achieved. It was noted that the admissible (σ=2.4 mm) value is equivalent to a critical IRI of 

3.2 m/km. This shows that the acceptance threshold values are stricter in the United States. 

Prediction of Longitudinal Roughness 

Prediction of Longitudinal Roughness Using Neural Networks 

The previously described IRI data was initially analyzed using the Qnet neural network modeling 

system with the objective of finding an NN model as function of the available input parameters. 

A single output layer for IRI value was used. The input layer consisted of 10 data neurons as 

follows: 

y Input 1 (AS): thickness of the asphalt surface course layer (cm). 

y Input 2 (AB): thickness of the asphalt binder layer (cm). 

y Input 3 (Ba): thickness of the granular base layer (cm). 

y Input 4 (SB): thickness of the granular subbase layer (cm). 

y Input 5 (SG): code for the type of subgrade soil. 

y Input 6 (FD): freezing index in equivalent freezing days. 

y Input 7 (RF): average annual rainfall (mm). 

y Input 8 (HD): number of hot days with temperature above 32 oC. 

y Input 9 (TV): average traffic volume of heavy vehicles daily. 

y Input 10 (Age): age of the pavement (years). 

The subgrade soil was divided in 14 types according to the Unified Soil Classification System 

(SUCS), plus rock foundation. The following codes were attributed to input 5 (SG): Rock=0, 

SC=1, SW–SM=2, SP=3, SP–SM=4, SM=5, CL=6, CL–CM=7, GC=8, GP=9, GP–GM=10, 

GC–GM=11, GM=12, GW=13, and MH=14. 

A few neural network architectures were tried to find the best configuration for the intermediate 

layers. The best results were obtained for the configuration illustrated in figure 6, with three 

intermediate layers of eight, five, and three neurons, respectively. All neurons of a given layer 

are connected to all neurons of the subsequent layer. A sigmoid activation function was used. 
14 

AS AB Ba SB SG FD RF HD TV Age 

IRI

Figure 6. Adopted neural network model. 

A training data set comprising 140 random sections out of 157 available (89 percent) was 

initially chosen for the learning stage. The NN of figure 6 produced excellent results, as 

illustrated in figure 7, which shows the training targets (measured IRI values) in the abscissa and 

the network outputs (computed IRI values) in the ordinate. The correlation coefficient for the 

learning stage was very high with R2=0.992. The root-mean square error was RMS=0.017. 

After the training stage, the remaining 17 sections were used to validate the model. Predictions 

were also good, despite a higher dispersion than in the learning stage (figure 8). The correlation 

coefficient for the validation stage was R2

=0.80 with a standard deviation of IRI values equal to 

0.28. The lower correlation of the validation stage may be attributed to the relatively small 

number of sections available or to an overfitting during the learning stage. 

Figure 7. Learning stage. 
15 

Figure 8. Validation stage. 

An important feature of the Qnet program is that it allows quantification of the relative 

contribution of each input neuron to the computed output value. Hence, it is possible to 

investigate the most relevant factors affecting roughness in a flexible pavement. Individual 

contributions of each input are shown in table 4. In the same table, the contributions were 

grouped for the pavement structure (38.1 percent), climatic factors (31.2 percent), and traffic 

conditions (20.4 percent), besides subgrade type (10.3 percent). 

Table 4. Relative contribution of input parameters for the final IRI value. 

Input 1 2 3 4 5 6 7 8 9 10 

 SC AB Ba SB SG FD RF HD TV Age 

Contribution 

(%) 10.44 9.46 10.81 7.39 10.3 11.39 7.94 11.86 10.22 10.18 

 Structure Subgrade Climate Traffic 

Contribution 

(%) 38.1 10.3 31.2 20.4 

Prediction of Longitudinal Roughness Using Multivariate Statistical Models 

The same IRI data values were used to try to establish statistical models using SYSTAT® 

software. The user defines a priori the type of model to be tried. In this paper, multivariate linear 

and nonlinear (exponential) models were tested. The model constants and correlation coefficients 

are shown in table 5. A plot of computed-versus-target IRI values for both models is shown in 

figure 9. Dispersion is complete and no correlation at all could be found with R2

=0.15 and 

R2

=0.21 at best for the linear and nonlinear models, respectively. 
16 

Table 5. Linear and nonlinear multivariate statistical models. 

Linear 

11,944

10

1

= ∑ + I =

I I IRI A x

Nonlinear 

∑=

=

10

i 1

B x

I i i IRI A e

Input xi Ai Ai Bi 

SC 1 −0.229 1.138 −0.237 

AB 2 0.033 0.735 −0.119 

Ba 3 0.006 1.287 −27.130 

SB 4 −0.015 0.414 −0.117 

SG 5 −0.151 0.376 −0.277 

FD 6 −0.004 1.598 −0.228 

RF 7 −0.005 22.355 −0.479 

HD 8 −0.037 0.682 −0.156 

TV 9 −0.001 2.229 −0.058 

Age 10 0.019 11.390 −960.900 

y = x

0 

1 

2

3 

4 

5 

0 1 2 3 4 5 

target IRI (m/km) 

computed IRI (m/km) 

IRI - LINEAR IRI - NONLINEAR

Figure 9. Computed-versus-target IRI values for both statistical models. 

CONCLUSIONS 

The International Roughness Index (IRI) and the standard deviation of longitudinal roughness 

(σ) were correlated for 6,329 pavement longitudinal profiles measured on 207 pavement sections 

of the LTPP program. A high correlation was found between the IRI and σ statistics (R2

=0.93). 

When the IRI and σ statistics were compared under different references, it was observed that the 

IRI rejected a higher number of pavement sections. However, when the IRI and σ statistics were 

equated using the regression model developed in this study, it was found that the σ statistic 
17 

rejected more pavement sections than the IRI. Therefore, the σ statistic is stricter than the IRI 

statistic. 

Using the regression model developed in this study, it was also found that the roughness 

acceptance threshold values are stricter in the United States than in Japan. 

Neural network and multivariate statistics regression were used to try modeling the computed 

IRI values. Out of 207 pavement sections, 157 sections were selected to achieve this study. A 

complete set of subgrade, pavement structure, climate, and traffic data and IRI computed values 

for each pavement measured profile were available. 

An extremely accurate model using Qnet neural network software was developed. This NN 

model gave a coefficient of correlation of 0.992 during the learning stage. Despite a lower 

correlation during the validation stage, it is believed that this feature can be improved as more 

data become available. On the other hand, it was not possible to model the data using 

multivariate linear and nonlinear (exponential) statistics models. 

The studied database (GPS–1) comprised concrete asphalt pavements with granular base and 

subbase over most U.S. States, covering a wide range of subgrade soils and climatic and traffic 

conditions. The NN model allowed quantification of the relative contributions of these factors on 

IRI values and credited most of the pavement roughness (around 49 percent) to structural factors 

(subgrade soil and pavement layer thickness), followed by climatic factors (31 percent) and 

traffic conditions (20 percent). The most important structural factor was the overall thickness of 

the asphalt layers (including binder), responsible for almost 20 percent of the roughness index. 

However, these numbers should not be extrapolated to other countries with different engineering 

practices and less rigorous traffic weight control. 

The neural network proved to be an extremely powerful tool to predict pavement roughness. 

Similar NN models may be developed for other databases of LTPP’s GPS program to include 

other types of pavement structures. 

ACKNOWLEDGMENTS 

The authors acknowledge the financial support of Brazilian agencies Conselho Nacional de 

Desenvolvimento Científico e Tecnológico (CNPq) and Coordenação de Aperfeiçoamento de 

Pessoal de Nível Superior (CAPES). 

REFERENCES 

1. Río, R.C. (1999). “Tread quality.” Jornadas sobre la calidad en el proyecto y la construcción 

de carreteras. AEPO Ingenieros Consultores, Barcelona, Spain. (In Spanish). At 

http://www.aepo.es/aepo-old/ausc/publ/calidad.pdf. 

2. Haas, R., Hudson, W.R., and Zaniewski, J. (1994). Modern Pavement Management. 

Melbourne, FL: Krieger Publishing Co. 
18 

3. Farias, M.M., and Souza, R.O. (2002). “Longitudinal roughness and its influence on pavement 

functional evaluation.” Proceedings VII National Meeting for Highways Conservation. CDROM. Vitória, Brazil. (In Portuguese). 

4. Melis, M., and Río, R.C. (1992). “IRI computation through highway longitudinal profile. The 

IRI once more. The IRI equation for a sinusoidal profile.” Cuaderno AEPO No 1. AEPO 

Ingenieros Consultores, Madrid, Spain. (In Spanish). 

5. Shahin, M.Y. (1994). Pavement Management for Airports, Roads and Parking Lots. Norwell, 

MA: Kluwer Academic Publishers. 

6. Paterson, W.D.O. (1987). Road Deterioration and Maintenance Effects: Models for Planning 

and Management. Highway Design and Maintenance Standard Series. Baltimore, MD: The John 

Hopkins University Press. 

7. Souza, R.O. (2002). “Influence of Longitudinal Roughness on the Evaluation of Pavement.” 

M.S. Dissertation. Pubication 625.8(043) S729i. University of Brasilia, Brasilia, Brazil. (In 

Portuguese). 

8. U.S. Department of Transportation. (1999). Status of the Nation's Highways, Bridges, and 

Transit: Conditions and Performance. Report to the U.S. Congress. Washington, DC. At 

http://www.fhwa.dot.gov/policy/1999cpr/report.htm. 

9. Pinto, S., and Preussler, E. (2001). “Pavimentação Rodoviária. Conceitos fundamentais sobre 

pavimentos flexíveis.” Copiarte, Copiadora e Artes Gráficas Ltda. Rio de Janeiro, Brazil. (In 

Portuguese). 

10. Río, R.C. (1997). “Introduction to pavements sounding.” Cuaderno AEPO No. 4. AEPO 

Ingenieros Consultores, Madrid, Spain. (In Spanish). 

At http://www.aepo.es/aepo-old/ausc/publ/auscultacion.pdf. 

11. Patiño, M.C.A., and Anguas, P.G. (1998). “International Roughness Index, application to the 

Mexican highway network.” Publicación Técnica No. 108. Instituto Mexicano del Transporte, 

Sanfandila, Querétaro, Mexico. (In Spanish). 

12. Suzuki, K. (2001). Personal Communication. Japan Highway Public Corporation, Tokyo, 

Japan. 

13. Sayers, M., and Karamihas, S.M. (1996). The Little Book of Profiling: Basic Information 

about Measuring and Interpreting Road Profiles. University of Michigan Transportation 

Research Institute, Ann Arbor, MI. 

14. Japan Road Association (1989). Manual for asphalt pavement. Second Edition. Tokyo, 

Japan. 

15. Anderson, A.J. (1995). An Introduction to Neural Networks. Cambridge, MA: MIT Press. 
19 

16. Kovács, L.Z. (1997). Artificial Neural Networks: Fundamentals and Applications (20 ed.). 

Editora Collegium Cognitio e Edição Acadêmica. (In Portuguese). 

17. Fausett, L. (1994). Fundamentals of Neural Networks: Architectures, Algorithms, and 

Applications. Englewood, NJ: Prentice-Hall. 

18. Hertz, J.A., Krogh, A., and Palmer, R. G. (1991). Introduction to the Theory of Neural 

Computation. New York: Addison-Wesley Publishing Co. 

19. Federal Highway Administration. (2001). DataPaveVersion 3.0. Washington, DC. At 

http://www.datapave.com/. 

20. Takahashi, S. (2001). Personal Communication. Japan Highway Public Corporation, Tokyo, 

Japan. 

21 

PAPER 2: USE OF LTPP DATA TO VERIFY THE ACCEPTANCE LIMITS 

DEVELOPED FOR PENNDOT PAVEMENT DISTRESS DATA 

Venkatesa Prasanna Kumar Ganesan

The Pennsylvania State University 

Faculty Advisor 

Shelley M. Stoffels, D.E., P.E., 

The Pennsylvania State University 

Agency Liaison 

Janice Arellano, P.E., 

Pennsylvania Department of Transportation 

Project Consultant 

Dennis Morian, P.E., 

Conneaut Lake, PA 

ABSTRACT 

State transportation agencies use various methods of pavement data collection. The major 

methods are manual, film-based, semiautomated, and automated collection. The Federal 

Highway Administration (FHWA) Long-Term Pavement Performance (LTPP) program has used 

both the manual method and the Pavement Distress Analysis System (PADIAS) film-based 

survey for its pavement data collection.(1) The Pennsylvania Department of Transportation 

(PennDOT) replaced its former manual method with a semiautomated method. The project team 

at the Pennsylvania Transportation Institute developed a quality assurance plan for PennDOT for 

pavement data collection and rating. Initial acceptance limits were developed by the project team 

with the assistance of PennDOT. The manual distress data are compared with the PADIAS 4.2 

distress data. This paper also summarizes the PennDOT quality assurance plan. The sources of 

variability affecting surface distress are also discussed. In this paper, the LTPP distress data are 

used to verify the PennDOT acceptance limits. The findings indicate that the proposed limits 

may require modification. Two types of modifications are attempted with the LTPP data, 

providing input to PennDOT’s future decisions. 
22 

INTRODUCTION 

Background 

State transportation agencies follow various treatment or decision matrices in their pavement 

management systems. Some agencies consider individual distress and severity levels and others 

consider composite indices, such as pavement condition index and pavement distress index, for 

their treatments. The Pennsylvania Department of Transportation (PennDOT) considers 

individual distress and severity level treatments for its treatment methodology. 

The methods of data collection for pavement surface distress can be categorized as manual, filmbased, semiautomated (remote survey), and automated. Transverse profile data are collected to 

determine the rut depth. Longitudinal profile data are collected to determine pavement 

roughness. 

This paper emerged from the “Videologging—QA Plan, Development, Implementation, and 

Analysis” project (Work Order 117) performed for PennDOT. The project was conducted at the 

Pennsylvania Transportation Institute (PTI).(2) In this project, a quality assurance (QA) plan was 

developed for PennDOT to monitor the collection of quality pavement condition data. In 1997, 

PennDOT replaced the manual method with a semiautomated method of pavement condition 

data collection. At that time, the distress definitions and decisionmaking methodologies were 

significantly altered, but PennDOT did not perform sensitivity analyses. Hence, as part of 

research under Work Order 117, detailed analyses were conducted to determine the impacts of 

individual distresses for the existing pavement conditions in Pennsylvania. 

From the comprehensive literature review and the results of the sensitivity analysis, the team 

proposed the initial acceptance limits for pavement condition data. The acceptance limits are 

based on the PennDOT method of data collection. PennDOT contracts to vendors the digital 

imaging and interpretation of pavement distress. Control and acceptance will be performed with 

PennDOT’s own equipment and personnel, resulting in different equipment manufacturers, 

software, and raters. While the Long-Term Pavement Performance (LTPP) program 

methodologies differ, this paper verifies the acceptance limits by comparing the manual distress 

data with the Pavement Distress Analysis System (PADIAS 4.2) data, thus simulating the control 

of one data stream with the use of another, nonidentical distress data methodology. 

Motivation 

The project team at PTI proposed initial acceptance limits for PennDOT pavement data 

collection. The limits were set based on the experts’ opinion and comprehensive literature 

review. The results from this paper will help PennDOT refine or broaden the limits, in 

combination with the first-year application of the methodology. Success by PennDOT may 

enable other agencies to establish formal acceptance methodologies for contracted pavement 

distress data collection. 
23 

LITERATURE REVIEW 

This section documents a literature review of recent studies about the types of data, pavement 

data collection, and distress data variability. 

Types of Pavement Condition Data 

Pavement condition data include surface distress and longitudinal profile data. Surface distresses 

in asphalt concrete include fatigue cracking, transverse cracking, longitudinal cracking, edge 

deterioration, raveling and weathering, bituminous patching, and rut depth. Surface distresses in 

jointed concrete pavements include transverse joint spalling, longitudinal joint spalling, 

longitudinal cracking, transverse cracking, bituminous patching, and concrete patching. 

Longitudinal profile data are collected for both asphalt concrete (AC) and jointed concrete 

pavements (JCP) to compute a parameter known as the International Roughness Index (IRI). 

While the QA plan developed for PennDOT also includes IRI, the analysis using LTPP data in 

this paper addresses surface distress only. 

Methods of Pavement Data Collection 

The methods of data collection for pavement surface distress can be categorized as manual, filmbased, semiautomated (remote survey), and automated. For manual data collection, the pavement 

raters travel to each site and rate pavement distresses. The film-based method uses 35-millimeter 

(mm) black-and-white photography to obtain images of pavement test sections.(3) Both the 

manual and film-based methods are used in distress data collection for the Federal Highway 

Administration (FHWA) LTPP program. In a semiautomated method, the pavement images are 

collected digitally using a camera mounted on a van by a process sometimes called videologging 

and are visually rated. In a fully automated method, the images are collected and then rated 

automatically using crack detection software. Most State transportation agencies are shifting 

their methods of data collection from manual to semiautomated or automated for their networklevel pavement management systems. Primary reasons for the change include safety and 

improved possibilities for consistency and quality control. 

Distress Variability 

The variability of pavement condition data is an important factor to be considered in a pavement 

management system. Pavement condition data collected can include variability from a number of 

sources.(2) These sources include the following: 

y Method of data collection. State agencies are shifting their methods of data collection from 

manual to semiautomated or automated methods. This may affect the quality of distress data 

and the consistency of the data over time. 

y Pavement condition. Pavement condition also affects the variability of the data. An LTPP 

study showed that the variability increases as the quantity of the distress increases. 

y Repeatability (within group variability). Repeatability is another important factor in 

assessing the condition data. Repeatability is the ability of the raters to get the same results if 

they rate the same section using the same software. 
24 

y Reproducibility (between group variability). Reproducibility is the ability of different 

pools of raters to produce the same result when rating the same section using different 

software. This is an important source of variability for an agency that contracts out pavement 

data collection. 

A study of LTTP distress data variability discussed the variability that affects the pavement 

condition data.(3) In that study, statistical analyses were performed to quantify the precision and 

bias between the raters by comparing with a reference value. The manual distress survey was 

compared with the PADIAS film-based survey. LTPP sections were rated by experts, individual 

raters, and teams. Statistical analyses were performed to determine the variability of the raters. 

The following observations were made from that study: 

y No significant negative or positive bias existed between the raters (i.e., there was no 

tendency of the raters to rate consistently low or high severity). 

y The variability increased as the quantity of distress increased. 

y The variability and the bias decreased tremendously when individual severity ratings were 

converted into a pavement condition index. 

y A coefficient of variation (COV) of 30 percent is common in manual distress surveys. 

y Statistical analysis showed that the between-rater variability was greater than the within-rater 

variability. 

y For AC pavements, for total distress quantities, the average COV was the following: 

o Between-rater variability for experts, individual raters, and teams was 26.8, 33.9, 

and 22.1 percent, respectively. 

o Within-rater variability for experts, individual raters, and teams was 16.4, 28.1, 

and 16.8 percent, respectively. 

y For portland cement concrete (PCC) pavements, for total distress quantities, the average 

COV was the following: 

o Between-rater variability for experts, individual raters, and teams was 13.3, 29.4, 

and 26.3 percent, respectively. 

o Within-rater variability for experts, individual raters, and teams was 24.9, 100.1, 

and 20.0 percent, respectively. 

y There was no bias for AC pavement and PCC pavement distresses, except for fatigue 

cracking in AC and transverse joint spalling in PCC. 

y For PCC, PADIAS surveys showed greater variability than the manual surveys. 

PENNDOT QA PLAN 

In 1997, PennDOT replaced the manual method with a semiautomated method of pavement 

condition data collection in its network-level pavement management systems. PennDOT 

contracts out pavement data collection to vendors for its network-level pavement management 

systems. PennDOT currently contracts out pavement data collection to a private vendor. 

PennDOT and the vendor use different equipment and software for pavement condition data 

collection and rating. 
25 

The impacts of distress variability on pavement modeling and decisionmaking depend on the 

type of treatment methodology used by the agency. Therefore, the sensitivity analyses results 

were considered in developing the PennDOT QA plan. 

Sensitivity Analysis 

Sensitivity analyses are generally defined as statistical studies to determine the sensitivity of a 

dependent variable to variations in independent variables over reasonable ranges. There are 

several reasons for doing sensitivity analysis of the treatment matrices. First, it shows the impact 

of a distress on determination of the final treatments for a pavement. It also helps to find out 

where to concentrate resources in the collection of quality pavement condition data. The IRI 

frequency ranges that represent most of the network conditions were used in the sensitivity 

analysis. The sensitivity analysis was used for the following: 

y Identify the critical variables (distresses) so that they may be given more careful 

consideration in the quality assurance plan. 

y Identify the critical conditions (values) so that they may be given more emphasis in the 

quality assurance plan. 

y Identify areas of potential improvements in the treatment matrices. 

Sensitivity analyses were performed on PennDOT’s treatment matrices, using roadway 

management system (RMS) 2001 data for both AC and JCP pavements. These analyses were 

performed in spreadsheets, using the treatment matrices. 

Sensitivity Analysis for Asphalt Concrete Pavements 

IRI frequency distributions were plotted for AC and JPC pavements. The IRI range for 

sensitivity analysis was selected by considering both the frequency in the network condition and 

the effects of IRI on the treatments. From the treatment matrices, for interstate pavements, for the 

value IRI>21, there is a trigger or an effect in the treatments; hence the IRI 121–160 that also has 

significant frequency was selected for the sensitivity analyses. To study the effect of IRI, 

sensitivity analyses were performed for IRI 81–120 and IRI 121–160 for interstates. For National 

Highway System (NHS) noninterstate, and non-NHS pavements, sensitivity analyses were done 

for the range IRI 121–160. 

There were seven independent variables including IRI; the dependent variable for this analysis 

was the treatment result. 

Sensitivity analysis was performed by keeping the following: 

y All distress values at minimum and varying one distress at a time to maximum. 

y All distress values at average and varying one distress at a time to maximum. 

y All distress values at average and varying one distress at a time to minimum. 

The sensitivity analyses were performed for all roadway categories and conditions (minimum to 

maximum, average to maximum, and average to maximum). 
26 

The results of the sensitivity analysis for asphalt concrete pavements are the following: 

y Fatigue cracking and rut depth are the two independent variables that are most sensitive when 

the value changes from minimum to maximum and average to minimum. In the former case, 

the treatments increase and in the latter, the treatments decrease. In any case, the change in 

values seriously affects the treatment results. Hence, fatigue cracking has to be considered an 

important independent variable in the quality assurance process. 

y Transverse cracking is the second most significant distress that is sensitive to the treatments. 

y All other distresses have similar effects on treatments. 

To illustrate how the sensitivities may differ from one highway type to another, the sensitivity 

analysis were performed for interstate, NHS noninterstate, and non-NHS pavements. The 

following are the results are: 

y Transverse cracking, raveling and weathering, and rut depth have significant effects for IRI 

121–160, interstate pavements. 

y Fatigue cracking, transverse cracking, and edge deterioration have significant effects for IRI 

81–120, interstate pavements. 

y All distresses (fatigue cracking, transverse cracking, miscellaneous cracking, edge 

deterioration, raveling and weathering, and rut depth) have a similar effect on noninterstate 

and non-NHS pavements. 

Sensitivity Analysis for Jointed Concrete Pavements 

Sensitivity analysis was performed for all types of highways under different annual average daily 

traffic (AADT) conditions using the PennDOT treatment matrices (5) for jointed concrete 

pavements. Sensitivity can be observed if there is any change in the treatments by varying the 

particular distress value to maximum or minimum from the average. For jointed concrete 

pavements, IRI 81–120 has the greatest percent frequency.(7) Only the data from this IRI 81–120 

range was used for sensitivity analysis. There were eight independent variables or distresses. 

The results of the sensitivity analysis for jointed concrete pavements are the following: 

y Transverse joint spalling is the independent variable that is most sensitive when the distress 

value changes from minimum to maximum and average to minimum, irrespective of 

conditions.(7) In the former case, the treatments increase and in the latter, the treatments 

decrease. In any case, the change in values frequently affects the treatment results. Hence, 

transverse joint spalling has to be considered an important independent variable in the quality 

assurance process for pavement condition data. 

y Longitudinal joint spalling is the second most sensitive distress that affects the treatment 

results when the distress value changes from minimum to maximum and average to 

minimum. 

To illustrate how the sensitivities may differ from one highway type to another, the sensitivity 

analyses for interstate, NHS noninterstate, and non-NHS pavements were compared. As before, 
27 

the transverse joint spalling and longitudinal joint spalling distresses play a major role in 

determining the final treatments. The following are the results: 

y Transverse joint spalling and longitudinal joint spalling are the two critical distresses that are 

the most sensitive distresses for all categories (interstate, noninterstate, and non-NHS).(7)

y Transverse cracking is more sensitive for interstate and noninterstate NHS than non-NHS. 

y Rut depths, left and right, are not sensitive for interstate and non-NHS for existing 

conditions. Rut depth, right, is a little sensitive on NHS noninterstate. 

y Longitudinal cracking is not sensitive for non-NHS, and has little sensitivity for interstate 

and NHS noninterstate. 

The project team reviewed the literature, treatment results, sensitivity analyses results, and 

variability studies and developed the QA plan for PennDOT pavement data collection and rating. 

This plan was then modified for implementation by PennDOT. The following sections 

summarize the QA plan developed by the project team. 

CONTROL SECTIONS 

Recommended Use of Control Sections 

Control sections are used to determine the repeatability and reproducibility between the agency 

and vendor. The control sections should be selected so that the sections represent the network 

pavement condition distribution. The project team made the following recommendations to 

PennDOT: 

For IRI, the repeatability for a single equipment/operator combination on immediately repeated 

runs on the same day should be less than plus or minus 5 percent, 95 percent of the time. Ten 

runs for each control site should be performed. The average for each equipment/operator 

combination should be within 3 percent of the reference value. 

The repeatability should be less than plus or minus 5 percent on the extent of each rutting 

severity. Bias can be quantified using manual profiling measurements as the reference value. It is 

recommended that the bias on actual rut depth be less than plus or minus 15 percent. 

The repeatability of each vendor and PennDOT rater should be within plus or minus 10 percent 

for each distress and severity combination. The average rating by each vendor and quality 

assurance rater should be within plus or minus 15 percent of the pooled average rating. 

Blind Verification Sites 

The project team recommended the use of blind verification sites in the PennDOT QA plan. 

About 100 segments should be selected based on geographic and anticipated pavement 

condition. Multiple runs and ratings should be performed by PennDOT before the vendor’s 

production schedule. The location of the segments should not be disclosed to the vendor. The 

vendor should provide the distress ratings and IRI of each blind verification site immediately 

upon the agency’s request. PennDOT should determine if the vendor’s reported values are within 
28 

the 95 percent prediction interval from the repeated runs. The vendor should be informed 

immediately if the results are not within 95 percent confidence. Investigation and possible 

recalibration will then be necessary. The use of the blind verification sites provides timely and 

confident checks with tighter control limits, and also allows immediate corrections rather than 

ultimate rejection of entire deliverables. 

Acceptance Testing 

Five percent of segments by highway types (interstate, NHS, and non-NHS) and by anticipated 

conditions should be selected based on stratified sampling by geographical distribution and 

anticipated condition. The anticipated conditions can be determined from the previous RMS (6) 

condition data. 

A greater concentration of acceptance samples should be chosen near critical condition values. 

For example, for interstate, for both AC and JCP pavements, an IRI of 120 is the breakpoint 

between routine maintenance and major treatments. Therefore, the segments should be selected 

near these critical conditions. Figure 1 shows the recommended use of the statistical sampling 

plan for acceptance testing. 

Proposed Initial Values for Acceptance Criteria 

Table 1 shows the proposed initial values for acceptance criteria and recommended actions if 

these criteria are not met. These initial criteria and limits were taken from the literature review 

and from the suggestions of experienced members of the project team. 
29 

Figure 1. Recommended use of statistical sampling for acceptance testing of each 

deliverable.(2)

Proposed Initial Values for Acceptance Criteria 

Table 1 shows the proposed initial values for acceptance criteria and recommended actions if 

these criteria are not met. These initial criteria and limits were taken from the literature review 

and from the suggestions of experienced members of the project team. 

From the sensitivity analyses results, the project team developed three types of distress criteria 

for AC and JCP pavements. The individual distress and severity combinations are considered for 

both AC and JCP pavements. Total fatigue cracking is the sum of all severity level values (low, 

medium, and high severity). Other sensitive distresses (miscellaneous cracking, transverse 

cracking, and edge deterioration) are combined to obtain total nonfatigue cracking. For JCP 

pavements, total transverse cracking and total joint spalling (transverse and longitudinal joint 

spalling) are used as distress criteria for acceptance. 

PERCENT 

SAMPLE OF ALL 

SEGMENTS 

SINGLE RUN PRIOR 

TO VENDOR’S 

SCHEDULE 

IDENTIFY 

DISCREPANCY 

AND REJECT 

NO 

PENNDOT OR 

VENDOR 

IMAGES 

ACCEPTANCE SEGMENTS 

PENNDOT RATING 

OF SELECTED 

IMAGES 

VENDOR 

IMAGES & 

PROFILES 

SCHEDULED 

DELIVERABLE OF IRI 

AND DISTRESS 

RATINGS 

PENNDOT 

IMAGES & 

PROFILES 

SINGLE RUN 

ON 

PRODUCTION 

SCHEDULE 

PERCENT 

WITHIN 

CONTROL 

LIMITS? 

ACCEPT 

YES
30 

Discussion of Proposed PennDOT QA Plan 

Rating results and IRI values for control sections should be checked for the initial criteria 

proposed in table 1. PennDOT will review the results for control sections and decide the final 

acceptance criteria for the production sites. Increasing or decreasing the proposed limits or 

percent within limits (PWL) should be considered by reviewing the control section results. If the 

control sections results are well within the proposed limits, then PennDOT may consider 

tightening the initial values to get the desired final acceptance criteria. However, PennDOT 

should not assume that the vendors may produce the same results for production sites. Therefore, 

before tightening the control limits, PennDOT may consider 1,000 segments of production site 

results to check whether the vendors are still producing results well within limits. If the 

variability for some distress is higher than expected, careful investigations should be performed 

to check if there are any outliers. If so, retraining of raters should be conducted to eliminate the 

outliers. 

Table 1. Proposed initial values for acceptance criteria and suggested actions.(2) 

Reported Value 

Initial 

Criteria 

Percent 

Within 

Limits 

(PWL) 

Recommended Action 

if Criteria Not Met 

IRI +/- 25% 95% Reject deliverable. 

Individual Distress 

Severity Combination +/- 30% 90% 

Feedback on potential 

bias or drift in ratings. 

Retrain on definitions. 

Total Fatigue 

Cracking +/-20% 90% Reject deliverable. 

Total Nonfatigue 

Cracking +/-20% 90% Reject deliverable. 

Total Joint Spalling +/-20% 90% Reject deliverable. 

Transverse Cracking, 

JCP +/-20% 90% Reject deliverable. 

Location Reference—

Segment/Offset 

Correct 

Segment All Return deliverable for correction. 

Location Reference—

Section Begin +/- 10 feet 95% 

Return deliverable for 

correction and system 

check. 

Panoramic Images Legible 

signs 80% Report problem. Reject subsequent deliverables. 

LTPP DATA ANALYSIS 

LTPP Data Collection 

The LTPP manual distress data were extracted from the DataPave Web site.(4) Table 2 shows the 

table names descriptions. Only the distress data that represent PennDOT distress type and 
31 

severity were considered in the analysis. The survey dates for manual and PADIAS 4.2 were 

compared and only the data that were within 6 months’ difference between the two dates were 

considered. 

Table 2. LTPP data collection. 

Table Name 

IMS 

Module Description 

Mon_Dis_AC_Rev Monitoring AC Manual Distress Data 

Revised 

Mon_Dis_JPCC_Rev Monitoring JPCC Manual Distress 

Data 

Mon_Dis_PADIAS42

_AC 

Monitoring AC PADIAS 4.2 Distress 

Data 

Mon_Dis_PADIAS42

_JPCC 

Monitoring JPCC PADIAS 4.2 

Distress Data 

LTPP Data Analysis 

Manual distress data and PADIAS 4.2 data for all regions for GPS sections were extracted from 

the DataPave Web site. The LTPP distress data were categorized into different types, as shown 

in table 3. The total fatigue cracking is in length for PennDOT and it is in area for LTPP. 

PennDOT considers longitudinal cracking between wheelpaths as the only longitudinal cracking; 

LTPP considers longitudinal cracking wheelpath, nonwheelpath, sealed, and unsealed as 

longitudinal cracking. The LTPP data for analysis were selected to approximate the PennDOT 

distress types. 

The minimum and maximum manual distress values (for example, for total fatigue cracking, 

minus 20 percent, and plus 20 percent of the manual distress values) were calculated and 

compared with the PADIAS 4.2 distress values. For example, in figures 2 and 3, “1” represents 

“within limits” and “-1” represents “not within limits.” In agreement with findings that the 

variability increases as the quantity of the distress increases, only lower values meet the criteria 

proposed in table 1.(3)
32 

Table 3. Comparison of LTPP and PennDOT distress data. 

Pavement Type PennDOT Distress Type 

Corresponding LTPP 

Distress Comments 

Total fatigue cracking, in 

length: (low+medium+high) 

severity 

Total fatigue cracking, 

in area (L+M+H) 

PennDOT considers 

longitudinal 

wheelpath cracking 

as low-severity 

fatigue cracking. 

AC 

Total nonfatigue cracking: 

miscellaneous cracking 

(longitudinal cracking 

between wheelpath), 

transverse cracking, edge 

deterioration 

Longitudinal cracking 

(sealed, WP, NWP, 

reflection cracking), 

transverse cracking 

(sealed, reflection 

cracking) 

Total joint spalling: 

(transverse joint spalling and 

longitudinal joint spalling) 

 In PennDOT rating 

manual, sealed 

cracks are considered 

as low transverse 

cracking. 

Total transverse cracking Total transverse 

cracking (transverse 

cracking + transverse 

cracking sealed) 

JPC 

Total longitudinal cracking Total longitudinal 

cracking (sealed) 

-1.5

-1

-0.5

0

0.5

1

1.5

0 20 40 60 80 100 120

Mean of Manual and PADIAS42 distress values

Within Limits

Figure 2. Within limits, total joint spalling, all regions, 1–yes, -1–no. 
33 

-1.5

-1

-0.5

0

0.5

1

1.5

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300

Mean of Manual and PADIAS42 values

Within Limits

Figure 3. Within initial limits, total fatigue cracking, all regions, 1–yes, -1–no. 

Figures 4 to 7 show the PWL for all regions for both AC and JPC pavements for the initial 

criteria proposed in table 1. The PennDOT acceptance limits were developed based on the 

semiautomated method (videologging). The PADIAS 4.2 distress survey method showed 

significant variation in low-severity cracking when compared to manual surveys. There may not 

be as much variation between the two semiautomated systems used by PennDOT and its vendor, 

as both systems are of similar resolution and are more likely to distinguish similar quantities of 

low-severity cracking. Therefore, only medium and high severities were considered for this 

comparison. 

Figure 4 shows the PWL with all severities for all regions and figure 5 shows the PWL with only 

medium and high severities. From these figures, it is clear that after excluding low severity, the 

PWL has increased significantly. Because the PWL was less, the low-severity fatigue cracking 

was not included in the total fatigue cracking and checked for PWL. There was a significant 

increase in PWL after removing the low severities for all distresses. 

0

10

20

30

40

50

60

70

80

90

100

Transverse Joint

Spalling

Longitudinal

Joint Spalling

Total Joint

Spalling

Total Transverse

Cracking

Total

Longitudinal

Cracking

% PW

L

Region 1

Region 2

Region 3

Region 4

All Regions

Figure 4. Percent within limits, all regions, GPS sections, JPC pavements (all severities). 
34 

0

10

20

30

40

50

60

70

80

90

100

Transverse Joint

Spalling

Longitudinal

Joint Spalling

Total Joint

Spalling

Total Transverse

Cracking

Total

Longitudinal

Cracking

% PW

L

Region 1

Region 2

Region 3

Region 4

All Regions

Figure 5. Percent within limits, all regions, GPS sections, JPC pavements (medium and 

high severities). 

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

100.00

Total Fatigue

Cracking (M+H)

Total Fatigue

Cracking

(L+M+H)

Longitudinal

Cracking - wheel

path

Total

Transverse

Cracking

Longitudinal

Cracking - Non

wheel path

% PW

L

Region 1

Region 2

Region 3

Region 4

All Regions

Figure 6. Percent within limits, all regions, GPS sections, individual distresses, AC 

pavements. 
35 

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

100.00

Total Fatigue Cracking

(M+H)

Total Fatigue Cracking

(L+M+H)

Total Non-Fatigue Cracking

(M+H)

% PW

L

Region 1

Region 2

Region 3

Region 4

All Regions

Figure 7. Percent within limits, all regions, GPS sections, total distress values, AC 

pavements. 

From the results above, it is clear that the most of the data are not within limits. This may occur 

for PennDOT pavement data collection; on the other hand, PennDOT data variability may be less 

than for PADIAS 4.2 survey data. As the control sites and initial blind verification and 

acceptance segments are tested, PennDOT may consider increasing the initial criteria or 

decreasing the PWL or both to achieve the reasonable acceptance data. 

Therefore, the initial criteria were increased to plus or minus 40 percent as a trial. There was no 

significant increase in PWL values. This implies that when a difference occurs, it is typically a 

very large difference. Another approach would be to decrease the minimum PWL required for 

each criterion. PWL was still less than 80 percent for all distress types, except longitudinal 

cracking in jointed portland cement concrete (JPCC). This can be illustrated by figure 8 and table 

4. Figure 8 shows the PWL for AC pavements for all regions and table 4 shows the PWL and 

initial criteria for JPCC pavements for all regions. 

Therefore, an agency must consider carefully the impact on its decision methodology of having 

over 20 percent of the data variable by a high amount. The initial criteria proposed for PennDOT 

may be modified by either changing the limits or the PWL values. However, the impacts of the 

modifications on pavement management outcomes must be carefully considered. 
36 

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

100.00

Total Fatigue

Cracking (M+H)

Total Fatigue

Cracking

(L+M+H)

Total

Longitudinal

Cracking - wheel

path

Total

Transverse

Cracking

Total

Longitudinal

Cracking - NWP

% PWL 

Inital Criteria 30%

Initial Criteria 40%

Figure 8. PWL for limits 20 percent and 40 percent, 

AC, all regions. 

Table 4. Initial criteria and PWL for JPC pavements all regions. 

Initial Trial Revised Trial 

Distress Type Criteria 

(limits) PWL Criteria (limits) PWL 

Transverse Joint Spalling 30% 63% 40% 63% 

Longitudinal Joint Spalling 30% 66% 40% 67% 

Total Joint Spalling 20% 51% 40% 53% 

Total Transverse Cracking 30% 71% 40% 72% 

Total Longitudinal Cracking 30% 84% 40% 85% 

RESULTS 

y For AC pavements, no distress categories met the proposed limits (90 percent PWL) 

developed for PennDOT. 

y For JPC pavements, only total longitudinal cracking (for Region 1 and Region 2) met the 

limits specified for PennDOT. 

y Only lower distress values fall within the limits for both AC and JPCC pavements. 

y Increase in initial criteria does not significantly affect the PWL for the PADIAS 4.2 surveys, 

as controlled by the manual surveys. 

CONCLUSIONS AND RECOMMENDATIONS 

y The PADIAS 4.2 distress data does not fall within the initial limits proposed for PennDOT 

when compared with manual distress data. 

y Excluding low-severity cracking in both AC and JPCC greatly increases the PWL. 

y PennDOT may consider increasing the initial criteria or decreasing the PWL, depending on 

the results from the control sections. Based on the results of this LTPP data analysis, it is 
37 

anticipated that increasing the PWL will be a more effective approach. However, the agency 

must realize that a significant portion of the data outside the limits may lie outside the 

established criteria by a large percentage. 

REFERENCES 

1. Federal Highway Administration, Long-Term Pavement Performance Program, at 

http://www.tfhrc.gov/pavement/ltpp/ltpp.htm. 

2. Stoffels, S., Ganesan, V., Morian, D., Antle, C., and Sinha, S.K. (2003). Videologging QA—

Development, Implementation and Analysis. Pennsylvania Transportation Institute Internal 

Report. Pennsylvania Transportation Institute, University Park, PA. 

3. Rada, G.R., Wu, C.L., Bhandari, R.K., Shekharan, A.R., Elkins, G.E., and Miller, J.S. (1999). 

Study of LTPP Distress Data Variability. FHWA-RD-99-074. Federal Highway Administration, 

Washington, DC. 

4. Federal Highway Administration, DataPave Online, Long-Term Pavement Performance 

Program, at http://www.datapave.com. 

5. Pennsylvania Department of Transportation. Pennsylvania Department of Transportation 

Treatment Matrices Manual. Roadway Management Division, Bureau of Maintenance and 

Operations, Pennsylvania Department of Transportation, Harrisburg, PA. 

6. Pennsylvania Department of Transportation. Automated Pavement Condition Surveying Field 

Manual. Publication 336-2002. Roadway Management Division, Bureau of Maintenance and 

Operations, Pennsylvania Department of Transportation, Harrisburg, PA. 

7. Ganesan, V., (2003). Impacts of Pavement Condition Data Variability on State Agency 

Pavement Management Decisions, Masters Thesis, Pennsylvania State University, PA. 

BIBILIOGRAPHY 

Anderson, D.A., Luhr, D.R., and Antle, C.E. (1990). Framework for Development of 

Performance-Related Specifications for Hot-Mix Asphaltic Concrete. National Cooperative 

Highway Research Program Report 332. Transportation Research Board, Washington, DC. 

Daleiden, J.F., and Simpson, A.L. (1998). “‘Off-the-Wall’ Pavement Distress Variability Study.” 

Transportation Research Record 1643. Transportation Research Board, Washington, DC, 62–70. 

Evans, D.L., and Eltahan, A. (2000). LTPP Profile Variability. FHWA-RD-00-113. Federal 

Highway Administration, Washington, DC. 

Federal Highway Administration. (1996). Long-Term Pavement Performance Program 

Reference Guide: Version 1.0. Pavement Performance Division, Federal Highway 

Administration, Washington, DC. 
38 

Hadley, W.O. (1994). SHRP-LTTP Data Analysis Studies: Five Year Report. Strategic Highway 

Research Program, Federal Highway Administration, Washington, DC. 

Huang, H.Y. (1993). Pavement Analysis and Design. Upper Saddle River, NJ: Prentice Hall, Inc. 

Marshall, H.E. (1999). "Sensitivity Analysis." In R.C. Dorf (Ed.) The Technology Management 

Handbook (8-59 and 8-63). Boca Raton, FL: CRC Press. 

Morian, D., Stoffels, S., and Frith, D. (2002). “Quality Management of Pavement Performance 

Data.” Proceedings of the Pavement Evaluation 2002 Conference, Roanoke, VA. 

Rada, G.R., Wu, C.L., Elkins, G.E., Bhandari, R.K., and Bellinger, W.Y. (1998). “Update of 

Long-Term Pavement Performance Manual Distress Data Variability: Bias and Precision.” 

Transportation Research Record 164. Transportation Research Board, Washington, DC, 71–79. 

Stoffels, S., Morian, D., Larsen, D., and Frith, D. (2001). “Quality Analysis Methods for 

Pavement Distress Data,” Transportation Research Board Annual Meeting presentation. 

Transportation Research Board, Washington, DC. 
39 

PAPER 3: INVESTIGATION OF SEASONAL VARIATION IN PAVEMENT 

FRICTION USING THE DATAPAVE 3.0 DATABASE 

Mark P. McDonald 

Auburn University 

Albertville, AL 

Faculty Advisor: 

Larry G. Crowley 

Auburn University 

Albertville, AL 

Faculty Advisor: 

Rod E. Turochy 

Auburn University 

Albertville, AL 

ABSTRACT 

The nature of seasonal variations in highway skid resistance is investigated through use of the 

DataPave 3.0 friction data. The investigation is approached using the first principle of the 

conservation of energy. Two common theories explaining seasonal variation in skid resistance 

are considered, one stating that the seasonal variations in skid resistance occur as snowfall 

removal operations increase microtexture, which is then worn away throughout the summer. The 

other states that seasonal variations are caused by seasonal differences in pavement temperature. 

This research also demonstrates a methodology that can be adapted to analyze general 

multivariate statistical systems and specifically the DataPave pavement structural data. The study 

uses visually oriented observational study techniques to assess the validity of the hypothesized 

factor structure in the data and to express it visually. After the visual analysis, structural 

equations modeling is used to express the structure numerically. The results indicate that 

snowfall did not organize the data; however, temperature did begin to organize the data. This 

suggests a temperature effect on skid resistance and indicates that seasonal variations in 

pavement friction depend on factors not related to surface texture. 

Using the developed model, monthly adjustments for skid numbers were calculated and 

compared with those used by the Virginia Department of Transportation. This comparison 

showed significant agreement between the developed model and in-place practices. 
40 

INTRODUCTION 

An understanding of how and why pavements behave as they do is crucial in the effective 

management of highway infrastructure systems. For this reason, the Federal Highway 

Administration (FHWA) began work on the Long-Term Pavement Performance (LTPP) program 

in 1984 to examine the behavior of pavements. These experiments fall into two categories, 

General Pavement Studies (GPS) and Specific Pavement Studies (SPS). The GPS studies focus 

on the most commonly used pavement designs, while the SPS studies focus on specific pavement 

design factors.(1)

As the data collection and management phase of the LTPP program comes to a close, a long and 

difficult data analysis process begins. Unfortunately, the analysis task is very complex as the data 

result from a factorial design. The many confounding effects of the factors result in significant 

“noise” in the data and weaken the strength of correlations in the data. This investigation will 

demonstrate a methodology for exploring the database using visually oriented observational 

study techniques. 

Highway skid resistance is a very important component of traffic safety. In Alabama, 22 percent 

of all accidents and 15 percent of all fatal accidents occur on wet pavements, although 

Alabama’s highways are wet only 4 percent of the time. While the benefits associated with wetweather traction are very great, the costs associated with providing it are great as well. 

Although much money is spent to provide wet-weather traction, the nature of the mechanics of 

tire friction is still poorly characterized. A better understanding of friction mechanics would 

allow researchers to understand the nature of seasonal and short-term variations in skid 

resistance that have been observed for more than 40 years. The purpose of this research is to 

explain the nature of seasonal variation in skid resistance. 

BACKGROUND AND LITERATURE REVIEW 

The law of conservation of energy has been chosen as the focal point of this investigation 

because friction is an energy conversion process. The conservation of energy is one of the most 

fundamental truths of physics. To begin an investigation of friction from any other perspective is 

to risk the development of a theory of friction that may not be consistent with the fundamental 

laws of nature. 

Richard Feynman explains that the law of conservation of energy is a fundamental law of nature 

for which we have no known exception.(2) We do not know exactly what energy is, but we do 

know the basic forms it takes. Feynman enumerates these as gravitational potential energy, 

kinetic energy, heat, sound, radiant energy, elastic energy, chemical energy, nuclear energy, and 

mass energy. He explains that while energy can be converted from one form to another, their 

sum is constant in a closed system. 

Frictional phenomena have played a central role in the discovery of this law of energy. In the 

classic physics experiment, Count Rumford noticed that as horses did work to bore holes in 

cannon barrels, an equivalent amount of heat was generated. This experiment shows that friction 

is a process by which energy is converted to waste forms at an interface between two surfaces. A 
41 

surface with good frictional properties suggests an improved rate at which energy can be 

converted to its waste forms. 

While friction has played a central role in the discovery of the laws of energy, it is also 

interesting to note that until 40 years ago there was no attempt to explain friction mechanics in 

an energy context. Kummer was the first to attempt to explain the mechanics of the tirepavement interface in such a context.(3) He examines several theories of friction before 

presenting his unified theory. First, he examines the classical Coulomb theory of friction, which 

stipulates that friction is caused by the interlock of two surfaces. He dismisses the theory on the 

basis that the kinetic energy lost as an object is lifted over the tops of the asperities is regained 

when the object is lowered. 

After dismissing the Coulomb theory, Kummer considers the idea of an abrasive theory, in which 

energy is lost through the mechanical wear of the surfaces at the interface. In the well-known 

Fermi estimation problem, one realizes that for every revolution of a tire, a layer of atoms 

approximately one atom thick is worn. When Kummer considers the small amount of matter lost 

in abrasion, he seriously questions whether abrasion alone can account for the amount of energy 

lost in a braking maneuver. 

Next, Kummer considers the possibility of friction resulting from a static electrical attraction. He 

experimented by applying an electrical potential difference across the interface, but he found that 

the voltage had no effect on the friction coefficient. After dismissing the impact of static 

electrical forces, his attention turns to the cyclical deformation and release of the rubber. He 

proposes that there are two types of deformations occurring simultaneously. He proposes that the 

macrotexture of the pavement creates bulk material deformations that agitate the polymer chains 

in the bulk rubber of the tire. This phenomenon is termed hysteresis. He further proposes that a 

similar adhesive phenomenon exists at the interface. 

Kummer proposes that friction is the sum of adhesion and hysteresis, both of which depend 

strongly on temperature and velocity. He also proposed that adhesion comprised approximately 

80 percent of friction and proposed a mathematical model for adhesion: 

Fa = As (1)

Where: 

Fa is the adhesive force on the tire. 

A is the actual contact patch area. 

s is the effective junction strength. 

The adhesion model is important because it explains the importance of aggregate microtexture in 

the frictional process. More microtexture results in a greater contact patch area and greater 

adhesive force. 

Kummer’s observations tend to defy conventional wisdom on the topic of friction, which states 

that friction is the function of the normal weight and the ability of the surfaces to interlock. There 

are, however, some recent observations of the Tokay Gecko that also defy the conventional 

wisdom. The gecko’s toes have been shown to be able to cling to micromechanically smooth 

surfaces because of van der Waals forces generated in the millions of hairs on its toes.(4) These 

van der Waals forces produce what Rabinowicz terms surface energy, or the energy of a free 
42 

surface.(5) He explains that this phenomenon is also the cause of surface tension in water and 

causes capillary rise. 

In addition to his observations about surface energy and the adhesive mechanism, Rabinowicz 

also makes some important observations on the temperature of the surfaces in contact. He 

comments that when an object slides rapidly over a cooler substrate, much more waste energy 

will enter the substrate. This is because the sliding object keeps the same hot contact patch at the 

interface while the contact patch continually moves over fresh substrate. 

Burchett and Rizenbergs have found that seasonal variations in skid resistance exhibit an annual 

sinusoidal cycle.(6) The changes in asphalt concrete surfaces were generally greater than the 

changes in portland cement concrete (PCC). They found that the correlation between skid 

number and the combination of traffic volume and temperature was as good as the correlation 

between skid number and day of year. It is likely that Burchett and Rizenbergs observed greater 

changes in asphalt surfaces because asphalt surfaces are very nearly a black body and are much 

more sensitive to solar radiation than are PCC pavements. They are thus are less receptive to 

energy vented in heat through frictional processes in hotter weather. 

Henry has also made some important observations on seasonal and short-term changes in the 

nature of the tire-pavement interface.(7) He has observed particularly dramatic seasonal variations 

in the northern climates. He has observed that snow removal and winter weather highway 

operations cause increased microtexture in the winter and early spring. Furthermore, Henry has 

observed that summertime operations wear the aggregate and reduce the microtexture. It is 

logical to conclude that not only would these wintertime operations increase microtexture and 

actual contact area, but they would also provide fresh, high-energy surfaces. 

Henry has also observed that poor friction performance often results after a long lack of rain. 

Surface contamination would likely bind the surface energy of the aggregate and tend to 

lubricate the interface. Henry has also observed temperature-related changes in skid resistance. 

CONCEPTUAL MODEL 

As discussed earlier, friction can be conceived as a rate-based measure of energy transfer across 

an interface. Consider the path diagram in figure 1 for a general braking maneuver with a locked 

wheel. In figure 1, K1 and K2 are properties of the interface, and the model is specified by the 

equations: 

Final Mechanical Energy = K1* Initial Mechanical Energy (2)

Waste Energy Generated = K2* Initial Mechanical Energy (3)

The relationship between K1 and K2 is defined as follows: 

K1 + K2 = 1 (by conservation of energy) (4)

The relationship of K1 to K2 is governed by the properties of the interface, but for the general 

system, it is expected that K2 is highly correlated with the 64 kilometers-per-hour (km/h) (40 

miles-per-hour (mi/h)) skid number. 
43 

K2 is obviously a function of many variables, including but not limited to coarse and fine 

aggregate properties and gradations, traffic loading, pavement age, geometric conditions such as 

grade and degree of curve, surface contamination, mixture-related problems such as bleeding and 

stripping, sliding velocity, and tire properties. 

However, the variables listed above are not expected to be the primary causes of seasonal 

variations. It is expected that intense winter weather operations will increase frictional 

performance as snowplows and snow tires roughen the surface, exposing newer, cleaner surfaces 

and increasing microtexture. 

In a locked-wheel test, assuming that all kinetic energy is converted to heat at the tire-pavement 

interface, it follows that energy will be vented directly into the pavement or into the tire as 

junctions formed at the interface rupture. If the pavement is cool, heat can enter the pavement at 

a rapid rate, but if the pavement is hot, more heat must enter the tire. The damping properties of 

the rubber (particularly at the interface) will change so that less energy is dissipated in straining 

and releasing the tire, lessening the ability of the tire to accept energy throughout the remainder 

of the test. This phenomenon is observed in time plots of friction tests with initially high peak 

performance that degrades as the test continues. If there is less heat sink available in the 

pavement and in the tire, then more energy will be forced to stay in the form of vehicular kinetic 

energy. Reduced friction performance should then be observed. 

For these reasons, the investigation will explain only the effects of snow removal and other 

winter weather operations and the effects of temperature on the frictional system. 

Figure 1. Conceptual model of frictional processes. 

DATA COLLECTION AND MANAGEMENT 

The benefit of DataPave to the research effort is tremendous. It allows researchers to conduct 

investigations of their choosing without spending time, money, and effort to design and execute a 

controlled experiment. For this reason, the investigation will use the DataPave 3.0 data. 

The sections included in the study are found in the GPS–1, GPS–2, and GPS–6 studies in the 

DataPave 3.0 database because they contain only conventional and full-depth asphalt concrete 

pavement sections. Because strains are distributed much differently in an asphalt concrete 

Initial 

Mechanical 

Energy 

Final 

Mechanical 

Energy 

Waste 

Energy 

Generated 

K1 

K2 
44 

overlay of PCC pavements, overlays have been excluded from the study. PCC sections, which 

have very different surface and mechanical properties, have also been excluded. 

Because very little petrographical data existed for these sections, the data were parsed by coarse 

aggregate bulk specific gravity with hopes of excluding aggregates that do not have high silica 

content. Crowley has noticed the importance of the chain-like structure of silica tetrahedra to 

good frictional performance.(8) He speculates that the release of the adhesive bonds at the tirepavement interface not only excites the polymer chains of the tire, but also likely excites the 

silica chains in the aggregate as well. For this reason, the bulk specific gravity of the uppermost 

layer was required to be between 2.60 and 2.70, as found in the DataPave INV_PMA module. 

The parsing process may still allow for the inclusion of some nonsiliceous material in the 

sample, but should exclude pure calcite with a bulk specific gravity of 2.72 and also exclude 

dolostones with a specific gravity of approximately 2.85. These are the two most common 

nonsiliceous materials used in highway construction. 

The skid test information for the sections selected was extracted from the MON_FRICTION 

module, and latitude and longitude for each section was extracted from the INV_ID module. The 

information in the MON_FRICTION module included the skid number at the beginning and end 

of each section. Most records provided ambient air temperature. Skid tests that were not 

performed according to ASTM E274, Standard Test Method for Skid Resistance of Paved 

Surfaces Using a Full-Scale Tire, with the standard ribbed tire were excluded, as were any tests 

for which the exact day was not known. Tests not performed at 64 km/h (40 mi/h) are excluded 

from consideration. Only the skid numbers taken at the beginning of each section are used. 

The nearest National Oceanic and Atmospheric Administration weather station in operation 

during the testing time period was selected for each site. The corresponding weather data for the 

6 months before the test day were extracted from the National Climatic Data Center’s National 

Virtual Data System for each skid test. The information missing from the nearest weather station 

was either assumed on the basis of other information available or inferred from another nearby 

weather station. The variables extracted from the National Virtual Data System include 180-day 

snowfall, maximum temperature on the day of the test, and minimum temperature on the day of 

the test. 

The 78 sites selected are in 28 States. Together, 263 skid tests were used in the analysis. The 

maximum skid number reported was 74 and the minimum was 16. The 25th percentile skid 

number was 44 and the 75th percentile skid number was 51. The mean and median skid number 

was 47, with a standard deviation of 8.25. The mode was 46. The skid numbers have a 

symmetric distribution. Figure 2 shows the distribution of the skid numbers. 
45 

10 20 30 40 50 60 70 80

30

20

10

0

SN 40

Frequency

Skid Number Frequency Distribution

Figure 2. Skid number frequency distribution. 

RESEARCH METHODOLOGY 

The problem of highway skid resistance is highly multidimensional. Many factors are expected 

to contribute to the frictional performance; some of these factors are naturally correlated with 

each other. For instance, heavy snowfall is expected to occur in colder climates. For this reason, 

if one were to examine the correlation between the skid number and snowfall as well as the 

correlation between the skid number and temperature, both might be significant. But whether 

either is causal is left unresolved. Numerically, it is difficult to assess the causality of the system, 

but if one uses graphical techniques, it is possible to quickly and easily distinguish whether one 

of these effects is not causal. 

This research includes two analyses of the data compiled. First, the data were examined visually 

by use of contour plots to see which, if any, of the factors are significant. In the visual procedure, 

two plots have been used. The first plot is a post plot, which shows the location of the data points 

under consideration. The purpose of the post plot is to indicate contours that lie in an 

unpopulated region and are based on dispersed information; they are thus less stable indicators of 

underlying trends. 

The second plot is a contour plot in which the elevations of the contour lines correspond with the 

skid number. The purpose of the contour plot is to enable the researcher to discern subtleties in 

the data. A main effect of a variable is shown by the slope of the plot in the direction of its axis. 

Slope in a direction not along either axis signifies a joint effect of two variables. The researcher 

may also find points that are unusually high or low relative to their respective cohort groups. 

These points are likely outliers on axes not included in the factor pairing. The outliers are not a 

problem, but rather the most informative points. As an aid in visually identifying the trends and 

outliers, contours corresponding to skid numbers of 40 and below are colored red, and contours 
46 

corresponding to skid numbers of 50 and above are colored blue. Contours between 40 and 50 

are colored green. These techniques allow the researcher to find patterns too complex for a 

computer to find. 

After a thorough visual exploration, the data were numerically modeled using structural 

equations modeling (SEM). SEM is a tool developed by social scientists needing the ability to 

model the effects of concepts that they could not measure directly and is applicable to problems 

in scientific data analysis. SEM explains the covariance structure observed in the manifest 

variables with the use of latent variables. Latent variables are hypothetical constructs for which 

no operational methods for direct measurement exist. The purpose of SEM is to see whether the 

causal structure seen in several manifest variables can be described as the product of a few latent 

variables. SEM allows researchers to test hypotheses on causal processes.(9)

SEM involves creating a mathematical model to explain the covariance structure in the data. 

These models can be summarized with the equation: 

data = model + residual (5)

Structural equations models depend on the assumption of conditional independence, which states 

that given the values of the latent variables, the manifest variables are independent of one 

another. The assumption of conditional independence implies that the latent variables produce 

the observed relationships among the manifest variables.(9)

Structural equations models can be represented by path diagrams on which manifest variables are 

shown as rectangles and latent variables are shown as ovals. Arrows, or paths, show the 

relationships between the variables. The value associated with each path is the value of Pearson’s 

r for the correlation, which is also the standardized regression weight. Disturbance terms are 

added to the manifest variables to represent the residuals unexplained by the model. The values 

of the regression slopes and the variances of the disturbance terms can be estimated using 

software such as AmosTM. 

It is possible to test the significance of the regression parameters. In this process, Amos uses a 

bootstrap method in which the data set is sampled with replacement and the model is fit to the 

samples generated. From these samples, Amos can estimate the standard error of the regression 

slope and can return a critical ratio, which is the slope divided by its standard error. The critical 

ratio is a t-statistic for which the area of the tail of the t-distribution for the N minus p degrees of 

freedom is the probability that the regression slope is zero. In this test, N is the number of 

observations and p is the number of manifest variables. The area under the tail is commonly 

referred to as the P-value. 

In this case, Amos was required to perform an analysis with incomplete data. Incomplete cases 

were excluded in pairs, meaning that the covariance of any two variables is computed using only 

the complete pairs of data common to the two variables. 

VISUAL ANALYSIS 

The two types of factor pairings examined were the pairing of 180-day snowfall and temperature 

measures, and the pairing of ambient air temperature and deviations from temperature extremes. 
47 

The plot of air temperature and 180-day snowfall in figure 3 shows that no main effect is 

associated with the snowfall axis. A main effect, however, is associated with the temperature 

axis. The absence of the effect of snowfall is further shown by the profile of the response surface 

of figure 4. taken at 15.56 °C (60 °F). On the other hand, the presence of a temperature effect is 

shown in figure 5, a profile of the same response surface taken at 2.7 centimeters (cm) (5 inches) 

of 180-day snowfall. 

Significant organization of the data is also associated with plots of air temperature and minimum 

temperature on the day of the test minus air temperature, shown in figures 6 and 7. On these 

plots, lines of constant minimum temperature run from the upper left corner to the lower right 

corner, creating minimum temperature axes running from the lower left corner to the upper right 

corner. The plots segregate the points of poor and excellent performance along these minimum 

temperature axes. The poor performers have accumulated largely on the upper right side of the 

graphs, in the direction of higher minimum temperatures, and the excellent performers have 

accumulated largely on the lower left side. 

The visual analysis of the data reveals no evidence of seasonal variations in skid resistance 

associated with snowfall removal operations and gives evidence of the existence of an 

independent temperature effect. 

40.00 60.00 80.00 100.00

Air Temperature

0.00

10.00

20.00

30.00

40.00

50.00

180-Day Snowfall

°C = (°F − 32) × 5/9

Figure 3. Contour plot of skid number versus air temperature and 180-day snowfall. 
48 

1 inch = 2.54 cm 

Figure 4. Skid number versus 180-day snowfall. 

0

10

20

30

40

50

60

70

0 20 40 60 80 100 120

Air Temperature, F

Skid Number

°C = (°F − 32) × 5/9

Figure 5. Skid number versus air temperature. 

0

10

20

30

40

50

60

70

0 10 20 30 40 50 60

180-Day Snowfall, in

Skid Number
49 

40.00 60.00 80.00 100.00

Air Temp

-40.00

-20.00

0.00

20.00

Min Temp - Air Temp

°C = (°F − 32) × 5/9

Figure 6. Post plot of skid number versus air temperature and minimum temperature 

minus air temperature. 

40.00 60.00 80.00 100.00

Air Temp

-40.00

-20.00

0.00

20.00

Min Temp - Air Temp

°C = (°F − 32) × 5/9

Figure 7. Contour plot of skid number versus air temperature 

and minimum temperature minus air temperature. 
50 

NUMERICAL RESULTS 

The numerical exploration process involved fitting two structural equations models in light of the 

results of the visual analysis. The two structural equations models involved one latent variable 

called “energy transfer.” The manifest variables are assumed to be a reflection of this latent 

variable. The first structural equations model specified the skid number, maximum temperature, 

minimum temperature, and air temperature as linear functions of the single latent variable. The 

second model specified the same variables as the first, but included 180-day snowfall. The path 

diagrams for the structural equations models are shown in figures 8 and 9. 

Figure 8. “Temperature alone” structural 

equations model. 

Energy 

Transfer

Min Temp

Max Temp

SN 40

Snowfall

u5

u3

b4 u4

u1

b3

b1

b5

Air Temp u2

b2

Energy 

Transfer

Min Temp

Max Temp

SN 40

Snowfall

u5

u3

b4 u4

u1

b3

b1

b5

Air Temp u2

b2

Figure 9. “Temperature and snowfall” structural equations model. 

The results of the modeling process are shown in tables 1 and 2. The structural equations model 

without snowfall yielded path coefficients that were all significant beyond the 99.9 percent 

confidence level. Although the path between energy transfer and skid number is negative, the 

paths between the energy transfer and the temperature variables were all positive, indicating an 

inverse relationship between temperature and skid number. 

The model could account for 5.7 percent of the variance in skid number. This means a central 

construct is reflected in the manifest variables and is a part of a much larger factor structure. This 

construct is a measure of the ability of the pavement to accept energy in the frictional process. 

Energy 

Transfer

Min Temp

Max Temp SN 40

Air Temp

u4 u2

b3

u3

u1

b2

b1

b4 Energy 

Transfer

Min Temp

Max Temp SN 40

Air Temp

u4 u2

b3

u3

u1

b2

b1

b4
51 

The addition of the snowfall variable did not increase the correlation between the latent variable 

energy transfer and the skid number. Although all paths were significant in the model, the 

correlation between the skid number and the 180-day snowfall was 0.055 and the R-square of the 

model for the skid number was still 0.057. This means that the addition of the snowfall variable 

added no descriptive power to the model. These results reaffirm the previous observations made 

in the contour plots and previous structural equations models that snowfall does not account for 

the organization in the data. 

MODEL VALIDATION 

The Virginia Department of Transportation (VDOT) currently applies a seasonal deduction to the 

skid test measurements it takes.(10) To validate the model developed through the investigation of 

the DataPave data, monthly correction factors for Richmond, VA, have been developed and 

compared with the seasonal adjustments VDOT now uses. 

From the defining structural equations of the model in figure 9 and the results in table 1, the 

following relationship with standardized slope is derived between temperature and skid number: 

SN 40 = 0.247 (Max Temp) (6)

In unstandardized form: 

Change in SN 40 = 0.138 (Max Temp) (7)

with SN 40 in skid numbers and Max Temp in degrees Fahrenheit. 

 

Using this relationship and climate normals for Richmond, a set of seasonal deductions is 

developed.(11) These deductions and the VDOT deductions are shown in table 3. There is 

agreement between the two sets of correction factors, signifying that the model is able to 

replicate the currently used seasonal corrections. The calculated corrections are somewhat higher 

than the in-place VDOT corrections. This is likely because of the exclusion of PCC pavements 

from the data used to develop the model. 
52 

Table 1. “Temperature alone” structural equations model. 

Regression Weights 

 Estimate S.E. C.R. P Label 

Max 

Temp Å

Energy 

Transfer 14.21 0.71 20.025 0.000 b2 

SN 40 Å

Energy 

Transfer -1.965 0.515 -3.816 0.000 b4 

Air 

Temp Å

Energy 

Transfer 11.926 0.878 13.582 0.000 b1 

Min 

Temp Å

Energy 

Transfer 12.348 0.706 17.484 0.000 b3 

Standardized Regression Weights 

 Estimate 

Max 

Temp Å

Energy 

Transfer 0.964 

SN 40 Å

Energy 

Transfer -0.239 

Air 

Temp Å

Energy 

Transfer 0.768 

Min 

Temp Å

Energy 

Transfer 0.884 

Intercepts 

 Estimate S.E. C.R. P 

 Air Temp 70.909 1 70.921 0.000 

 Min Temp 51.133 0.863 59.236 0.000 

 SN 40 47.027 0.508 92.498 0.000 

 Max Temp 74.46 0.911 81.728 0.000 

Variances 

 Estimate S.E. C.R. P 

 

Energy 

Transfer 1 

 u1 98.805 10.643 9.283 0.000 

 u3 42.763 6.415 6.666 0.000 

 u4 63.861 5.599 11.406 0.000 

 u2 15.54 7.029 2.211 0.027 

Squared Multiple Correlations 

 Estimate 

 Air Temp 0.590 

 Max Temp 0.929 

 Min Temp 0.781 

 SN 40 0.057 

S.E. – Standard Error 

C.R. – Critical Ratio 

P – P-Value 
53 

Table 2. “Temperature and snowfall” structural equations model. 

Regression Weights 

 Estimate S.E C.R. P Label 

Max 

Temp Å

Energy Transfer 

 14.15 0.709 19.946 0.000 b2 

SN 40 Å Energy Transfer -1.967 0.516 -3.813 0.000 b5 

Air 

Temp Å

Energy Transfer 

11.963 0.876 13.65 0.000 b1 

Min 

Temp Å

Energy Transfer 

12.396 0.703 17.625 0.000 b3 

Snowfall Å Energy Transfer -2.321 0.629 -3.693 0.000 b4 

Standardized Regression Weights 

 Estimate 

Max 

Temp Å

Energy Transfer 

0.960 

SN 40 Å Energy Transfer -0.239 

Air 

Temp Å

Energy Transfer 

0.771 

Min 

Temp Å

Energy Transfer 

0.887 

Snowfall Å Energy Transfer -0.232 

Intercepts 

 Estimate S.E. C.R. P 

 Air Temp 70.917 1 70.94 0.000 

 Min Temp 51.133 0.863 59.236 0.000 

 SN 40 47.027 0.508 92.498 0.000 

 Max Temp 

 

74.460 0.911 81.728 0.000 

 Snowfall 3.932 0.619 6.351 0.000 

Variances 

 Estimate S.E. C.R. P 

 Energy Transfer 1 

 u1 97.878 10.55 9.278 0.000 

 u3 41.573 6.28 6.620 0.000 

 u5 63.853 5.60 11.403 0.000 

 u2 17.256 6.824 2.529 0.011 

 u4 95.008 8.33 11.406 0.000 

Squared Multiple Correlations 

 Estimate 

 Snowfall 0.054 

 Air Temp 0.594 

 Max Temp 0.921 

 Min Temp 0.787 

 SN 40 0.057 
54 

Table 3. Calculated skid number reductions. 

Month Model VDOT 

January 5.8 3.7 

February 5.2 3.7 

March 4.0 3.1 

April 2.6 1.7 

May 1.6 0.7 

June 0.5 0.3 

July 0.0 0.0 

August 0.2 0.0 

September 1.1 0.6 

October 2.5 1.7 

November 3.8 3.1 

December 5.2 3.7 
55 

CONCLUSIONS AND SUGGESTIONS 

The investigation of the friction data selected has shown that the seasonal variations in the data 

set cannot be attributed to snowfall removal and winter weather highway operations. However, it 

is likely that temperature-related effects create the seasonal variations observed in the data set. 

This result is significant because it implies that seasonal variations in pavement friction depend 

on variables not related to surface texture. The observations support prior assertions that the 

release of energy through hysteresis and adhesion is the principal cause of tire friction. 

Temperature-related effects accounted for approximately 5.7 percent of the total variance in the 

skid numbers, and these effects are statistically significant. These results imply that temperature 

effects are part of a much larger factor structure. Further investigations performed in a similar 

manner should reveal more fully the impact of factors such as aggregate size and properties, 

wear, rainfall and surface contamination, tire properties, and the amount of water present. These 

investigatory techniques can also be applied to DataPave structural data with good result. 

REFERENCES 

1. Transportation Research Board Long-Term Pavement Performance Committee. (2001). 

Fulfilling the Promise of Better Roads: A Report of the TRB Long-Term Pavement Performance 

Committee. Transportation Research Board, Washington, DC. 

2. Feynman, R. (1995). Six Easy Pieces: Essentials of Physics Explained by its Most Brilliant 

Teacher. Originally prepared for publication by Robert B. Leighton and Matthew Sands, new 

introduction by Paul Davies. Reading, MA: Helix Books. 

3. Kummer, H. (1966). Unified Theory of Rubber and Tire Friction: Engineering Research 

Bulletin B-94. Pennsylvania State University, State College, PA. 

4. Autumn, K., Sitti, M., Liang, Y., et al. (2002). “Evidence for van der Waals Adhesion in 

Gecko Setae.” Proceedings of the National Academy of Sciences, vol. 99, no.19. Washington, 

DC. 

5. Rabinowicz, E.R. (1995). Friction and Wear of Materials (2nd ed.). New York: John Wiley 

and Sons. 

6. Burchett, J. and Rizenbergs R. (1980). “Seasonal Variations in the Skid Resistance of 

Pavements in Kentucky.” Transportation Research Record, Transportation Research Board, 

Washington, DC. 788, 6–14. 

7. Henry, J., and Saito, K. (1983). “Mechanistic Model for Seasonal Variations in Skid 

Resistance.” Transportation Research Record, Transportation Research Board, Washington, DC. 

946, 29-38. 

8. Crowley, L. (2001). Skid Resistance of Asphalt Wearing Courses Made With Limestone

Aggregates. Alabama Department of Transportation, Montgomery, AL. 

9. Everitt, B. (1984). An Introduction to Latent Variable Models. London: Chapman and Hall. 
56 

10. Henry, J. (2000). National Cooperative Highway Research Program Synthesis 291: 

Evaluation of Pavement Friction Characteristics. Transportation Research Board, Washington, 

DC. 

11. National Climatic Data Center. (2002). “Monthly Station Normals of Temperature, 

Precipitation, and Heating and Cooling Degree Days 1971–2000.” Climatography of the United 

States No. 81, 44 Virginia. Asheville, NC. 
57 

PAPER 4: ASSESSMENT OF OVERLAY ROUGHNESS IN THE LTPP—A 

CANADIAN CASE STUDY 

James T. Smith, B.A.Sc. 

University of Waterloo 

Waterloo, Ontario, Canada, 

Faculty Advisor 

Susan L. Tighe, Ph.D., P.E. 

University of Waterloo 

Waterloo, Ontario, Canada 

ABSTRACT 

This paper studies asphalt pavement overlay performance in the Canadian environment. It 

investigates the impact of asphalt overlay thickness, climatic zone, and subgrade type on the 

progression of roughness as described by the International Roughness Index (IRI). Data from the 

Canadian Long-Term Pavement Performance (LTPP) program test sites were analyzed. Through 

the investigation, pavement factors that significantly impact overlay performance in the 

Canadian environment can be identified. 

Data collected over the first 13 years of study were used to show national and provincial 

roughness trends from 53 test sites. The IRI data were statistically summarized (mean, standard 

deviation) for each category by the age of the overlay section. Using the summarized data, 

regression analysis was used to determine an equation that best describes the progression of 

roughness. Two-factor analysis of variance was used to determine any significant differences 

within specific categories. The results of the regression analysis were compared to the Canadian 

Strategic Highway Research Program (C–SHRP) LTPP data to confirm the validity of the 

roughness progression equations. 

Results show that overlay thickness and climatic zones significantly impact roughness, while 

subgrade type has little influence on the IRI values. The roughness progression equations 

achieved squared correlation coefficients (R2

) between 0.93 and 0.39, demonstrating the 

accuracy of the model equations. 
58 

INTRODUCTION 

In 1987, as part of a comprehensive 20-year study of inservice pavements, the Long-Term 

Pavement Performance (LTPP) program was initiated. The purpose of this program was to 

develop an understanding of why some pavements perform better than others and how to 

maintain a cost-effective highway system.(1) The LTPP program monitors 107 test sections in 

Canada. At each site, data on distress, roughness, structural capacity, traffic, and other pavement 

performance measures were collected. 

The individual test sites are identified by a climatic zone, overlay thickness, and subgrade. 

Figure 1 and Table 1 shows the distribution and identification numbers of the 53 test sites in the 

LTPP study that have overlays and are included in the data analysis for this paper. 

The International Roughness Index (IRI) is a measurement scale to evaluate pavement 

roughness. The index is based on the result from a response-type road roughness measuring 

system (RTRRMS) to the longitudinal profile of the road surface. The profile captures the 

movement between the axle and vehicle body in response to the motion of the vehicle traveling 

down the pavement surface at 80 kilometers per hour (km/h).(2)

IRI is measured in units of meters per kilometer (m/km). An absolutely perfect pavement profile, 

one with no vertical displacements, has an IRI value equal to 0 m/km. As the index value 

increases, the smoothness of the road decreases. There is no maximum limit to the scale; 

however, IRI values greater than 8 m/km are classified as damaged pavements or rough, unpaved 

roads. At an IRI value greater than 2.15 m/km, pavements are in poor condition and become 

uncomfortable at speeds greater than 80 km/h. 
59 

Figure 1. Distribution of LTPP test sites. 

Table 1. LTPP test site identification numbers. 

BC AB SK MB ON QC NB PE NS NF 

82-1005 81-502 90-6405 83-502 87-1620 89-1021 84-1684 86-6802 

82-6006 81-503 90-6410 83-503 87-1622 89-1125 84-6804 

82-6007 81-504 90-6412 83-504 87-1680 89-1127 

 81-505 90-6420 83-505 87-1806 89-9018 

 81-506 90-6801 83-506 87-2811 89-A310 

 81-507 90-A310 83-507 87-2812 

 81-508 90-B310 83-508 87-A310 

 81-509 83-509 87-A311 

 81-1804 83-3802 87-B310 

 81-1895 83-6450 87-B311 

 81-8529 83-6451 

 83-6452 

 83-6454 

 83-A310 

SCOPE AND OBJECTIVES 

This paper analyzes the relationship between pavement performance measured by IRI and the 

age of the asphalt pavement overlay. All data used in the analysis was extracted from the LTPP 

Information Management System’s DataPave Online Release 15. 

Based on this extraction, the following analysis was carried out: 

• National and provincial roughness trends were summarized. 

3 sites 

in BC

11 sites 

in AB

7 sites 

in SK

14 sites 

in MB

10 sites 

in ON

5 sites 

in QC

2 sites 

in NB

0 sites 

in PE

1 site 

in NS

0 sites 

in NF
60 

• The effect of climatic zones (wet-freeze, wet-no freeze, and dry-freeze) on roughness 

progression was analyzed. 

• The effect of overlay thicknesses (30 to 60 millimeters (mm), 60 to 100 mm, and 100 to 185 

mm) on roughness progression was analyzed. 

• The effect of subgrade types (coarse and fine) on roughness progression was analyzed. 

• The LTPP results were compared to the Canadian Strategic Highway Research Program (C–

SHRP) LTPP study. 

Overall, the paper is directed at determining the pavement factors with the most significant 

impact on overlay performance in the Canadian environment. 

IMPORTANCE OF IRI AS A PAVEMENT INDICATOR 

Pavement roughness is the primary measure most transportation agencies use to establish the 

need for rehabilitation. Pavement roughness affects driving comfort, vehicle operating costs, and 

safety.(3) ASTM International (originally known as the American Society for Testing and 

Materials) defines pavement roughness as “the deviation of the surface from a true planar surface 

with characteristic dimensions that affect vehicle dynamic, ride quality, dynamic loads, and 

drainage.” 

METHODOLOGY FOR ANALYSIS 

Two-way analysis of variance (ANOVA) was used to compare population means based on a 

simple random sample (SRS). Each population is assumed to be normal, possibly with different 

means and the same standard deviation. ANOVA separates the total variation of the data into 

variation between group means and variation within groups. The null hypothesis (Ho) states that 

the population means are equal. The alternative hypothesis (Ha) is true if there is any difference 

between the population means. If the variation between groups is large compared to the variation 

within the groups, there is evidence against the null hypothesis.(4)

Analysis was conducted using a four-step process: 

1. Sample means and standard deviations were calculated for all groups. 

2. Plots were made to provide an overview of the data. These plots were observed for any 

extreme deviations from normal. 

3. Null and alternative hypotheses were formulated for each option. 

4. ANOVA analysis was conducted and Ho was accepted if FCalc ≤ FCrit or rejected if FCalc

> FCrit. 

NATIONAL ROUGHNESS TREND 

Figure 2 is a boxplot that shows the national roughness trend from 1989 to 2002 for all Canadian 

test site overlays in the LTPP program. For each year, six measures are used to describe 

roughness. Minimum and maximum IRI values are used to illustrate the best and worst 

performing test sections. The mean and median are also given to describe the IRI distribution. 
61 

The first and third quartiles, which capture 50 percent of the test population, are provided to 

show the divergence of IRI values from the median value. 

y = 0.9971e0.0314x

R2 = 0.9467

0.0

0.5

1.0

1.5

2.0

2.5

3.0

0 1 2 3 4 5 6 7 8 9 10 11 12 13

Overlay Age (Years)

IRI (m/km)

 Quartile 1 (25%) Minimum Median Mean

 Maximum Quartile 3 (75%) Exponential

Figure 2. National IRI trends. 

Figure 2 shows an increase in the overlay roughness during the study period. Over the period, the 

average IRI measured increased from 1.031 m/km to 1.630 m/km, representing a pavement that 

is still smooth and functioning properly. The difference between the first and third quartiles 

remained fairly constant, approximately 0.4 m/km. National roughness progression is best 

explained using an exponential regression, as shown by equation 1, and accurately predicts the 

LTPP IRI data as evidence of the squared correlation coefficient (R2) equal to 0.9467. Using an 

IRI trigger level of 2.15 m/km for maintenance, rehabilitation, and reconstruction (MR&R), the 

average lifespan for overlays in Canada is 25 years. Note that all factors have been aggregated in 

this analysis. However, the results show that overlays can provide good performance. 

 (1) 

PROVINCIAL ROUGHNESS TREND 

Figure 3 shows the boxplot roughness trends for the province of Ontario. There are 6 sites and 10 

test sections included in this analysis. 

The provincial roughness trend shows a constant increase in the average IRI value similar to the 

national roughness trend. The average IRI changed from 1.076 m/km to 1.852 m/km in 8 years. 

The difference between the first and third quartiles did not remain constant over the study period, 

varying between 0.055 m/km to 0.676. Ontario roughness progression is best explained using an 

exponential regression equation shown by equation 2. The high R2 value of 0.9195 shows that 

equation 2 accurately predicts the LTPP IRI data. An overlay in the province of Ontario has a 

useful life of approximately 12 years. This significantly lower service life compared to the 

national value could be attributed to Ontario’s high traffic loads and extreme climate. Both 

factors could result in decreased service life because of traffic- and environment-related 

distresses. 

 (2) 0.0671( ) 0.9621 Age IRI = e

(Age ) IRI e 0.0314 = 0.9971
62 

y = 0.9621e0.0671x

R2 = 0.9165

0.0

0.5

1.0

1.5

2.0

2.5

3.0

0 1 2 3 4 5 6 7 8 9 10 11 12 13

Overlay Age (Years)

IRI (m/km)

 Quartile 1 (25%) Minimum Median Mean

 Maximum Quartile 3 (75%) Exponential

Figure 3. Ontario IRI trends. 

OVERLAY THICKNESS EFFECTS 

Overlay thickness is a primary consideration for pavement designers. Figure 4 illustrates the 

effects of overlay thickness on the progression of roughness. The overlays were divided into 

three categories, thin (30 to 60 mm), medium (60 to 100 mm), and thick (100 to 185 mm). 

Overall, this resulted in six thin overlays, two medium overlays, and three thick overlays. 

During the first 8 years, the thin overlay had the greatest increase in roughness, while the 

changes for moderate and thick overlays remained almost identical. After the eighth year, the 

deterioration rate for the moderate overlay thickness was accelerated while the thick overlay 

increased gradually. Table 2 presents the results of the regression analysis and approximate 

lifespan of the overlay for all three overlay categories. 

Figure 4. Effect of overlay thickness on roughness progression 

in wet-freeze climatic zones with fine-grained subgrades. 

0.00

0.25

0.50

0.75

1.00

1.25

1.50

1.75

2.00

0 1 2 3 4 5 6 7 8 9 10 11 12 13

Overlay Age (Years)

IRI (m/km)

> 60 mm 60 < 100 mm 100 < 180 mm
63 

As the thickness of the overlay increases, the structural capacity of the pavement increases. This 

allows the pavement to resist deterioration and produce lower roughness values. The results must 

be interpreted with caution, however, because thickness cannot increase indefinitely. Correct 

compaction cannot be achieved if the overlay thickness becomes excessive, resulting in 

accelerated roughness progression due to early failure of the pavement structure. 

Table 2. Overlay regression analysis. 

Pavement Class Regression Equation R2 NOBS. Life (Years) 

Wet-freeze / Fine grained / Thin y = 0.0059x2 + 0.033x + 1.0147 0.9124 41 12 

Wet-freeze / Fine grained / Medium y = 0.0059x2 + 0.033x + 1.0147 0.9719 25 16 

Wet-freeze / Fine grained / Thick y = 0.8732e0.0429x 0.7949 37 21 

A two-factor ANOVA analysis conducted at an α level of 0.05 or 95 percent showed a 

significant change in the IRI values over time for all overlay thicknesses. The results from the 

ANOVA analysis are presented in table 3. 

Table 3. Overlay thickness ANOVA analysis. 

Overlay Thickness df FCalculated FCritical Significant 

Thin and Medium 1,6 7.74 5.99 Yes 

Thin and Thick 1,5 13.66 6.61 Yes 

Medium and Thick 1,12 12.62 4.75 Yes 

CLIMATIC ZONE EFFECTS 

The three climatic zones presented on the LTPP sites are wet-freeze (WF), wet-no freeze (WNF), 

and dry-freeze (DF). This analysis is intended to isolate the impact of climatic zone on 

performance. Overall, this resulted in 10 wet-freeze climatic zones, 2 wet-no freeze climatic 

zones, and 2 dry-freeze climatic zones. Figure 5 presents the relationship between climatic zone 

and roughness progression. 

Figure 5. Effect of climatic zone on roughness progression in 

thin overlays with coarse-grained subgrades. 

0.00

0.25

0.50

0.75

1.00

1.25

1.50

1.75

2.00

0 1 2 3 4 5 6 7 8 9 10 11 12 13

Overlay Age (Years)

IRI (m/km)

Wet-Freeze (WF) Wet-No Freeze (WNF) Dry-Freeze (DF)
64 

The roughness values varied widely from one year to another. This is possibly explained by the 

time of year the roughness data were collected. However, regardless of the time of year, dryfreeze zones exhibited the poorest performance in this study. Table 4 presents the results of the 

regression analysis and approximate lifespan of the overlay for the three climatic zone 

categories. 

Roughness progression is accelerated by freeze-thaw effects and trapped water. This effect is 

shown by the wet-no freeze zone being the best performing zone. The presence of water did not 

affect roughness progression because it does not freeze in this zone and cause additional stress to 

and deterioration of the pavement structure. 

Table 4. Climatic zone regression analysis. 

Pavement Class Regression Equation R2 NOBS. Life (Years) 

Wet-freeze / Coarse grained / Thin y = 0.0355x + 0.9319 0.7789 72 34 

Wet-no freeze / Coarse grained / Thin y = -0.0007x2 + 0.0651x + 0.7301 0.8218 17 35 

Dry-freeze / Coarse grained / Thin y = 0.0050x2 - 0.0156x + 0.9474 0.9475 22 18 

A two-factor ANOVA was performed to determine if the differences between the climatic zones 

for thin overlays on coarse-grained subgrade were statistically significant. Two-factor ANOVA 

analysis conducted at an α level of 0.05 showed a significant change in the IRI values over time 

between the wet-freeze and wet-no freeze, dry-freeze and wet-freeze climatic zones. The 

difference between the wet-no freeze and dry-freeze climatic zones is not statistically significant. 

The results from the ANOVA analysis are presented in table 5. 

Table 5. Climatic zone ANOVA analysis. 

Climatic Zone df FCalculated FCritical Significant 

Wet-freeze and Wet-no freeze 1,5 8.13 6.61 Yes 

Wet-freeze and Dry-freeze 1,5 7.19 6.61 Yes 

Wet-no freeze and Dry-freeze 1,9 0.81 5.12 No 

SUBGRADE EFFECTS 

The next analysis in this research focused on examining subgrade type on pavement 

performance. Two categories of subgrade were used, coarse and fine. Coarse-grained subgrades 

are composed of sands and gravels, whereas fine-grained subgrades are composed of silts and 

clays.(5) Overall, this resulted in two fine-grained subgrades and four coarse-grained subgrades. 

Figure 6 compares the effect that the roadway subgrade has on the progression of roughness. 
65 

0.00

0.25

0.50

0.75

1.00

1.25

1.50

1.75

2.00

0 1 2 3 4 5 6 7 8 9 10 11 12 13

Overlay Age (Years)

IRI (m/km)

Fine Grained (FG) Coarse Grained (CG)

Figure 6. Effect of subgrade type on roughness progression 

in medium overlays with wet-freeze climatic zones. 

The progression of roughness for both coarse and fine subgrades is similar. During the first 8 

years, the overlay with fine-grained subgrades preformed better than coarse-grained subgrades. 

This trend was reversed during the second half of the life cycle. Table 6 presents the results of 

the regression analysis and approximate lifespan of the overlay for the two subgrade categories. 

Table 6. Subgrade regression analysis. 

Pavement Class Regression Equation R2 NOBS. Life (Years) 

Wet-freeze / Fine grained / Medium y = 0.0054x2 - 0.0061x + 1.0273 0.9719 25 15 

Wet-freeze / Coarse grained / Medium y = 0.0050x2 - 0.0254x + 1.2357 0.9862 41 17 

Although the effect of subgrade type produced similar results for pavements in a wet-freeze 

climatic zone and with medium overlay thickness, special attention must be made to match the 

subgrade to the environmental conditions. Pavements in areas susceptible to frost should avoid 

fine-grained subgrades because of the problems associated with continuous freeze-thaw effects. 

Two-factor ANOVA analysis conducted at an α level of 0.05 showed no significant change in 

the IRI values over time when comparing coarse- and fine-grained subgrades. The results from 

the ANOVA analysis are presented in table 7. 

Table 7. Subgrade ANOVA analysis. 

Subgrade Type df FCalculated FCritical Significant 

Fine grained and Coarse 

grained 

1,12 0.87 4.75 No 

ROUGHNESS AND C–SHRP 

The Canadian Strategic Highway Research Program (C–SHRP) LTPP program began in 1989, 2 

years after the start of the LTPP program. The goal of the C–SHRP LTPP experiment was to 
66 

build on the LTPP program, focusing on inservice pavement performance of rehabilitated 

pavements over 15 years at the national and provincial levels.(6)

The roughness deterioration equations from the LTPP study were validated by comparing them 

to the roughness data taken from the C–SRHP LTPP study and the R2

 calculated.(7) 

Figure 7 presents the comparison between the two LTPP studies for thin overlays in a wet-freeze 

fine-grained subgrade. The second-degree polynomial regression equation accurately predicts the 

C–SHRP roughness deterioration with a R2

 of 0.9346. 

 (3) 

0

0.25

0.5

0.75

1

1.25

1.5

1.75

2

0 1 2 3 4 5 6 7 8 9 10 11 12 13

Overlay Age (Years)

IRI (m/km)

LTPP C-SHRP LTPP-Regression Equation

Figure 7. Roughness progression of thin overlays in wetfreeze climatic zones with fine-grained subgrades. 

Figure 8 illustrates the comparison between the two LTPP studies for moderate overlays in a 

wet-freeze fine-grained subgrade. The second-degree polynomial regression equation accurately 

predicts the C–SHRP roughness deterioration with a R2

 of 0.7676.

 (4) 

IRI = 0.0059(Age)2 + 0.033(Age) + 1.0147 

0.0054( ) 0.0061( ) 1.0273 2 IRI = Age − Age +
67 

0

0.25

0.5

0.75

1

1.25

1.5

1.75

2

0 1 2 3 4 5 6 7 8 9 10 11 12 13

Overlay Age (Years)

IRI (m/km)

LTPP C-SHRP LTPP-Regression Equation

Figure 8. Roughness progression of medium overlays 

in wet-freeze climatic zones with fine-grained subgrades. 

Figure 9 shows the comparison between the two LTPP studies for moderate overlays in a wetfreeze fine-grained subgrade. The second-degree polynomial regression equation moderately 

predicts the C–SHRP roughness deterioration with a R2 of 0.3879. 

 (5) 

0

0.25

0.5

0.75

1

1.25

1.5

1.75

2

0 1 2 3 4 5 6 7 8 9 10 11 12 13

Overlay Age (Years)

IRI (m/km)

LTPP C-SHRP LTPP-Regression Equation

Figure 9. roughness progression of thick overlays 

in wet-freeze climatic zones with fine-grained subgrades. 

CONCLUSIONS 

The LTPP experiment data represents only the first 13 years of testing. The major conclusions 

and findings to date can be summarized as follows: 

1. The progression of roughness on a national level increases steadily over time and is best 

explained using an exponential regression equation. 

2. Provincial trends follow the same trends as the national average. 

(Age ) IRI e 0.0429 = 0.8732
68 

3. Overlay thickness and climatic zones significantly affect roughness. 

a. As the thickness of the overlay increased, the pavement performance increased in 

areas characterized by wet-freeze climates and fine-grained subgrade. 

b. Wet-no freeze climatic regions had the best pavement performance, while dryfreeze regions performed the worst for test sections with a thin overlay and 

coarse-grained subgrade. 

4. Subgrade type has little influence on the IRI values for asphalt overlays. 

5. Asphalt overlays in Canada should have a lifespan of 12 to 35 years. 

6. LTPP regression equations adequately explain roughness progression for the C–SHRP sites. 

RECOMMENDATIONS 

This study gives an initial look at the performance of overlay roughness for Canadian LTPP test 

sites. Further study would be beneficial in the following areas: 

1. Continue study for the remaining study period. 

2. Create smaller subcategories to better describe the test section properties. 

3. Investigate the potential for other performance factors (overlay type, traffic level, etc.) 

influencing roughness. 

4. Develop a single equation to explain roughness progression that accounts for all performance 

factors. 

REFERENCES 

1. Federal Highway Administration. (2003). DataPave Online, Long-Term Pavement 

Performance Program, at http://www.datapave.com. 

2. Sayers, M.W., Gillespie, T.D., and Patterson, W.D. (1986). Guidelines for the Conduct and 

Calibration of Road Roughness Measurement. World Bank Technical Paper 46. The World 

Bank, Washington, DC. 

3. Transportation Association of Canada. (1997). Pavement Design and Management Guide. 

Ottawa, Canada. 

4. Moore, D., and McCabe, G. (1996). Introduction to the Practice of Statistics (2nd ed.). New 

York: W.H. Freeman & Co. 

5. Craig, R.F. (1997). Craig’s Soil Mechanics (6th ed.). New York: E & FN Spon. 

6. Tighe, S., Haas, R., and Li, N. (2001). Overlay Performance in the Canadian Strategic 

Highway Program’s LTPP Study. Transportation Research Board, Washington, DC. 

 

7. Haas, R., Li, N., and Tighe, S. (1999). Roughness Trends at C–SHRP LTPP Sites. Canadian 

Strategic Highway Research Program, Ottawa, Canada. 
69 

PAPER 5: ASSIGNMENTS WITH PURPOSE: USING LTPP FOR 

EDUCATING TOMORROW’S ENGINEER 

Susan L. Tighe, Ph.D., P.E. 

University of Waterloo 

Waterloo, Ontario, Canada 

ABSTRACT 

The overall scope of this paper involves a university perspective on how the Long-Term 

Pavement Performance (LTPP) program can be used to educate and train skilled engineers in the 

pavement sector. 

Building on a presentation at the 2003 Transportation Research Board Annual Meeting, this 

paper first presents a context for using the LTPP data. In formulating and addressing the use of 

the data, the following main points are discussed: education and training using LTPP, 

development of assignments with purpose, discussion of using LTPP to develop pavement 

research themes, and conclusions. The paper is directed primarily at academics. However, it does 

have relevance to the public and private sectors, as it directs assignments that will result in 

highly qualified people and potential leaders in the field of pavement engineering. It also 

recognizes the competing demands that face academics, so the assignments are intended to be 

straightforward and are designed for academics with limited preparation time. Overall there is a 

need to produce intelligent engineers with good problem-solving skills. Thus, the primary focus 

is to encourage independence and creativity through inquiry-based learning. 

 

In summary, the basic premise of this paper is that good design, construction, and maintenance 

of long-life pavements can be realized most effectively in education and training through 

inquiry-based learning with LTPP. 
70 

INTRODUCTION 

The broad issue of transportation education and training and the associated issues of supply and 

demand have existed for decades. A key motivating factor for training and educating future 

engineers in the transportation sector is concern about whether the transportation sector is and 

will be adequately served in terms of education and training, supply of skilled people, 

availability of resources, and future demands and commitment by both public and private 

agencies. 

Overall, the transportation sector has many dimensions. It can be viewed by modal type, public 

versus private versus academic, professionals versus technologists/technicians versus operators, 

function ranging from engineering to financial or accounting to planning to administrative or 

management, supply sources, skill sets needed, breakdown of demand, remuneration levels—and 

the list goes on. 

This paper recognizes the many dimensions of the needs of the transportation sector, while the 

focus remains on the academic perspective and the use of the Long-Term Pavement Performance 

(LTPP) program to promote transportation and more specifically to attract highly qualified 

people to the pavement sector. 

Key Issues and Questions 

The key issues related to the people side of the pavement engineering sector and the associated 

education and training needs can be categorized as follows: 

1. Supply- and demand-related issues: adequacy of supply of trained professionals, subsector 

specifics, cyclic nature and cyclic offset of supply and demand, influence of the U.S. 

economy, remuneration and demand, incentives to pursue transportation, and succession 

planning. 

2. Education-, training-, and skills-related issues: basic versus advanced training and education 

and research support, continuity of training and education, discipline choice or background, 

specific skill sets (technical and nontechnical) needed, and faculty resources. 

Scope and Objectives 

The overall scope of this paper involves a university perspective on how the LTPP program can 

be used to educate and train skilled engineers in the pavement sector. 

This paper builds on an earlier presentation at the 2003 Transportation Research Board Annual 

Meeting by first presenting a context for using the LTPP data. In formulating and addressing use 

of the data, the following main points are discussed: education and training using LTPP, 

development of assignments with purpose, discussion of using LTPP to develop pavement 

research themes, and conclusions. The paper is directed primarily at academics. However, it does 

have relevance to the public and private sectors as it directs assignments that will result in highly 

qualified people and potential leaders in the field of pavement engineering. It also recognizes the 

competing demands academics face, so the assignments are intended to be straightforward and 
71 

are designed for academics with limited preparation time. Overall there is a need to produce 

intelligent engineers with good problem-solving skills. Thus, the primary focus is to encourage 

independence and creativity through inquiry-based learning. 

In summary, the basic premise of this paper is that good design, construction, and maintenance 

of long-life pavements can be realized most effectively in education and training through 

inquiry-based learning with LTPP. 

BACKGROUND: UNIVERSITIES AS THE UPSTREAM COMPONENT 

Supply and Demand Chain 

A 1972 study noted that the major supply source for skilled professionals in the transport sector 

and thus pavement sector was civil engineering graduates.(1) Since then, the number of graduates 

has continued to be cyclic and out of phase with economic cycles and the marketplace. The 

enrollments and production of civil engineers declined by about one-third over the past decade. 

This has occurred during a generally increasing economy, a sharply increasing infrastructure 

backlog that includes roads, an aging cohort of professionals in public agencies and industry, and 

an era of downsizing, surplusing, reengineering, and strategic repositioning (all buzzwords used 

to try to soften the reality that layoffs, early retirements, and dismissals were involved) by 

transport departments. The net effect is supply and skills shortages, which already exist in a 

number of transport subsectors. Unfortunately, there is no magic tap to turn on or off in these 

situations and, even more unfortunately, the transport sector has not learned from history. 

Whether we will now enter a new and more enlightened era of human resource planning, 

including the critical component of succession planning as subsequently discussed, is at least a 

case for optimism.(2)

The decline in the production of civil engineers (the primary supply source for transportation 

professionals) has been alarming, not only in absolute numbers but also in comparison to 

computer, mechanical, and electrical engineers. It is also noteworthy that in 1972 civil 

engineering graduates composed about 21 percent of the total but now, 30 years later, that has 

shrunk to about 14 percent.(2) It is also notable that civil engineering now enjoys close to 100 

percent placement during its undergraduate programs. At the University of Waterloo, for 

example, civil engineering during the past 2 years has received work term job placements ahead 

of the other disciplines. 

Required Skill Sets for Transportation Professionals 

It is important to recognize that transportation professionals need to possess a number of skills. 

Again, the universities are the upstream component in equipping their students with the initial 

skill sets. Table 1 lists the key nontechnical, basic, and technical and special skills professionals 

require to carry out their work at both the network/systemwide level and the project/site-specific 

level. Of course, the depth of any individual skill required will vary with a number of factors, 

such as type of transportation network or project, size and complexity, environmental impacts, 

and financing. 
72 

Table 1. Required skills for transportation professionals.(3)

NON-TECHNICAL SKILLS BASIC SKILLS 

NETWORK/SYSTEM WIDE LEVEL 

Financial Planning Public Policy Business Admin. Personnel Manag. Engineering Law Public Relations Computing 

Data base design & 

manag

Statistics Math Models Engineering Economics 

Locational reference system x x 

Facilities Inventory x 

Field monitoring & other data x x x 

Data processing x x x 

Data 

Present status reports x x x 

Min. levels of service x x 

Max user costs x 

Max program costs x x x 

Needs now/future x x 

Deficiency/ 

Needs 

Deter.predictions x x x x 

Maint & rehab alternatives 

Selection Criteria x 

Eng.anal.&perf.predictions x x x x 

Life Cycle costs x x 

Alternative 

Strategies 

and 

Life Cycle 

Costing Priority analysis x x x 

Funding Levels x x 

Final Capital Program x x x 

Final Maint. Program x x x 

Construct schedule x x x 

Maint. schedule x 

Program monitoring x x x 

Priorities, 

Programs, 

Schedules 

Budget & financial planning x x x PROJECT/SITE SPECIFIC LEVEL 

Detailed site and other data x x x x 

Data Subdivision of project x x x 

Data processing x x x 

Specifications x x 

Max. project cost x x x 

Min. interruptions to service x x 

Selection criteria x 

Alternatives & Analysis 

Detailed 

Design 

Lifecycle analysis & best alt. x x 

Activities, control, records x x x x x x Construction 

Built to within specifications x 

Maint. Activities and records x x x x Maintenance 

Budget and schedule updates x x x 
73 

Table 1. Required skills for transportation professionals, continued.

(3)

TECHNICAL AND SPECIAL SKILLS 

NETWORK/SYSTEM WIDE LEVEL 

Geotechnical Geology

Building Materials Construction Material Materials Testing Field Testing Traffic Analysis Pavement Engineering

Bridge Engineering Structural EngineeringConcrete Design Construction Manag

Locational reference system x x x x 

Facilities Inventory 

Field monitoring & other data x x 

Data processing 

Data 

Present status reports 

Min. levels of service 

Max user costs 

Max program costs 

Needs now/future x x x 

Deficiency/ 

Needs 

Deter.predictions x x x x x x 

Maint & rehab alternatives x x x x x x x x x 

Selection Criteria 

Eng.anal.&perf.predictions x x x x x x x x 

Life Cycle costs x x 

Alternative 

Strategies 

and 

Life Cycle 

Costing Priority analysis x x x 

Funding Levels x x 

Final Capital Program x x 

Final Maint. Program x x 

Construct schedule x 

Maint. schedule 

Program monitoring x 

Priorities, 

Programs, 

Schedules 

Budget & financial planning x PROJECT/SITE SPECIFIC LEVEL 

Detailed site and other data x 

Data Subdivision of project x 

Data processing 

Specifications x x x x x x x x x 

Max. project cost 

Min. interruptions to service 

Selection criteria 

Alternatives & Analysis x x x x x x x x 

Detailed 

Design 

Lifecycle analysis & best alt. 

Activities, control, records x x x x Construction 

Built to within specifications x x x x x 

Maint. Activities and records Maintenance 

Budget and schedule updates x 
74 

EDUCATOR TOOLS 

In general, academics tend to rely on textbooks or course notes to assist with lecturing material. 

However, CDs, labs, computers, and various software programs can assist in the delivery of 

material. The advantage of using these latter forums is that it can provide students with exposure 

to the state of the practice. It also can enable students to work with real-world data and provide 

them with hands-on experience. In addition, with specific reference to the LTPP data, it provides 

an unlimited ability to manufacture questions. From an instructor’s point of view, it allows for 

creativity. More specifically, students can be directed to analyze data in various ways to 

reinforce basic engineering concepts. Both straightforward and challenge-type questions can be 

developed using the extensive LTPP database. Another big advantage to incorporating LTPP into 

a curriculum is that it can challenge students and, if presented properly, can plant the seed for 

graduate studies. In the author’s experience, if students are challenged it serves as a beacon for 

attracting the intelligent students to the area. Lastly, the use of LTPP data can be a marketing 

tool for pavement engineering as a profession. 

ASSIGNMENTS WITH PURPOSE 

Assignments using LTPP data in this paper are directed at the following courses in a typical civil 

engineering program: 

• Introductory undergraduate transportation course assignment problems. 

• Undergraduate “challenge” problems. 

• Undergraduate student projects. 

• Senior-level undergraduate pavement course assignment problems. 

• Graduate pavement course assignment problems. 

Undergraduate Transportation Course 

Every civil engineering program has at least one mandatory course in transportation engineering. 

Ideally, this course should be delivered in a manner that attracts students to the transportation 

sector. It is notable that at many universities the bright students in civil engineering tend to 

pursue structural engineering because they believe it is a more challenging field. This further 

reinforces the need to present transportation in a way that sparks student interest. The challenge 

with the mandatory transportation course is that it covers all of the basic concepts (i.e., traffic 

analysis, transportation planning, geometric design, pavement engineering, etc.). Consequently, 

the time spent on pavement engineering is limited. However, assignment problems can be 

developed so that students can easily carry out an analysis. For example, if the instructor 

provides the LTPP Web site address with detailed instructions as shown later in this paper and 

sets clear expectations of what needs to be done, these problems can be both challenging and 

interesting. The tasks in the problem should highlight major design features, major distresses, 

and/or data needs for design. Ultimately, these problems can also be used to provide magnitude 

checks so that once engineers graduate, they will have an appreciation for typical pavement 

thicknesses, distresses, and various other practical aspects of pavement design and management. 
75 

For example, the following assignment problems have been developed using data from the 

Canadian LTPP (C–LTPP) program. This experiment, which is complementary to the LTPP 

program, focuses on the following design factors: 

• Overlay thickness (three levels). 

• Climatic zones (four types). 

• Subgrade types (two types). 

• Traffic levels (two levels). 

• Asphalt concrete (AC) types (two types). 

In one assignment, students are asked to analyze roughness progression on the various sites, as 

shown in figures 1 through 4. Students are given the data and asked to prepare the figures and 

tables that explain performance. The problems presented are built on a study carried on in 

2001.(4) Students are asked to comment on what the trends mean. For example, figure 1 compares 

the thickness effects on roughness progression among the three levels of overlay thickness. This 

is for the two levels of traffic, the two types of subgrade (fine and coarse), and the three types of 

climatic zones combined. It is clear that the thinner overlays are deteriorating at a significantly 

higher rate than the medium and thick overlays. The highest International Roughness Index (IRI) 

values after 8 years, about 1.8 meters per kilometer (m/km), occur for the lowest overlay 

thickness level of 30 to 60 millimeters (mm) for both high and low traffic, while the lowest IRIs 

after 8 years, about 1.4 m/km, occur for the highest overlay thickness level of 100 to 185 mm. 

However, the average IRI difference between the medium and high thickness overlays after 8 

years is only 0.1 m/km. The students should provide a recommendation on the need for 

continued observations so that future trends can also be monitored. 

0.80

1.00

1.20

1.40

1.60

1.80

2.00

30-60 mm 60-100 mm 100-185 mm

IRI (m/km)

1990 1994 1998

Figure 1. Effect of overlay thickness. 

Figure 2 presents the overall trends of roughness progression in the three climatic zones (all 

levels of overlay thickness, traffic level, subgrade type, and overlay material combined). It 

reveals that a relatively higher rate of roughness progression takes place in wet, low-freeze 

zones. Roughness trends for pavements in dry, high-freeze zones are relatively flat. 
76 

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

2.2

We t, Low Fre e ze Zone s We t, High Fre e ze Zone s D ry, High Fre e ze Zone s

IRI (m /k

m

1990 1994 1998

Figure 2. Effect of climatic zone. 

It is apparent that the pavements with thin overlays (figure 3) deteriorate the fastest in the 

wet, low-freeze zones. However, the thickness effect on roughness is substantially 

reduced in dry, high-freeze zones. For pavements in wet, high-freeze zones, roughness 

levels are intermediate between the other two zones. 

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

Wet,Low Freeze Zones Wet, High Freeze Zones Dry, High Freeze Zones

IRI (m/km)

1990 1994 1998

Figure 3. Roughness trends for thin overlays 

(30 to 60 mm) in high traffic. 

Further explanation in the student’s response should elaborate on the trends. More 

specifically, the average IRI of the thin overlays in wet, low-freeze zones increased from 

about 1.3 m/km (as-built in 1990) to 2.1 m/km (observed in 1998), for a net IRI increase 

of 0.8 m/km in 8 years. On the other hand, there is no increase in average IRI for these 

low-thickness overlays in dry, high-freeze zones. The average IRI of the pavements in 

wet, high-freeze zones increased from about 1.1 m/km (as-built in 1990) to about 

1.6 m/km (observed in 1998), for a net IRI increase of about 0.5 m/km in 8 years. 

Another assignment problem examines the subgrade influences on pavement 

performance. According to the database codes of the Canadian Strategic Highway 

Research Program (C–SHRP), subgrade soils are classified into two categories: fine 

grained and coarse grained. The coarse-grained subgrade soils include sands and gravels, 

while the fine-grained subgrade soils are composed mainly of silts and clays. Analyzing 
77 

the factor effects on roughness trends under various traffic, thickness, and climate 

conditions has shown that pavement deterioration is influenced to a considerable extent 

by the type of subgrade soil on which the pavement is built. Generally, pavements on 

fine-grained subgrade soils will deteriorate significantly faster than pavements on coarsegrained subgrade soils in terms of average IRI progression in the same time period. This 

factor effect is significant when examining thin overlays. 

Figure 4 shows the roughness trends for thin overlays (30 to 60 mm) in wet, low-freeze 

zones. The effect of fine subgrade for either level of traffic is quite apparent. This might 

be expected because in this type of climatic zone with more moisture and freeze-thaw 

cycles and a lower depth of frost penetration, a thin overlay on a fine subgrade should 

deteriorate more rapidly. Similarly, at the high traffic level, pavements with fine-grained 

subgrade soils increased their average IRI by 2.5 times, compared with the pavements 

with coarse subgrade soils. On the other hand, the coarse subgrade sections showed 

comparatively less deterioration, but the rate of deterioration would still suggest 

(recognizing the risk of extrapolation) that even in this case a relatively short overlay life 

might be expected. 

Overall, these questions would be directed in such a way that students could analyze data 

and comment on what the trends mean. 

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80

2.00

2.20

2.40

2.60

2.80

1990 1994 1998

IRI (m/km)

Fine Subgrade

(LT)

Coarse

Subgrade (LT)

Fine Subgrade

(HT)

Coarse

Subgrade (HT)

Figure 4. Roughness trends for thin overlays in wet, low-freeze zones. 

Undergraduate Challenge Problems 

Challenge problems also can be easily developed by using the LTPP data. These problems would 

be directed specifically at the top students. They involve analysis of data and must be 

accompanied with design recommendations and the use of statistical analysis. The students 

would use the data and be asked to articulate linkages to design factors. The advantage of using 

LTPP is that it enables the instructor to develop unlimited questions and unique problems aimed 

specifically at the top students in the course with the intention of planting a seed for graduate 

studies. For example, 2 of 87 students correctly answered an LTPP problem developed in 2002. 

It involved data analysis that required students to isolate design factors and come up with the 
78 

best pavement design for various climates, subgrades, pavement thicknesses, and traffic levels. It 

is interesting to note that the students had a very positive reaction despite the challenge. 

An example of a challenge problem assignment is shown in table 2. It uses roughness data from 

the C–LTPP study. In this case, students are asked to determine a regression relationship 

between roughness progression versus time for all of the Canadian Provinces over a given time 

period. Table 2 summarizes the roughness trends over 8 years for the individual Provinces. It 

appears that climate has an influence on roughness progression in terms of the amount of IRI 

increase. Among the 24 test sites or 65 sections, all in Quebec and two in Ontario are classified 

as being in wet, high-freeze zones; all in the three prairie Provinces (Alberta, Manitoba, and 

Saskatchewan) are in dry, high-freeze zones; the rest are in wet, low-freeze zones. 

Table 2. Changes in average IRI for individual Provinces. 

IRI As-built in 

1989/1990 

Location IRI in 1997 IRI in 1998 Δ IRI Δ IRI 

and 

Climatic 

Zone 

Q1

(2) Q3 (3) Mean Q1(2) Q3 (3) Mean Q1(2) Q3 (3) Mean In 7 

Yrs. 

In 8 

Yrs. 

AB (III) 1.148 1.281 1.220 1.240 1.386 1.344 — — — 0.120 — 

B C ( I ) 0.880 1.219 1.056 1.170 1.229 1.250 — — — 0.190 — 

MB (III) 0.876 1.262 1.103 1.264 1.436 1.336 0.976 1.180 1.179 0.233 0.076 

N B ( I ) 1.187 1.389 1.270 1.442 1.729 1.652 1.238 1.749 1.541 0.382 0.271 

N F ( I ) 0.930 1.531 1.231 1.134 1.544 1.320 1.044 1.566 1.350 0.089 0.119 

N S ( I ) 1.367 1.666 1.554 1.350 1.996 1.877 1.398 2.071 1.859 0.323 0.305 

ON (I, II) 0.827 1.045 0.978 1.114 1.681 1.492 1.798 2.347 2.072 0.514 1.094 

P E ( I ) 1.108 1.203 1.158 1.330 1.735 1.546 — — — 0.390 — 

Q C ( I I ) 1.109 1.254 1.181 1.183 1.420 1.333 2.145 3.280 2.708 0.152 1.527 

SK (III) 0.882 1.127 1.002 0.964 1.184 1.162 1.046 1.329 1.273 0.160 0.271 

Table 3 is provided to compare predicted and observed IRIs for seven Provinces in 1998 and 

three Provinces in 1999. Predicted values are determined using regression. In the case of figure 5 

for Saskatchewan, the relationship is based on a (exponential) form of roughness progression 

data using data from 1989 to 1997 for each Province. The predicted values using the equation are 

then compared to the observed. The point of this exercise is to incorporate statistics and have the 

students examine the accelerating trends in roughness progression. Ultimately, the point is to 

demonstrate that the models should be recalibrated when future data becomes available. 

Generally, table 3 shows that predicted and observed values are relatively close. However, there 

are some relatively large differences (e.g., Quebec), and this implies that continued observation 

of performance prediction is most important, particularly if performance prediction models for 

overlay design are to be developed in the future. 
79 

Table 3. Comparison of predicted and observed IRI using mean values. 

Location 

Predicted 

1998 

Observed 

1998 

Predicted 

1999 

Observed 

1999 

MB 1.15 1.28 1.16 1.22 

NB 1.37 1.54 — —

NF 1.38 1.35 — —

NS 1.80 1.86 1.84 1.99 

ON 1.76 2.07 — —

QB 1.44 2.71 — —

SK 1.27 1.24 1.4 1.27 

Figure 5. Observed roughness trends on sections in Saskatchewan. 

Undergraduate Project Courses 

These courses typically require students to work in groups on an engineering problem (junior and 

senior years). For example, at the University of Waterloo, two of these courses are included in 

the engineering curriculum. The instructor provides basic instructions for using the DataPave 

Online Web site. Students are required to provide a detailed proposal of how they plan to use the 

data and what they plan to analyze. 

One project at the University of Waterloo involved a safety project in which skid numbers were 

assessed for selected flexible and rigid pavements. The students developed relationships between 

various factors in SN=f(time) and IRI=f(time). In another project course, one senior-level student 

validated IRI models developed for C–LTPP using LTPP data. The student used the analysis of 

variance (ANOVA) technique to determine if the C–LTPP data and the LTPP data were 

statistically different. As shown in table 4, significant differences were observed in 6 of 10 

Provinces at a 95 percent confidence level between groups. This student ended up pursuing 

graduate studies in pavement engineering. Another aspect of this project involved identifying 

sources of error in the prediction model. 

0.88 0.92 0.90

0.85 0.87

1.14

0.91

0.96

1.05 1.09

1.00

1.05

1.10

1.05 1.06

1.37

1.13 1.16

1.27

1.40

1.13 1.15

1.21

1.15 1.15

1.51

1.22 1.18

1.33

1.45

y = 0.9923e0.0249 Age

R2 = 0.405

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

IRI (m/km)

1st Quartile Mean

Median

Expon. (Mean)

3 rd Quartile
80 

Table 4. Senior-level student ANOVA comparison. 

Senior-Level Pavement Course 

A senior-level course in pavement engineering is also offered at the University of Waterloo. At 

this level, the students have used the data in various capacities. For example, one assignment 

involved comparing pavements in Ontario to pavements in California. The problem was directed 

at examining climatic performance, distress data, and relationship to IRI, traffic data, falling 

weight deflectometer (FWD) data, and environmental data. This senior-level engineering 

analysis included a core thickness assignment that involved calculating the variability of 

pavement thickness throughout a section and how this would impact the long-term performance. 

Then students were asked how additional traffic loads would impact the performance and 

ultimately how this variation would affect the structural soundness of the pavement. 

Another example of an assignment related to pavement management is presented in figures 6, 7, 

and 8. Various sections are analyzed based on all available data with particular emphasis on 

distress data, roughness data, and structural data. In this case, figures 6 and 7 show the amount of 

transverse cracking and the extent of rut depths on the Ontario sections. Using the data, students 

are required to develop a needs analysis for all of the Ontario sections. The summary is presented 

in figure 8. Other assignment problems require students to examine the impact of poor soil 

conditions. As part of the assignment, students are always required to provide charts and table 

that reinforce their conclusions. 

Ontario Test Section Performance Rating 

by Transverse Cracking - 1997

0

2

4

6

8

10

12

14

 < 25 25 < 100 > 100

# of Transverse Cracks per Section

# of Sections

Figure 6. Transverse crack analysis. 
81 

Ontario Test Section Performance Rating 

by Rutting - 1997

0

2

4

6

8

10

12

14

 < 25 25 < 50 > 50

Max Wheelpath Rut Depth (mm)

# of Sectons

Figure 7. Rut depth analysis. 

 

Ranking Section ID

1 B340

2 A310

3 2811

3 2812

5 1621

6 B330

7 1622

8 1620

8 B320

10 1680

10 B310

Needs

Now

Future

Needs

Ranking Section ID

1 B340

2 A310

3 2811

3 2812

5 1621

6 B330

7 1622

8 1620

8 B320

10 1680

10 B310

Needs

Now

Future

Needs

Figure 8. Needs analysis based on LTPP data. 

Graduate-Level Pavement Course 

The LTPP data has been used as part of a major course assignment in a graduate-level pavement 

course at the University of Waterloo. This assignment has been modified based on an initial 

discussion with Professor Norb Delatte.(5) This assignment problem is known as the “Pathfinder” 

assignment because there is no defined route for the students. Each student assigned a different 

Province or State. The student must use the LTPP data to provide senior management for that 

State’s or Province’s department of transportation with pavement design and management 

recommendations. Students are asked to address the following factors: 

• Determine whether current designs are working. 

• Compare designs to standards. 

• Carry out statistical analysis to prove their points. 

• Conduct regression, distribution, and cluster analyses. 
82 

• Provide recommendations on pavement management system (PMS) needs. 

• Develop policy with the data. 

The following is the actual question that was used in the 2003 Graduate-Level Pavement 

Engineering Course at the University of Waterloo. 

This question will require you to use data available through the U.S. Long-Term Pavement 

Performance Program. Data is available online at www.datapave.com. 

For this project you will need to analyze the performance of three LTPP sections. Analyze three 

sections (you select them) in the Province or State next to your name in the table below. If you 

have difficulty, you may use an alternative section from another Province or State. If you analyze 

more than three sections, bonus marks will be given. 

Consider this as a consulting effort for assessing distresses. Is there a rutting problem on this 

section? Is there a roughness concern? If so, what is causing these problems? 

The first step in using DataPave is to select the LTPP test sections of interest to the user. There 

are two ways of accomplishing this, by using either the visualization by location or the criteria 

method.(6) 

By Location (Using Online Map) 

1. Select the appropriate experiment type: general pavement studies (GPS), specific pavement 

studies (SPS), or seasonal monitoring program (SMP). 

2. Then select the specific experiment: GPS-1, SPS-5, or SMP. 

3. Then select the site-specific experiment and its State location. 

Note: This selection will provide performance data for the selected LTPP section(s). 

By Criteria 

1. Select first the State(s) or LTPP region. 

2. Select the experiment type. 

3. Select parameters for climatic region, subgrade type, and other filter criteria. 

Note: This selection will provide performance data for the selected LTPP section(s). 

Performance trends and detailed information can be obtained using either method. 

Table Export 

Data extraction is possible by conducting the following steps: 

1. Select “Tools/Table Export” from the menu items. 

2. Select the “IMS Module” and “Table” that you want to extract data from. 

3. Select the test site(s) (multiple selections are allowed by using standard keystrokes) that you 

want data from. 

4. Click on “export.” 
83 

5. Click on “download.” 

Note: A summary of the requested information is displayed in the following screen. Three 

additional documents can be downloaded at this time as well. 

• “LTPP Data Disclaimer.” 

• “LTPP Data Dictionary.” 

• “LTPP Data Codes.” 

You should include the following in your analysis: 

• Construction (compare inventory data on layer thickness to actual layer). 

• Pavement age and traffic carried. 

• Current damage and roughness, including cracking as well as rutting. 

• Subgrade classification and compaction (compare moisture content and density to γmax, 

optimum moisture content (OMC)). 

• Properties of each layer. 

• An 8-to-10-page summary of your analysis, including appropriate figures and tables. 

You must provide your analysis electronically in addition to a hard copy. 

You may also want to consider the following questions: 

• How does the magnitude of distress vary over the length of the section? 

• How has the extent of distress varied with time? 

• Do plots of transverse profiles provide any insight into what may be going on? 

• Do the aggregate gradations for the AC layers avoid the Superpave® restricted zone? 

The following table provides a summary of which student is responsible for each Province and 

State. 
84 

Table 5. Students responsible for Provinces 

and States. 

Name Province/State Sections 

Ishtiaque Tunio 

Syed Javed Iqbal 

Khalid Manzar 

Kehui Zhang 

Fayyaz Khan 

Ahmad Shah 

Saeed Ahmad 

Hamid S. Mohmand 

Iram Burhari 

Ignacio Davila Pazmino 

Liaquat Ali 

Rafi Uddin Ahmed 

Syed Aqeel Ahmed 

Muhammad (Tariq) Mahmood 

Muhammad Shoaib Kiani 

Bo Lan 

Haroon Raza 

Rickenson Daniel 

Gary Tang 

Javed Iqbal 

Aamer Shakoor 

Alberta 

British Columbia 

Manitoba 

New Brunswick 

Nova Scotia 

Ontario 

Quebec 

Saskatchewan 

Colorado 

Florida 

Georgia 

Illinois 

Indiana 

Maryland 

Massachusetts 

Michigan 

New York 

Ohio 

Oklahoma 

Pennsylvania 

Virginia 

Washington 

Wisconsin 

81 

82 

84 

85 

86 

87 

89 

90 

8 

12 

13 

17 

18 

24 

25 

26 

36 

39 

40 

42 

51 

53 

55 

In summary, this assignment is a course highlight because it forces students to think. They are 

required to use the data to develop design, construction, maintenance, and inservice pavement 

needs. It highlights the complex nature of pavement engineering and the need to examine 

multiple factors to develop a construction and maintenance program. 

CONTEXT 

The current asset value of North American roads and pavements is huge. Protecting this 

investment is critically important to the movement of goods and the mobility of people. 

However, competing pressure for funding from other segments of society and the need to cope 

with more costly and diminishing materials resources, requirements for zero-waste management, 

and sustainability present real threats to our ability to protect the investment and offer the level 

of service society expects. At the same time, there is both an opportunity and a critical need to 

carry out the research and technology development that will advance the planning, design, 

construction, and operation of our roads to a new level over the coming decades.(7)

The LTPP data can be used to provide effective and long-term partnerships among researchers, 

public sector agencies, and private industry. It can be used to help promote key areas for research 

such as the following: 
85 

• Innovative structural and materials technologies for pavements. 

• Advanced computer applications related to roads. 

• Pavement construction, preservation, and sustainable development. 

• Pavement and roadway safety. 

CONCLUSIONS 

This paper has presented several assignments for using the LTPP data in a typical civil 

engineering curriculum. The experiences and problems presented have been used at the 

University of Waterloo over the past 3 years. Overall, the following comments can be made: 

• LTPP data forces students to think. 

• It provides an endless source of unique problems. 

• It allows for instructor and student creativity. 

• It provides an opportunity to increase the talent pool of highly qualified people. 

• It exposes students to the state of the practice in pavement engineering. 

• It teaches critical thinking, which is an important skill for graduating engineers. 

• It provides data that results in avoiding cookie-cutter assignments 

• It provides students with appreciation for real-world data. 

Overall, there is a need for highly qualified people in this industry. Ultimately, educators play an 

important role in training and educating tomorrow’s engineers. Thus, there is a need to be 

responsible and ensure that graduates have the tools to handle technically complex problems. 

LTPP provides a great resource to academics to accomplish this task. 

ACKNOWLEDGMENTS 

The author gratefully acknowledges the students in her classes who take on these assignments 

with great enthusiasm. Their interest and feedback make the preparation time to develop 

problems worthwhile. In addition, special appreciation is extended to the LTPP staff for 

maintaining the Web site and making the data so readily available. 

REFERENCES 

1. Haas, R. (1972). Skilled Manpower Requirements for the Canadian Transportation Industry. 

Report prepared for Department of Manpower and Immigration, Ottawa, Canada. 

 

2. Tighe, S. (2003). “Succession Planning: Is the Civil Engineering Community Up For The 

Challenge?” Canadian Civil Engineer. Toronto, Canada, April–May, 20–23. 

3. Tighe, S.L., Patel, A., Haas, R., and Hudson, W. R. (1997). "Education and Training Needs for 

Road Infrastructure Renewal." Proceedings of the X-III IRF World Meeting. Toronto, Canada. 
86 

4. Tighe, S., Haas, R., and Li, N. (2001). “Overlay Performance in the Canadian Strategic 

Highway Research Program’s LTPP Study.” Transportation Research Record No. 1778.

Transportation Research Board, Washington, DC, 191–200. 

5. Delatte, N. (2001, July).Personal communication on Using LTPP in graduate courses at the 

University of Alabama at Birmingham. Birmingham, AL. 

6. Federal Highway Administration. (2003). “LTPP Guidelines for the Fourth International 

Contest on Using LTPP Data Analysis.” Washington, DC. 

7. Haas, R. (2001). "Reinventing the (Pavement Management) Wheel." Distinguished Lecture, 

Proceedings of the Fifth International Conference on Managing Pavements. Seattle, WA. 
87 

PROGRAM ACKNOWLEDGMENTS 

We are grateful for the efforts of the American Society of Civil Engineers/Transportation and 

Development Institute (ASCE/T&DI) task committee that oversaw the contest activities. In 

particular, we would like to recognize the contributions of the contest review board in charge of 

administering the contest: 

Karim Chatti, Michigan State University, Acting Chair 

Fouad M. Bayomy, University of Idaho, Past Chair 

Antonio Nieves Torres, Federal Highway Administration, Co-Chair 

Andrea Baker, Transportation and Development Institute, ASCE Liaison 

In addition, we would like to recognize the technical support of the ASCE/T&DI task committee 

members who helped review the contest papers: 

Imad L. Al-Qadi, University of Illinois at Urbana-Champaign (formerly at Virginia Polytechnic 

Institute and State University) 

Neeraj J. Buch, Michigan State University 

Armando J. De La Rocha, Western Technologies, Inc. 

Norbert J. Delatte, Cleveland State University (formerly at the University of Alabama at 

Birmingham) 

Gerardo W. Flintsch, Virginia Polytechnic Institute and State University 

Paul T. Foxworthy, ERES Consultants 

Andrew J. Gisi, Kansas Department of Transportation 

Roger M. Larson, Applied Pavement Technology, Inc. 

K. Wayne Lee, University of Rhode Island 

Tom T. Papagiannakis, Washington State University 

Olga L. Selezneva, ERES Consultants 

Ali A. Selim, South Dakota State University 

Robert E. Smith, MACTEC, Inc. 

Roger E. Smith, Texas A&M University 

Nabil J. Suleiman, University of North Dakota 

Waheed Uddin, University of Mississippi 

We would also like to give special recognition to the technical reviewers who are not members 

of the task committee for their voluntary effort in reviewing the contest papers: 

Ghassan Abu-Lebdeh, Michigan State University 

Adrian R Archilla, University of Hawaii 

Gilbert Y. Baladi, Michigan State University 

Don Fohs, Federal Highway Administration 

Keith Herbold, Federal Highway Administration 

Haiming Huang, ERES Consultants 

Newton Jackson, Nichols Consulting Engineers 

Sean Lin, Federal Highway Administration 

Walid Nassar, Bradley University 
88 

Jorge Prozzi, University of Texas at Austin 

Stefan Romanoschi, Kansas State University 

Monte Symons, Federal Highway Administration 

Finally, we would like to acknowledge the participants in the contest, who included students and 

their advisors (categories 1 and 2), pavement engineering professionals (category 3), and 

university professors (categories 3 and 4). They have taken their valuable time to make an 

important contribution. In all, 20 papers were submitted, including 3 in category 1 

(undergraduate students), 14 in category 2 (graduate students), 1 in category 3 (partnership 

between industry and academia), and 3 in category 4 (curriculum development). The winning 

papers from each category for the 2003–2004 contest are included in this volume. 
HRDI-13/09-06(1M)E

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