Research, Development, and Technology

Turner-Fairbank Highway Research Center

6300 Georgetown Pike

McLean, VA 22101-2296
Foreword 

A key factor in the long-term performance of both asphalt and portland cement concrete pavements is 

initial pavement smoothness. In general, the smoother a pavement is built, the smoother it stays over 

time, resulting in lower maintenance costs and more comfort and safety for the traveling public. State 

highway agencies recognized in the 1960s the importance of controlling initial pavement smoothness, 

and began developing and implementing smoothness specifications. As the technology and equipment 

for measuring pavement smoothness advanced, two predominant methods emerged. 

The profilograph is widely used to measure and control initial smoothness by producing profile traces, 

which can be evaluated to identify severe bumps and to establish an easily understood, overall measure 

of smoothness, the profile index (PI). However, concerns about the accuracy of the profilograph have 

grown significantly in the last decade. The more recently developed inertial profiler is used to quickly 

and accurately monitor in-service pavements, and produces a more definitive profile of a pavement 

from which the widely accepted International Roughness Index (IRI) can be computed. Use of inertial 

profilers has remained limited in initial construction acceptance testing due to their higher cost and 

constraints on timeliness of testing. Thus, in many agencies, initial pavement smoothness has been 

measured one way (profilograph PI) and smoothness over time has been measured another way (inertial 

profiler IRI). 

Despite efforts to make adjustments for more accuracy in the computation of PI, it is evident that IRI 

will become the statistic of choice in future smoothness specifications. So how do agencies make the 

switch from their current PI-based specifications to IRI specifications? This study attempts to provide 

answers through the analysis of comprehensive time history smoothness data collected by high-speed 

inertial profilers under the Long-Term Pavement Performance (LTPP) program. Using advanced 

computer simulation algorithms, it is possible to compute PI values from surface profile data, thereby 

allowing detailed comparisons between IRI and PI. 

T. Paul Teng, P.E. 

Director, Office of Infrastructure 

 Research and Development 

Notice 

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

interest of information exchange. The U.S. Government assumes no liability for its contents or use 

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

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

appear in this report only because they are considered essential to the object of the document. 
Technical Report Documentation Page

1. Report No.

 FHWA-RD-02-057

2. Government Accession No.

 

3. Recipient’sCatalog No.

 

5. Report Date

 OCTOBER 2002

4. Title and Subtitle

 PAVEMENT SMOOTHNESS INDEX RELATIONSHIPS

6. Performing Organization Code

 

7. Author(s)

 Kelly L. Smith, Leslie Titus-Glover, Lynn D. Evans

8. Performing Organization Report No.

 

10. Work Unit No.

 C6B

9. Performing Organization Name and Address

 ERES Division of Applied Research Associates, Inc.

 505 W. University Avenue

 Champaign, IL 61820

11. Contract or Grant No.

 GS-10F-0298K

13. Type of Report and Period Covered

 Final Report

 February - November 2001

12. Sponsoring Agency Name and Address

 Office of Pavement Technology

 Federal Highway Administration

 400 Seventh St., S.W.

 Washington, D.C. 20590

14. Sponsoring Agency Code

 

15. Supplementary Notes

 FHWA Contracting Officer’s Technical Representative (COTR): Mark Swanlund, HIPT-1

16. Abstract

Nearly all State highway agencies use smoothness specifications to ensure that hot-mix asphalt (HMA) and 

portland cement concrete (PCC) pavements are built to high levels of smoothness. Not only is an initially 

pavement generally indicative of quality workmanship, but it has been shown to last longer than a pavement built 

rougher.

About half of all current State smoothness specifications for HMA and more than three-fourths of all current PCC 

smoothness specifications are centered around the Profile Index (PI), as often measured using a profilograph. The 

vast majority of these specifications utilize a 5-mm (0.2-in) blanking band in computing PI (i.e., PI5-mm). 

Unfortunately, because of the technical limitations of the profilograph equipment and PI computation procedures, 

the adequacy of PI5-mm in characterizing roughness and having it relate to user response has come into question.

The International Roughness Index (IRI) or the Profile Index using a 0.0-mm blanking band (PI0.0) seem to 

provide better measures of smoothness and better correlation with user response. However, one barrier to more 

widespread implementation of these new smoothness standards is the lack of objective, verifiable correlation 

methods for use in establishing specification limits using the IRI or PI0.0. Assistance in selecting appropriate IRI 

and PI0.0 specification limits is needed to provide a basis for modifying current specifications to thes e more 

reproducible and portable smoothness indices.

This research effort has developed a series of relationships between IRI and PI that can assist States in 

transitioning to in IRI or PI0.0 smoothness specification for HMA and PCC pavements.

17. Key Words

 LTPP, pavement smoothness,

 profilograph, inertial profiler,

 international roughness index,

 profile index, smoothness limits

18. Distribution Statement

 No restrictions. This document is available to the public through the

 National Technical Information Service, Springfield, Virginia 22161.

19. Security Classif. (of this report) 

 Unclassified

20. Security Classif. (of this page)

 Unclassified

21. No. of Pages

 116

22. Price

 

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized
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APPROXIMATE CONVERSIONS TO SI UNITS APPROXIMATE CONVERSIONS FROM S

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

 LENGTH LENGTH 

in inches 25.4 millimeters mm mm millimeters 0.039 inches ft feet 0.305 meters m m meters 3.28 feet yd yards 0.914 meters m m meters 1.09 yards mi miles 1.61 kilometers km km kilometers 0.621 miles 

AREA AREA 

in2 square inches 645.2 square millimeters mm2 mm2 square millimeters 0.0016 square ft2 square feet 0.093 square meters m2 m2 square meters 10.764 square yd2 square yard 0.836 square meters m2 m2 square meters 1.195 square ac acres 0.405 hectares ha ha hectares 2.47 acres mi2 square miles 2.59 square kilometers km2 km2 square kilometers 0.386 square 

VOLUME VOLUME 

fl oz fluid ounces 29.57 milliliters mL mL milliliters 0.034 fluid ougal gallons 3.785 liters L L liters 0.264 gallons ft3 cubic feet 0.028 cubic meters m3 m3 cubic meters 35.314 cubic feyd3 cubic yards 0.765 cubic meters m3 m3 cubic meters 1.307 cubic yNOTE: volumes greater than 1000 L shall be shown in m3

MASS MASS 

oz ounces 28.35 grams g g grams 0.035 ounces lb pounds 0.454 kilograms kg kg kilograms 2.202 pounds T short tons (2000 lb) 0.907 megagrams 

(or "metric ton") 

Mg (or "t") 

Mg (or "t") 

megagrams (or "metric ton") 

1.103 short to

TEMPERATURE (exact degrees) TEMPERATURE (exact degrees) oF Fahrenheit 5 (F-32)/9 Celsius oC oC Celsius 1.8C+32 Fahren

 or (F-32)/1.8 

ILLUMINATION ILLUMINATION 

fc foot-candles 10.76 lux lx lx lux 0.0929 foot-cafl foot-Lamberts 3.426 candela/m2 cd/m2 cd/m2 candela/m2 0.2919 foot-La

FORCE and PRESSURE or STRESS FORCE and PRESSURE or STRESS 

lbf poundforce 4.45 newtons N N newtons 0.225 poundflbf/in2 poundforce per 

square inch 

6.89 kilopascals kPa kPa kilopascals 0.145 poundfo

square 

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

ii 
iii 

TABLE OF CONTENTS 

Chapter 1. Introduction............................................................................................................... 1 

Background ......................................................................................................................... 1 

Problem Statement .............................................................................................................. 2 

Study Objectives ................................................................................................................. 3 

Chapter 2. Literature Review ..................................................................................................... 5 

Introduction ......................................................................................................................... 5 

Past Studies on PI–IRI Relationships.................................................................................. 5 

Summary ........................................................................................................................... 14 

Chapter 3. LTPP Data Collection and Project Database Development................................ 17 

Introduction ....................................................................................................................... 17 

Collection of LTPP Profile Data ....................................................................................... 17 

Conversion of Profile Data to Simulated PI Values.......................................................... 18 

Populating the Project Database........................................................................................ 18 

Chapter 4. Development of LTPP-Based Smoothness Index Relationships......................... 21 

Introduction ....................................................................................................................... 21 

Step 1—Preliminary Evaluation........................................................................................ 22 

Step 2—Selection of Appropriate Model Form ................................................................ 32 

Step 3—Group Data into Sets with Similar Smoothness Relations.................................. 32 

Steps 4 and 5—Develop Tentative Models and Assess Models for 

Reasonableness.................................................................................................................. 39 

Step 6—Select Final Models............................................................................................. 42 

Chapter 5. Adaptation of LTPP-Based Models to Current State 

 Smoothness Specifications...................................................................................... 45 

Introduction ....................................................................................................................... 45 

Overview of State Smoothness Specifications.................................................................. 45 

Development of Recommended Initial IRI and PI0.0 Levels............................................. 46 

Chapter 6. Conclusions and Recommendations...................................................................... 57 

Conclusions ....................................................................................................................... 57 

Recommendations ............................................................................................................. 58 

Appendix A: IRI and PI Relationships for AC Pavements 

Appendix B: IRI and PI Relationships for PCC Pavements 
iv 

LIST OF FIGURES 

Figure 

No.

1. Sensitivity of simulated profilograph to spatial frequency.................................................... 2 

2. Relationship between IRI and manually generated PI in PTI profilograph 

 calibration study. ................................................................................................................... 6 

3. Relationship between IRI and computer-generated PI in PTI profilograph 

 calibration study. ................................................................................................................... 6 

4. Correlation of IRI and PI in Arizona pavement smoothness study ....................................... 8 

5. IRI–PI5-mm (PI0.2-in) correlations established in Florida’s ride quality 

 equipment study .................................................................................................................... 9 

6. IRI–PI2.5-mm (PI0.1-in) and IRI–PI0.0 correlations established in Florida’s ride quality 

 equipment study .................................................................................................................... 9 

7. Relationship between IRI and computer-simulated PI values in TTI equipment 

 comparison study................................................................................................................. 11 

8. Plots of IRI and simulated PI0.0 values generated by various profilers in Kansas DOT 

 lightweight profiler comparison study ................................................................................ 12 

9. Relationship between IRI and California profilograph PI0.0 in Kansas DOT 

 lightweight profiler comparison study ................................................................................ 12 

10. Relationship between IRI and simulated PI0.2-in (PI5-mm) in Illinois DOT 

 bridge smoothness study ..................................................................................................... 13 

11. Graphical comparison of documented PI5-mm–IRI smoothness relationships...................... 15 

12. Graphical comparison of documented PI2.5-mm–IRI smoothness relationships.................... 15 

13. Graphical comparison of documented PI0.0–IRI smoothness relationships......................... 16 

14. Flow chart for developing pavement smoothness models................................................... 21 

15. Histogram showing the distribution of IRI data used in model development 

 (all AC pavements).............................................................................................................. 23 

16. Histogram showing the distribution of IRI data used in model development 

 (all PCC pavements)............................................................................................................ 23 

17. Histogram showing the distribution of PI5-mm data used in model development 

 (all AC pavements).............................................................................................................. 24 

18. Histogram showing the distribution of PI5-mm data used in model development 

 (all PCC pavements)............................................................................................................ 24 

19. IRI vs. PI0.0 for all AC pavements and climates .................................................................. 27 

20. PI0.0 vs. PI5-mm for all PCC pavements and climates ........................................................... 27 

21. IRI vs. PI0.0 by AC pavement type for all climates ............................................................. 28 

22. PI0.0 vs. PI5-mm by climate for all PCC pavement types....................................................... 28 

23. Graphical comparison of PI5-mm–IRI smoothness relationships for AC pavements............ 29 

24. Graphical comparison of PI5-mm–IRI smoothness relationships for PCC pavements.......... 30 

25. Graphical comparison of PI2.5-mm–IRI smoothness relationships for AC pavements.......... 30 

26. Graphical comparison of PI0.0–IRI smoothness relationships for AC pavements............... 31 

27. Graphical comparison of PI0.0–IRI smoothness relationships for PCC pavements............. 31 
v 

LIST OF FIGURES (continued) 

Figure 

No.

28. Conceptual plot showing relationships of smoothness indices within 

 and between cells ................................................................................................................ 35 

29. Histogram showing the distribution of slope for IRI versus PI0.0

 (for cells in block 1, AC surface pavements)...................................................................... 35 

30. Histogram showing the distribution of slope for IRI versus PI0.0

 (for cells in block 2, PCC surface pavements).................................................................... 36 

31. Histogram showing the distribution of slope for PI0.0 versus PI5-mm

 (for cells in block 1, AC surface pavements)...................................................................... 36 

32. Histogram showing the distribution of slope for PI0.0 versus PI5-mm

 (for cells in block 2, PCC surface pavements).................................................................... 37 
vi 

LIST OF TABLES 

Table 

No.

1. Summary of documented PI–IRI relationships ................................................................... 14 

2. Breakdown of test sections by LTPP experiment................................................................ 19 

3. Breakdown of test sections by State .................................................................................... 20 

4. Summary of basic statistics of data used in model development ........................................ 25 

5. Matrix of scatter plots created for model development....................................................... 26 

6. Factorial of cells used for model development.................................................................... 33 

7. ANOVA results on the effect of pavement type and climate on PI–IRI relationship 

 for AC pavements................................................................................................................ 38 

8. ANOVA results on the effect of pavement type and climate on PI–IRI relationship 

 for PCC pavements.............................................................................................................. 39 

9. Summary of groupings (merged cells) used for model development.................................. 40 

10. PI-to-IRI index conversion equations and variability indices for AC pavements............... 41 

11. PI-to-PI index conversion equations and variability indices for AC pavements................. 41 

12. PI-to-IRI index conversion equations and variability indices for PCC pavements............. 42 

13. PI-to-PI index conversion equations and variability indices for PCC pavements............... 42 

14. State agency smoothness specifications for asphalt pavements .......................................... 47 

15. State agency smoothness specifications for concrete pavements........................................ 49 

16. Estimated equivalent PI0.0 and IRI values for PI-based smoothness specifications 

 for new AC pavement ......................................................................................................... 51 

17. Estimated equivalent PI0.0 and IRI values for PI-based smoothness specifications 

 for AC overlays on AC pavement....................................................................................... 52 

18. Estimated equivalent PI0.0 and IRI values for PI-based smoothness specifications 

 for AC overlays on PCC pavement..................................................................................... 53 

19. Estimated equivalent PI0.0 and IRI values for PI-based smoothness specifications 

 for new PCC pavement and PCC overlays.......................................................................... 54
1 

Chapter 1. Introduction 

Background 

Initial pavement smoothness is a key factor in the performance and economics of a pavement 

facility. All other things being equal, the smoother a pavement is built, the smoother it will stay 

over time. The smoother it stays over time, the longer it will serve the traveling public, thereby 

benefiting the public in terms of investment (initial construction and upkeep) and vehicular wear 

costs, as well as comfort and safety. 

As a means of controlling initial pavement smoothness, several highway agencies began 

developing and implementing smoothness specifications in the late 1950s and 1960s. These 

specifications generally included straightedge testing and a form of ride quality testing using 

response-type mechanical equipment, such as the Bureau of Public Roads (BPR) Roughometer, 

the Mays Ridemeter, and the Portland Cement Association (PCA) Ridemeter, or simple profiling 

devices, such as the Chloe profilometer and the profilograph. 

Between the late 1960s and the 1980s, the profilograph emerged as the clear choice among 

highway agencies for measuring and controlling initial smoothness, particularly for concrete 

pavements. This 7.6-meters (25-feet) rolling reference system is capable of producing profile 

traces, which can be evaluated to identify severe bumps and to establish an overall measure of 

smoothness (i.e., the profile index [PI]). 

During this same period of time, more complex profiling systems were being developed and 

marketed, which provided a much quicker assessment and more accurate representation of 

pavement smoothness. Inertial profilometers or profilers consist of an integrated set of vertical 

displacement sensors, vertical accelerometers, and analog computer equipment mounted in a 

full-sized vehicle (usually a van or large automobile) equipped with a distance-measuring 

instrument (DMI). These pieces of equipment, which can be operated at highway speed, are 

capable of producing a more definitive profile of a pavement, from which the universally 

accepted International Roughness Index (IRI) can be computed. 

Inertial profilers’ first major role in the pavements realm involved long-term condition 

monitoring of in-service pavements. The reliability and repeatability of these devices greatly 

enhanced the quality of the pavement management data used by highway agencies in 

programming maintenance and rehabilitation (M&R) activities. Although the use of inertial 

profilers in condition monitoring increased substantially in the 1980s and early 1990s, their 

application in construction acceptance testing remained limited due to their high cost and 

constraints on the timeliness of testing (i.e., tests on rigid pavements could not be performed 

until after a few days of curing). Thus, in many agencies, initial pavement smoothness has been 

measured one way (profilograph PI) and smoothness over time has been measured another way 

(inertial profiler IRI). 

In recent years, the technology of inertial profiling systems present on full-sized vehicles has 

been adopted on smaller motorized vehicles, such as the John Deere and Kawasaki utility carts 
2

and four-wheel all-terrain vehicles (ATVs). These lightweight profilers, which are currently 

being evaluated by several agencies and have been approved for use by a few, enable testing 

personnel to obtain timely and highly definitive measurements of surface profiles at rates of 

speed significantly higher than profilographs (24 km/hr [15 miles/hour] versus 5 kilometers/hour 

[3 mi/hr]). The profilers are capable of producing IRI and other indices (e.g., simulated PI and 

Mays output, ride number [RN]) commonly used in controlling and monitoring pavement 

smoothness.

Problem Statement

Although the profilograph has served the highway community fairly well as an easily understood 

index of initial pavement smoothness, concerns about its accuracy and relationship with user 

response (fair to poor) have grown significantly in the last decade. For instance, because the 

device measures only wavelengths within the range of 0.3 to 23 m (1 to 75 ft) and because it 

amplifies wavelengths that are factors of its length (i.e., 7.6 m [25 ft]), the profile it produces is 

biased from a pavement’s true profile. This can be seen in figure 1, where a true profile would 

be represented by a gain of 1.0. Coupled with the fact that a 2.5- or 5-millimeters (0.1- or 0.2-

inch) blanking band is often applied when computing PI, thereby masking some roughness, it is 

understandable how correlation with user response is generally deemed inadequate.

Over the last 6 years, a handful of State agencies have moved toward using a zero blanking band 

PI (PI0.0) statistic for construction acceptance testing. This has reportedly improved the ability to 

control initial smoothness and bettered the relationship between profilograph PI and user 

response. However, the fact that the same biased profiles are being used to compute PI0.0 does 

not fully alleviate the major concerns with the profilograph. Among many agencies, the belief 

persists that inertial profilers are the best means for specifying and evaluating initial smoothness.

Figure 1. Sensitivity of simulated profilograph to spatial frequency.

0

1

2

3

4

0.001 0.01 0.1 1 10

Spatial frequency (cycles/ ft)

Gain
3

Additional support for using inertial profilers in construction acceptance testing comes from the 

desire for a “cradle-to-grave” smoothness index. Since it has been shown that future smoothness 

is a function of initial smoothness, use of one index for tracking smoothness over the entire life 

of a pavement would significantly benefit pavement managers and designers through improved 

performance prediction modeling.

Recent surveys of State highway agencies indicate that about 10 percent (4 of 34 respondents) 

use IRI to control initial smoothness (Baus and Hong, 1999), while about 84 percent (31 of 37 

respondents) use IRI to monitor pavement smoothness over time (Ksaibati et al., 1999). It is 

quite evident that IRI will become the statistic of choice in future smoothness specifications, 

given that: many agencies are investigating lightweight inertial profilers, and that the proposed 

2002 Design Guide under development by the National Cooperative Highway Research Program 

(NCHRP) will include IRI prediction models that are a function of initial IRI (IRI0).

So, how do agencies make the switch from their current PI-based specifications to IRI 

specifications? What levels of IRI should be specified which would be comparable or equivalent 

to the PI values currently stipulated? How confident can an agency be that newly established IRI 

levels reflect the levels of ride quality previously specified? These are all questions that must be 

properly addressed in light of the fact that several past pavement smoothness studies show poor 

correlation between PI values produced by a profilograph and IRI values generated by inertial 

profilers.

This study attempts to provide answers to the above questions through the analysis of 

comprehensive time history smoothness data collected by high-speed inertial profilers under the 

Long-Term Pavement Performance (LTPP) program. These smoothness data include archived 

surface profile data and corresponding computed IRI values for many General Pavement Studies 

(GPS) and Specific Pavement Studies (SPS) test pavements located throughout the United States. 

Using advanced computer simulation algorithms, it is possible to compute PI values from the 

surface profile data, thereby allowing detailed comparisons between IRI and PI.

Study Objectives

The specific objectives of the study include the following:

• Analyze LTPP profile data from GPS and SPS test sections for IRI and PI using the 0.0-mm 

(0.0-inch), 2.5-mm (0.1-inch), and 5.0-mm (0.2-inch) blanking bands. This includes profile 

data from asphalt concrete (AC) and portland cement concrete (PCC) test sections in the four 

LTPP climatic zones: dry freeze (DF), dry nonfreeze (DNF), wet freeze (WF), and wet 

nonfreeze (WNF).

• Compile and provide recommendations for smoothness specification acceptance limits for 

new and rehabilitated PCC and hot-mix asphalt (HMA) pavements, based upon IRI and PI.

5 

Chapter 2. Literature Review 

Introduction 

To begin the investigation of the relationship between IRI and PI, a fairly extensive literature 

search was performed focusing on national and State-sponsored pavement smoothness studies 

conducted in the last 15 years. This search resulted in the collection of many reports, papers, and 

articles on the topic of smoothness, but only a handful dealing specifically with the correlation of 

IRI and PI. 

Presented in this section is a synopsis of seven documented studies and the PI-to-IRI correlations 

developed in those efforts. Most of the correlations involve PI readings from actual profilograph 

equipment; however, a few are based on computer-simulated PI values produced from surface 

profiles measured by inertial profilers. 

Past Studies on PI–IRI Relationships 

Pennsylvania Transportation Institute Profilograph Calibration Study

As part of a major effort to develop calibration procedures for profilographs and evaluate 

equipment for measuring the smoothness of new pavement surfaces, the Pennsylvania 

Transportation Institute (PTI) conducted a full-scale field-testing program on behalf of the 

Federal Highway Administration (FHWA) (Kulakowski and Wambold, 1989). Concrete and 

asphalt pavements at five different locations throughout Pennsylvania were selected for the 

experiment; each pavement was new or newly surfaced. Multiple 0.16-km (0.1-mi) long 

pavement sections were established at each location, resulting in 26 individual test sections over 

which 2 different types of profilographs (California and Rainhart), a Mays Meter, and an inertial 

profiler were operated. The resulting smoothness measurements were evaluated for correlation. 

Figure 2 shows the relationship between the inertial profiler IRI and the PI5-mm (PI0.2-in) 

determined manually from the California-type profilograph. As can be seen, the resulting linear 

regression equation had a coefficient of determination (R2) of 0.57. Figure 3 shows the 

relationship between the inertial profiler IRI and the computer-generated PI5-mm (PI0.2-in) from the 

California-type profilograph. Although the resulting linear regression equation had a similar 

coefficient of determination (R2 = 0.58), its slope was considerably flatter. For any given IRI, 

the data show a wide range of PI5-mm (PI0.2-in). 

Although both of these relationships were based on measurements from both concrete and 

asphalt pavement sections, neither one is considerably different from regressions based solely on 

data from the concrete sections. 
6 

Figure 2. Relationship between IRI and manually generated PI5-mm (PI0.2-in) in PTI profilograph 

 calibration study (Kulakowski and Wambold, 1989). 

 

Figure 3. Relationship between IRI and computer-generated PI5-mm (PI0.2-in) in PTI profilograph 

 calibration study (Kulakowski and Wambold, 1989). 

IRI = 4.0238*PI + 70.13

R2

 = 0.5654

N = 25

0

20

40

60

80

100

120

140

160

180

0 2 4 6 8 10 12 14 16 18 20

PI0.2-in (analog strip chart, manual reduction), in/mi

IRI, in/mi

PCC Pavements AC Pavements Linear (PCC and AC Pavements)

IRI = 2.4599*PI + 66.22

R2

 = 0.5806

N = 25

0

20

40

60

80

100

120

140

160

180

0 4 8 12 16 20 24 28 32 36

PI0.2-in (computerized, continuous scale), in/mi

IRI, in/mi

PCC Pavements AC Pavements Linear (PCC and AC Pavements)
7 

Arizona DOT Initial Smoothness Study

In 1992, the Arizona Department of Transportation (AZDOT) initiated a study to determine the 

feasibility of including their K.J. Law 690 DNC Profilometer (optical-based inertial profiler) as 

one of the principal smoothness measuring devices for measuring initial pavement smoothness 

on PCC pavements (Kombe and Kalevela, 1993). At the time, the AZDOT used a Cox 

California-type profilograph to test newly constructed PCC pavements for compliance with 

construction smoothness standards. 

To examine the correlative strength of the Profilometer (IRI) and profilograph (PI) outputs, a 

group of twelve 0.16-km (0.1-mi) pavement sections around the Phoenix area were selected for 

testing. The smoothness levels of the sections spanned a range that is typical of newly built 

concrete pavement—PI5-mm (PI0.2-in) between 0 and 0.24 m/km (15 inches/mile). A total of three 

smoothness measurements were made with the Profilometer over each wheelpath of each 

selected section, whereas a total of five measurements were made by the profilograph over each 

wheelpath of each section. The mean values of each set of three or five measurements were then 

used to correlate the IRI and PI5-mm (PI0.2-in) values. 

Simple linear regression analyses performed between the left wheelpath, right wheelpath, and 

both wheelpath sets of values indicated generally good correlation between the two indexes. 

Figure 4 shows the scatter plots of each group, as well as the regression line associated with the 

both wheelpath data group. As can be seen, the R2

 for the both wheelpath regression line was 

very high (0.93). 

University of Texas Smoothness Specification Study

In the course of developing new smoothness specifications for rigid and flexible pavements in 

Texas, researchers at the University of Texas conducted a detailed field investigation comparing 

the McCracken California-type profilograph and the Face Dipstick, a manual Class I profile 

measurement device (Scofield, 1993). The two devices were used to collect smoothness 

measurements on 18 sections of roadway consisting of both asphalt and concrete pavements. For 

both devices, only one test per wheelpath was performed. 

Results of linear regression analysis showed a strong correlation (R2 = 0.92) between the IRI and 

PI5-mm (PI0.2-in) values. The resulting linear regression equation had a higher intercept value than 

those obtained in the PTI and AZDOT studies, while the slope of the equation was more in line 

with the slopes generated in the PTI study. 

Florida DOT Ride Quality Equipment Comparison Study

Looking to upgrade its smoothness testing and acceptance process for flexible pavements, the 

Florida DOT (FLDOT) undertook a study designed to compare its current testing method (rolling 

straightedge) with other available methods, including the California profilograph and the highspeed inertial profiler (FLDOT, 1997). A total of twelve 0.81-km (0.5-mi) long pavement 

sections located on various Florida State highways were chosen for testing. All but one of the 

sections represented newly constructed or resurfaced asphalt pavements. 
8 

 

Figure 4. Correlation of IRI and PI5-mm (PI0.2-in) in Arizona pavement 

 smoothness study (Kombe and Kalevela, 1993).

The left and right wheelpaths of each test section were measured for smoothness by each piece of 

equipment. The resulting smoothness values associated with each wheelpath were then 

averaged, yielding the values to be used for comparing the different pieces of equipment. 

The inertial profiler used in the study was a model manufactured by the International Cybernetics 

Corporation (ICC). Because one of the objectives of the study was to evaluate different 

technologies, the ICC inertial profiler was equipped with both laser and ultrasonic sensors. 

Separate runs were made with each sensor type, producing two sets of IRI data for comparison. 

Figure 5 shows the relationships developed between the profilograph PI5-mm (PI0.2-in) and the IRI 

values respectively derived from the laser and ultrasonic sensors. As can be seen, both 

correlations were fairly strong (R2

 values of 0.88 and 0.67), and the linear regression equations 

were somewhat similar in terms of slope. As is often the case, however, the ultrasonic-based 

smoothness measurements were consistently higher than the laser-based measurements, due to 

the added sensitivity to items such as surface texture, cracking, and temperature. This resulted in 

a higher y-intercept for the ultrasonic-based system. 

Figure 6 shows the correlations developed between IRI and PI2.5-mm (PI0.1-in) and IRI and PI0.0. It 

is quite clear from this and the previous figure that the application of smaller blanking bands 

results in higher PI values, since additional components of roughness are considered. More 

significant, however, is the fact that both the slopes and the y-intercept values in the resulting 

linear regression equations decrease with smaller blanking bands. This is, again, the result of 

additional profile roughness being considered. 

IRI = 6.1282*PI + 52.931

R2

 = 0.9245

N = 12

0

20

40

60

80

100

120

140

160

180

0 2 4 6 8 10 12 14 16

PI0.2-in, in/mi

IRI, in/mi

Left WP Right WP Both WP Linear (Both WP)
9 

Figure 5. IRI–PI5-mm (PI0.2-in) correlations established in Florida’s ride quality equipment study 

 (FLDOT, 1997). 

Figure 6. IRI–PI2.5-mm (PI0.1-in) and IRI–PI0.0 correlations established in Florida’s 

 ride quality equipment study (FLDOT, 1997). 

Ultrasonic IRI = 3.1539*PI + 52.196

R2

 = 0.6665

N = 12

Laser IRI = 3.9519*PI + 39.927

R2

 = 0.8846

N = 12

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8 10 12 14

PI0.2-in, in/mi

IRI, in/mi

Laser Profilometer Ultrasonic Profilometer

Linear (Ultrasonic Profilometer) Linear (Laser Profilometer)

Ultrasonic IRI = 2.708*PI(0.1-in) + 37.969

R2 = 0.9568, N = 5

Laser IRI = 2.7345*PI(0.1-in) + 31.907

R2 = 0.9586, N = 5

Ultrasonic IRI = 2.1991*PI(0.0-in) + 19.361

R2 = 0.9016, N = 5

Laser IRI = 2.1899*PI(0.0-in) + 13.75

R2 = 0.8784, N = 5

0

20

40

60

80

100

120

140

0 5 10 15 20 25 30 35 40

PI, in/mi

IRI, in/mi

Laser IRI vs PI(0.1-in) Ultrasonic IRI vs PI(0.1-in)

Laser IRI vs PI(0.0-in) Ultrasonic IRI vs PI(0.0-in)

Linear (Ultrasonic IRI vs PI(0.1-in)) Linear (Laser IRI vs PI(0.1-in))

Linear (Ultrasonic IRI vs PI(0.0-in)) Linear (Laser IRI vs PI(0.0-in))
10 

It is reasonable to surmise from these observations that, if the PI0.0 was computed from a more 

accurate pavement profile than the one generated by a profilograph, the y-intercept would be 

much closer to zero. This is because the roughness associated with long wavelengths (e.g., long 

dips or humps) is automatically filtered out as a result of the short baselength of profilographs. 

Texas Transportation Institute Smoothness Testing Equipment Comparison Study

As part of a multi-staged effort to transition from a profilograph-based smoothness specification 

to a profile-based specification, the Texas Transportation Institute (TTI) was commissioned by 

the Texas DOT (TXDOT) in 1996 to evaluate the relationship between IRI and profilograph PI 

(Fernando, 2000). The study entailed obtaining longitudinal surface profiles (generated by one 

of the Department’s high-speed inertial profiler) from 48 newly AC resurfaced pavement 

sections throughout Texas, generating computer-simulated profilograph traces from those 

profiles using a field-verified kinematic simulation model, and computing PI5-mm (PI0.2-in) and 

PI0.0 values using the Pro-Scan computer software. 

A total of three simulated runs per wheelpath per section were performed, from which an average 

PI value for each section was computed. The resulting section PI values were then compared 

with the corresponding section IRI values, which had been computed by the inertial profiling 

system at the time the longitudinal surface profiles were produced in the field. Since both the PI 

and IRI values were based on the same longitudinal profiles, potential errors due to differences in 

wheelpath tracking were eliminated. 

Illustrated in figure 7 are the relationships between the IRI and the simulated PI response 

parameters. As can be seen, a much stronger trend was found to exist between IRI and PI0.0 than 

between IRI and PI5-mm (PI0.2-in). Again, this is not unexpected since the application of a 

blanking band has the natural effect of masking certain components of roughness. In comparison 

with the other IRI–PI5-mm (IRI–PI0.2-in) correlations previously presented, the one developed in 

this study is quite typical. The linear regression equation includes a slightly higher slope but a 

comparable y-intercept value. 

Kansas DOT Lightweight Profilometer Performance Study

The major objective of this 1999/2000 study was to compare as-constructed smoothness 

measurements of concrete pavements taken by the Kansas DOT’s (KDOT) manual Californiatype profilograph, four lightweight inertial profilers (Ames Lightweight Inertial Surface 

Analyzer [LISA], K.J. Law T6400, ICC Lightweight, and Surface Systems Inc. [SSI] 

Lightweight), and two full-sized inertial profilers (KDOT South Dakota-type profiler, K.J. Law 

T6600) (Hossain et al., 2000). The simulated PI0.0 values produced by the various lightweight 

systems were statistically compared with the California-type profilograph PI0.0 readings to 

determine the acceptability of using lightweight systems to control initial pavement smoothness. 

In addition, IRI values generated by the lightweight systems were statistically compared with 

those generated by the full-sized, high-speed profilers to investigate whether the IRI statistic can 

be used as a “cradle-to-grave” statistic for road roughness. 
11 

Figure 7. Relationship between IRI and computer-simulated PI values in 

TTI equipment comparison study (Fernando, 2000). 

The field evaluation was performed at eight sites along I-70 west of Topeka. Each lane (driving 

and passing) at each site was tested with KDOT’s profilograph and full-sized profiler, while the 

remaining profilers tested at only some of the eight sites. At a given site, one run of each 

wheelpath was made with the profilograph, and the average of the two runs was determined and 

reported. For the lightweight and full-sized profilers, three and five runs were made, 

respectively, with both wheelpaths measured and averaged during each run. 

Statistical analysis of the data indicated that the lightweight systems tended to produce 

statistically similar PI0.0 values when compared to the KDOT manual profilograph. It also 

showed similarities in IRI between the KDOT full-sized profiler and three of the four lightweight 

profilers, giving some credence to the “cradle-to-grave” roughness concept. 

The study included correlation analysis between the PIs from the manual profilograph and those 

from the lightweight systems. It also included correlation analysis between the simulated PI and 

IRI values produced by each inertial profiler. Plots of these data are provided in figure 8, which 

also shows the linear IRI–PI0.0 relationship that results when data from all profiling devices are 

considered. 

No correlations were made in the KDOT study between profilograph PI0.0 and inertial profiler 

IRI. However, using data from the report, several such trends have now been developed and are 

illustrated in figure in 9. Each data point in this figure represents the mean smoothness 

(profilograph PI0.0 and profiler IRI) of one lane at one test site. As can be seen, only the IRI data 

IRI = 4.0871*PI(0.2-in) + 52.735

R2

 = 0.5909, N = 48

IRI = 2.1425*PI(0.0-in) + 19.328

R2

 = 0.8532, N = 48

0

20

40

60

80

100

120

0 5 10 15 20 25 30 35

Simulated PI, in/mi

IRI, in/mi

0.2-in blanking band 0.0-in blanking band

Linear (0.2-in blanking band) Linear (0.0-in blanking band)
12 

Figure 8. Plots of IRI and simulated PI0.0 values generated by various profilers 

 in KDOT lightweight profiler comparison study.

Figure 9. Relationship between IRI and California profilograph PI0.0 in KDOT lightweight 

 profiler comparison study. 
13 

taken by two of the lightweight profilers (Ames Lightweight Inertial Surface Analyzer (LISA) 

and ICC) and the KDOT full-sized profiler are represented. The other three profilers collected 

data from only two of the eight sites, which resulted in very limited data sets. 

Illinois DOT Bridge Smoothness Specification Development Study

As part of an effort to develop a preliminary bridge smoothness specification for the Illinois 

DOT (ILDOT), the University of Illinois coordinated a series of bridge smoothness tests in 1999 

using the K.J. Law T6400 lightweight inertial profiler (Rufino et al., 2001). A total of 20 bridges 

in the Springfield, Illinois area were chosen and tested, with each bridge measured for IRI and 

PI5-mm (PI0.2-in). At least one run per wheelpath of the driving lane was made, and each run 

extended from the front approach pavement across the bridge deck to the rear approach 

pavement. 

A correlation analysis of the IRI and simulated PI5-mm (PI0.2-in) values produced by the 

lightweight profiler was performed in the study, which resulted in the graph and linear 

relationship given in figure 10. Unlike other relationships presented earlier in this chapter, this 

relationship covers a larger spectrum of PI values— PI5-mm (PI0.2-in) values largely in the range of 

0.4 to 1.0 m/km (25 to 63 inches/mile)—due to the fact that bridges are often much rougher than 

pavements. 

Figure 10. Relationship between IRI and simulated PI5-mm (PI0.2-in) in ILDOT 

 bridge smoothness study (Rufino et al., 2000). 

IRI = 2.1645*PI0.2-in + 73.657

R2

 = 0.83

0

50

100

150

200

250

0 10 20 30 40 50 60 70 80

Simulated PI0.2-in, in/mi

IRI, in/mi
14 

Summary 

Table 1 summarizes the various regression equations found in the literature relating IRI from an 

inertial profiling system with PI statistics (PI5-mm, PI2.5-mm, and PI0.0) generated by Californiatype profilographs or simulated by inertial profilers. How these various relationships compare 

visually with one another can be seen in figures 11 through 13. Generally speaking, there is 

considerable disparity in the vertical positioning of each trend, but the slopes are rather similar. 

The fact that different pavement types, different roughness ranges, and different pieces of testing 

equipment are represented by the various trends is believed to account in large part for the 

disparities observed. 

Table 1. Summary of documented PI–IRI relationships. 

Study (Year) Pavement 

Types 

No. of Test 

Sections Remarks Linear Regression 

Equation, m/km 

Linear Regression 

Equation, in/mi 

IRI vs. PI5-mm

Manual profilograph PI, 

Laser-type inertial profiler IRI = 4.02*PI + 1.11 IRI = 4.02*PI + 70.13 PTI (1988) AC and PCC 

26 Computerized profilograph PI, 

Laser-type inertial profiler IRI = 2.46*PI + 1.04 IRI = 2.46*PI + 66.22 

 Arizona DOT 

 (1992) 

PCC 12 Computerized profilograph PI, 

Laser-type inertial profiler IRI = 6.10*PI + 0.83 IRI = 6.10*PI + 52.90 

 University of 

 Texas (1992) 

AC and PCC 18 Computerized profilograph PI, 

Manually computed IRI (Dipstick) IRI = 2.83*PI + 1.16 IRI = 2.83*PI + 73.70 

Computerized profilograph PI, 

Laser-type inertial profiler IRI = 3.95*PI + 0.63 IRI = 3.95*PI + 39.93 Florida DOT  (1996) 

AC 

12 Computerized profilograph PI, 

Ultrasonic-type inertial profiler IRI = 3.15*PI + 0.82 IRI = 3.15*PI + 52.20 

 TTI (1996) AC overlays 48 Computer-simulated PI, 

Laser-type inertial profiler IRI = 4.09*PI + 0.83 IRI = 4.09*PI + 52.74 

 Illinois DOT 

 (2000) 

Bridge decks 20 Simulated PI, 

Lightweight inertial profiler IRI = 2.16*PI + 1.16 IRI = 2.16*PI + 73.66 

IRI vs. PI2.5-mm

Computerized profilograph PI, 

Laser-type inertial profiler IRI = 2.73*PI + 0.50 IRI = 2.73*PI + 31.91 Florida DOT 

 (1996) 

AC 

12 Computerized profilograph PI, 

Ultrasonic-type inertial profiler IRI = 2.71*PI + 0.60 IRI = 2.71*PI + 37.97 

IRI vs. PI0.0

 TTI (1996) AC overlays 48 Computer-simulated PI, 

Laser-type inertial profiler IRI = 2.14*PI + 0.31 IRI = 2.14*PI + 19.33 

Computerized profilograph PI, 

Laser-type inertial profiler IRI = 2.19*PI + 0.22 IRI = 2.19*PI + 13.75 Florida DOT 

 (1996) 

AC 

12 Computerized profilograph PI, 

Ultrasonic-type inertial profiler IRI = 2.20*PI + 0.31 IRI = 2.20*PI + 19.36 

Manual profilograph PI, 

Full-sized and lightweight inertial 

profilers 

IRI = 2.00*PI + 0.56 IRI = 2.00*PI + 35.50 

Computer-simulated PI, 

Full-sized inertial profiler IRI = 2.76*PI + 0.36 IRI = 2.76*PI + 22.82 

Computer-simulated PI, 

Lightweight inertial profiler 1 IRI = 2.87*PI + 0.33 IRI = 2.87*PI + 20.92 

 Kansas DOT 

 (1999/2000) 

PCC 

8 

Computer-simulated PI, 

Lightweight inertial profiler 2 IRI = 2.79*PI + 0.31 IRI = 2.79*PI + 19.65 
15 

Figure 11. Graphical illustration of documented PI5-mm–IRI smoothness relationships. 

Figure 12. Graphical illustration of documented PI2.5-mm–IRI smoothness relationships. 

0.00

0.50

1.00

1.50

2.00

2.50

0.00 0.05 0.10 0.15 0.20 0.25

PI5-mm, m/km

IRI, m/k

m

1992 AZDOT (PCC) 1992 Univ. of Texas (PCC)

1996 FLDOT (AC, laser IRI) 1996 FLDOT (AC, ultrasonic IRI)

1996 TTI (ACOL, computer-simulated PI) 1988 PTI (AC & PCC, manual PI)

1988 PTI (AC & PCC, computerized PI) 2000 ILDOT (Bridges, computer-simulated PI)

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

0.00 0.05 0.10 0.15 0.20 0.25

PI2.5-mm, m/km

IRI, m/k

m

1996 FLDOT (AC, laser IRI) 1996 FLDOT (AC, ultrasonic IRI)
16 

Figure 13. Graphical illustration of documented PI0.0–IRI smoothness relationships. 
17

Chapter 3. LTPP Data Collection and Project Database 

Development

Introduction

As mentioned previously, the main thrust of this study involves the comprehensive analysis of 

LTPP smoothness data. Since the time the LTPP program was initiated in 1989, several hundred 

test pavements throughout the country have been tested for smoothness on an annual or biennial 

basis using full-sized, high-speed inertial profilers. In each test, the longitudinal surface profile 

of each wheelpatch was measured and recorded, and from those profiles the IRI of each 

wheelpath was computed and recorded for inclusion in the LTPP Information Management 

System (IMS) database. The sections below describe in detail the collection of LTPP data and 

the development of the project database used to examine the relationship between IRI and PI.

Collection of LTPP Profile Data

To retrieve the profile and smoothness data required for this study, a data request was submitted 

to the LTPP IMS database manager. All 1996 – 2001 archived profile data contained in the 

Ancillary Information Management System (AIMS) and IRI data contained in the IMS were 

requested, covering all LTPP test sections. Data for this time period only were requested, as they 

represented data collected by a specific model of profiling equipment—the 1995 version of the 

K.J. Law T-6600 inertial profiler. Four such profilers were purchased by LTPP in 1996 for use 

by each LTPP Regional Contracting Office (North Central, North Atlantic, Southern, Western).

The 1995 T-6600 profiler is considered a class I accelerometer-established inertial profiling 

reference based on American Society of Testing and Materials (ASTM) E-950-98. It is a vanmounted system containing two infrared sensors spaced 1,676 mm (66 inches) apart. The system 

collects longitudinal profile data at 25.4-mm (1-inch) intervals, and these data are processed 

through a moving-average smoothing filter to generate 152-mm (6-inch) profile data, which are 

subsequently downloaded and stored in the IMS database. Also stored in the IMS database are 

the individual wheelpath IRI values computed from the 152-mm (6-inch) profile data.

The original 25.4-mm (1-in) profile data are also archived, but they are done so in the AIMS 

databases managed by each Regional Contracting Office. Because current automated 

profilographs record profile traces on 32-mm (1.25-inch) intervals, the AIMS profile data 

represent a closer match of the profile traces than the 152-mm (6-inch) IMS profile data. Hence, 

in addition to requesting IRI and relevant test section data (e.g., State ID, SHRP ID, experiment 

number, pavement type, climatic information) contained in the IMS database, all available 25.4-

mm (1-inch) profile data were solicited.
18

Conversion of Profile Data to Simulated PI Values

To model profilograph traces and generate simulated PI values from the AIMS profile data, a 

calibrated software modeling system was used. In 1995, K.J. Law developed software to model 

California-type profilograph traces and output PI values. This software is now used with their 

lightweight profilers to compute PI and IRI. K.J. Law’s lightweight profilers use the same 

vertical elevation sensors that are mounted on the T-6600 profiler, which again has been the 

device used to collect profiles for the LTPP program. Although there are several good 

lightweight profilers and PI modeling systems available, the K.J. Law modeling software was 

selected for this study to provide the most compatibility with the available LTPP profile data.

Using the modeling and index computation software currently installed on their commercial 

lightweight profilers, K.J. Law developed interface for analysis of the LTPP data. Named 

“Indexer,” the software computes PI, IRI, and ride number (RN) values using University of 

Michigan Transportation Research Institute (UMTRI) Engineering Research Department (ERD) 

format input files. The operator can set the blanking band, as well as several other parameters, 

such as the type of smoothing filter (moving average or third-order Butterworth) and the type of 

scallop filter (height, length, rounding).

In this study, the 25.4-mm (1-inch) AIMS profile data were processed into 0.0-, 2.5-, and 5-mm

(0.0-, 0.1-, and 0.2-inch) blanking band PI values (herein designated as PI0.0, PI2.5-mm, and PI5-mm) 

for each profile data set using the K.J. Law Indexer 3.0 software. These simulated PI values 

were computed using a 0.76-m (2.5-ft) moving-average filter, along with minimum height, 

maximum height, and rounding scallop filters settings of 0.9, 0.6, and 0.25 mm (0.035, 0.024, 

and 0.01 inches), respectively.

During the conversion of profile data into simulated PI values, the issue of subsectioning of SPS 

profile data was addressed. Unlike GPS test sites, which serve as individual 152.5-m (500-ft) 

test sections, each SPS test site contains between 3 and 20 test sections comprised of different 

designs, materials, and construction practices. Profile data for each SPS site are collected in one 

pass, and the data are subsectioned only after conversion to 152-mm (6-in) intervals.

To extract 25.4-mm (1-inch) profile data for each SPS test section, a special subsectioning 

program was developed and applied to each continuous SPS test site profile. Each subsectioned 

profile was then processed for IRI, PI0.0, PI2.5-mm, and PI5-mm using the Indexer program. As a 

data quality control measure, each IRI value computed by Indexer was compared with the IRI 

value computed in the field and subsequently reported in the IMS database. All profiler runs that 

showed more than 0.0075 m/km (0.475 inches/mile) difference between the Indexer-computed 

IRI and the IMS database IRI were excluded from the project database.

Populating the Project Database

IRI and relevant test section data obtained from the IMS database were downloaded into 

Microsoft Access, a database management system that provides easy extraction of data into 

spreadsheets and statistical analysis input files. The IRI data consist of right and left wheelpath 
19 

IRI values generated from individual profiler runs conducted on GPS and SPS sites between 

1996 and 2001. 

Simulated PI0.0, PI2.5-mm, and PI5-mm values derived from the 25.4-mm (1-inch) profile data were 

also added to the project database. Moreover, to successfully carry out the data analyses for the 

project, mean IRI and mean simulated PI values were computed from each pair of left- and rightwheelpath smoothness values. The resulting means were then added to the project database. 

A total of 1,793 LTPP test sections located in 47 States and 8 Canadian Provinces formed the 

basis for this evaluation. The sections represent a variety of pavement types, including original 

and restored AC and PCC pavements, asphalt overlays of both AC and PCC pavements, and 

concrete overlays of PCC pavements. They also span all four climatic zones—dry freeze, dry 

nonfreeze, wet freeze, wet nonfreeze—as defined by mean annual precipitation (wet being 

greater than 508 mm [20 inches] of precipitation per year) and mean annual freezing index (FI) 

(freeze being more than 66°C-days [150°F-days] per year). 

Each test section in the database includes IRI and simulated PI values corresponding to 

individual profiler runs made between 1996 and 2001. Breakdowns of the test sections by LTPP 

experiment and by State are provided in tables 2 and 3, respectively. 

Table 2. Breakdown of test sections by LTPP experiment. 

GPS Sections SPS Sections 

Experiment No. No. of LTPP 

Sections Experiment No. No. of LTPP Sections 

GPS-1 

(Conventional AC pavement) 140 SPS-1 

(Structural factors for flexible pavement) 207 

GPS-2 

(Full-depth AC pavement) 77 SPS-2 (Structural factors for rigid pavement) 166 

GPS-3 

(JPC pavement) 120 SPS-3 

(Preventive maintenance of AC pavement) 259 

GPS-4 

(JRC pavement) 50 SPS-4 

(Preventive maintenance of PCC pavement) 72 

GPS-5 

(CRC pavement) 64 SPS-5 

(AC overlays on AC pavement) 152 

GPS-6 

(AC overlays on AC pavement) 169 SPS-6 

(CPR and AC overlays on PCC pavement) 103 

GPS-7 

(AC overlays on PCC pavement) 55 SPS-7 

(Bonded PCC overlays on PCC pavement) 27 

GPS-9 

(Unbonded PCC overlays) 24 SPS-8 

(Environmental effects on AC, PCC pavements) 47 

 SPS-9 

(SHRP AC mix designs) 80 
20 

Table 3. Breakdown of test sections by State. 

State/Province No. of LTPP 

Sections State/Province No. of LTPP 

Sections State/Province No. of LTPP 

Sections 

Alabama 58 Michigan 62 South Dakota 27 

Arizona 81 Minnesota 68 Tennessee 25 

Arkansas 57 Mississippi 38 Texas 156 

California 78 Missouri 79 Utah 31 

Colorado 42 Montana 36 Vermont 4 

Connecticut 7 Nebraska 40 Virginia 29 

Delaware 28 Nevada 37 Washington 41 

Florida 46 New Hampshire 1 West Virginia 5 

Georgia 28 New Jersey 23 Wisconsin 53 

Idaho 22 New Mexico 33 Wyoming 21 

Illinois 36 New York 7 Alberta 16 

Indiana 35 North Carolina 39 British Columbia 4 

Iowa 60 North Dakota 16 Manitoba 20 

Kansas 56 Ohio 41 New Brunswick 3 

Kentucky 15 Oklahoma 55 Newfoundland 2 

Louisiana 13 Pennsylvania 30 Ontario 12 

Maine 17 Rhode Island 1 Quebec 11 

Maryland 21 South Carolina 7 Saskatchewan 17 

Massachusetts 3 
21 

Chapter 4. Development of LTPP-Based Smoothness 

Index Relationships 

Introduction 

Based on a comprehensive review of past model development research, the following procedure 

was utilized in developing LTPP-based PI-to-PI0.0 and PI-to-IRI relationships: 

1. Perform preliminary evaluation of the assembled database, including a detailed check of 

data quality, appropriate data cleaning, and development of comprehensive scatter plots. 

2. Select the most appropriate model form for the smoothness indices relationships. 

Selection will be based on trends observed from the preliminary data analysis and past 

research. 

3. Analyze the results of the preliminary data analysis (bivariate plots) and conduct an 

analysis of variance (ANOVA) to determine the groupings of pavement types, climatic 

regions, and other factors with similar smoothness indices relationships (e.g., no 

significant differences in slopes for linear relationships). 

4. Develop tentative models for the smoothness indices relationships. 

5. Assess tentative models for reasonableness (e.g., assess model diagnostic statistics, such 

as correlation coefficient [R2

] and the standard error of the estimate [SEE]). 

6. Select final models. 

The steps outlined for model development are summarized in the flow chart shown in figure 14 

and are explained in greater detail in the sections that follow. This approach has been used in 

previous research studies and has been improved to provide practical and accurate models. 

Figure 14. Flow chart for developing pavement smoothness models. 

Step 1

Preliminary Evaluation

Step 2

Select Appropriate Model Form

Step 3

Group Data with Similar Trends 

and Smoothness Relations

Step 4

Develop Tentative Models 

Step 5

Assess Tentative Models for Reasonableness

Step 6

Select Final Models

Step 1

Preliminary Evaluation

Step 2

Select Appropriate Model Form

Step 3

Group Data with Similar Trends 

and Smoothness Relations

Step 4

Develop Tentative Models 

Step 5

Assess Tentative Models for Reasonableness

Step 6

Select Final Models
22 

Step 1—Preliminary Evaluation 

In step 1 of model development, the assembled database was examined to determine its general 

properties and to identify possible data anomalies (i.e., outliers, missing or erroneous data). The 

data were “cleaned” as appropriate and then sorted to allow for the development of various PI– 

PI0.0 and PI–IRI scatter plots for use in model development. 

Data Quality Evaluation

Basic statistics, such as the mean and range of data, were used to identify possible gaps in the 

data and to determine whether the database was representative of the expected inference space. 

Of specific interest in this process were the following: 

• The ranges of PI and IRI data to be used in model development and whether those ranges 

were consistent with the purposes for which the data would be used. 

• Climatic regions of the pavements from which data were obtained. 

Figures 15 through 18 present histograms showing the distribution of IRI and PI5-mm for all ACand PCC-surfaced pavements. A detailed summary of the information depicted in the plots 

(categorized by pavement type and climatic region) is provided in table 4. It is clear from these 

exhibits that the data used for analysis (i.e., the cleaned data), and for developing PI–PI0.0 and 

PI–IRI relationships, fully cover the ranges of smoothness typical of new construction and AC 

overlays (i.e., IRI between 800 and 2,000 mm/km [50 and 125 inches/mile], PI5-mm between 0 

and 235 mm/km [0 and 15 inches/mile]). 

Development of Scatter Plots 

As summarized in table 5, 111 scatter plots of IRI versus PI5-mm, PI2.5-mm, and PI0.0, and 111 

scatter plots of PI0.0 versus PI5-mm and PI2.5-mm were produced to aid the model development 

process. The scatter plots represent various combinations of climatic zone and pavement type. 

Complete sets of the scatter plots developed in the study are provided in appendixes A and B. 

At the broadest level, over 14,000 asphalt pavement smoothness data points (representing the 

average roughness of right and left wheelpaths) and over 8,000 concrete pavement data points 

representing all four climatic zones were available for plotting and model development. Figure 

19 shows the PI0.0–IRI scatter plot for all AC pavements, and figure 20 shows the PI5-mm–PI0.0

scatter plot for all PCC pavements. These plots, which are typical of most of the scatter plots, 

show reasonably strong (R2 > 0.75) and virtually linear relationships between the smoothness 

indices. They also, however, illustrate the considerable amount of variation due in large part to 

the inherent differences in the way the smoothness indices process different surface wavelengths. 

Examples of the effects of pavement type and climatic zone on the smoothness relationships can 

be seen in figures 21 and 22. In the case of the PI0.0–IRI trends for different AC pavements 

(figure 21), the differences are almost negligible. Slightly more distinct differences, however, 

are discernible among the PI5-mm–PI0.0 trends representing different climatic zones (figure 22). 
23 

Figure 15. Histogram showing the distribution of IRI data used in model development 

 (all AC pavements). 

Figure 16. Histogram showing the distribution of IRI data used in model development 

 (all PCC pavements). 

0

2000

4000

6000

8000

10000

12000

14000

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 >

5000

IRI, mm/km

Frequency

.00%

20.00%

40.00%

60.00%

80.00%

100.00%

Frequency

Cumulative percent

0

1000

2000

3000

4000

5000

6000

7000

8000

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 >

5000

IRI, mm/km

Frequency

.00%

20.00%

40.00%

60.00%

80.00%

100.00%

Frequency

Cumulative percent
24 

Figure 17. Histogram showing the distribution of PI5-mm data used in model development 

(all AC pavements). 

Figure 18. Histogram showing the distribution of PI5-mm data used in model development 

(all PCC pavements). 

0

2000

4000

6000

8000

10000

12000

30 90 150 210 270 330 390 450 510 570 630 690 >690

PI (5.0 mm blanking band), mm/km

Frequency

.00%

20.00%

40.00%

60.00%

80.00%

100.00%

Frequency

Cumulative percent

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

30 90 150 210 270 330 390 450 510 570 630 690 >690

PI (5.0 mm blanking band), mm/km

Frequency

.00%

20.00%

40.00%

60.00%

80.00%

100.00%

Frequency

Cumulative percent
25 

Table 4. Summary of basic statistics of data used in model development. 

Smoothness Index 

IRI PI0.0 PI2.5-mm PI5-mm

Pavement 

Type Climate N 

Mean Std. Dev. Mean Std. Dev. Mean Std. Dev. Mean Std. Dev. 

DF 2,720 1,252.5 671.5 373.8 216.9 190.3 187.8 91.0 137.6 

DNF 1,740 1,031.4 518.7 297.7 179.1 142.0 139.4 63.8 89.9 

WF 6,502 1,330.3 661.2 450.8 238.7 254.2 215.1 131.8 163.4 

AC 

WNF 4,046 1,186.0 492.6 370.9 181.9 193.5 165.0 92.5 122.4 

DF 1,856 1,397.6 629.0 412.3 218.2 220.4 187.3 106.4 130.8 

DNF 1,502 1,011.1 422.2 274.6 141.9 106.5 103.0 40.1 63.4 

WF 3,832 1,135.8 423.9 342.2 159.6 174.4 140.5 79.5 98.3 

AC/AC 

WNF 1,426 1,125.6 491.2 346.6 192.3 181.9 158.8 91.0 110.4 

DF 90 1,072.4 196.9 336.4 88.2 152.8 115.3 63.2 72.7 

DNF 0 — — — — — — — — 

WF 3,774 1,208.5 444.5 387.1 167.7 181.0 138.2 74.3 89.6 

AC/PC 

WNF 376 1,280.6 461.8 358.4 140.3 163.0 130.1 68.4 100.5 

DF 2,154 1,536.8 545.2 475.2 210.6 226.4 195.8 99.9 139.5 

DNF 1,270 1,464.9 500.1 394.8 175.1 162.1 168.3 68.9 117.8 

WF 6,542 1,639.8 703.4 572.3 294.8 334.2 286.5 180.4 236.1 

JPC a

WNF 2,196 1,737.3 612.7 594.1 228.3 337.7 216.0 161.5 174.1 

DF 0 — — — — — — — — 

DNF 0 — — — — — — — — 

WF 1,950 1,955.7 508.0 780.0 233.1 531.2 231.2 321.9 197.4 

JRC a

WNF 349 2,053.8 349.0 785.4 144.4 546.3 142.8 328.9 129.2 

DF 39 1,330.3 54.0 508.8 25.8 336.3 55.3 160.8 29.0 

DNF 120 1,275.2 397.6 395.0 207.2 180.3 200.7 98.0 142.2 

WF 722 1,575.6 478.0 541.7 202.2 332.9 204.0 181.7 166.7 

CRC a

WNF 358 1,620.7 457.5 563.4 193.3 343.6 188.6 189.2 153.7 

— No data available. 

a

 New construction only. 
26 

Table 5. Matrix of scatter plots created for model development. 

Climatic 

Zone Model All AC AC AC/AC AC/PCC All PCC JPC JRC CRC 

IRI vs. PI0.0 √ √ √ √ √ √ — √

Dry-Freeze IRI vs. PI2.5-mm √ √ √ √ √ √ — √

IRI vs. PI5-mm √ √ √ √ √ √ — √

IRI vs. PI0.0 √ √ √ — √ √ — √

IRI vs. PI2.5-mm √ √ √ — √ √ — √ DryNonfreeze 

IRI vs. PI5-mm √ √ √ — √ √ — √

IRI vs. PI0.0 √ √ √ √ √ √ √ √

Wet-Freeze IRI vs. PI2.5-mm √ √ √ √ √ √ √ √

IRI vs. PI5-mm √ √ √ √ √ √ √ √

IRI vs. PI0.0 √ √ √ √ √ √ √ √

IRI vs. PI2.5-mm √ √ √ √ √ √ √ √ WetNonfreeze 

IRI vs. PI5-mm √ √ √ √ √ √ √ √

IRI vs. PI0.0 √ √ √ √ √ √ √ √

All IRI vs. PI2.5-mm √ √ √ √ √ √ √ √

IRI vs. PI5-mm √ √ √ √ √ √ √ √

PI0.0 vs. PI5-mm √ √ √ √ √ √ — √

Dry-Freeze PI0.0 vs. PI2.5-mm √ √ √ √ √ √ — √

PI2.5-mm vs. PI5-mm √ √ √ √ √ √ — √

PI0.0 vs. PI5-mm √ √ √ — √ √ — √

PI0.0 vs. PI2.5-mm √ √ √ — √ √ — √ DryNonfreeze 

PI2.5-mm vs. PI5-mm √ √ √ — √ √ — √

PI0.0 vs. PI5-mm √ √ √ √ √ √ √ √

Wet-Freeze PI0.0 vs. PI2.5-mm √ √ √ √ √ √ √ √

PI2.5-mm vs. PI5-mm √ √ √ √ √ √ √ √

PI0.0 vs. PI5-mm √ √ √ √ √ √ √ √

PI0.0 vs. PI2.5-mm √ √ √ √ √ √ √ √ WetNonfreeze 

PI2.5-mm vs. PI5-mm √ √ √ √ √ √ √ √

PI0.0 vs. PI5-mm √ √ √ √ √ √ √ √

All PI0.0 vs. PI2.5-mm √ √ √ √ √ √ √ √

PI2.5-mm vs. PI5-mm √ √ √ √ √ √ √ √

— No data available. 
27 

Figure 19. IRI vs. PI0.0 for all AC pavements and climates. 

Figure 20. PI0.0 vs. PI5-mm for all PCC pavements and climates. 

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI0.0, m/km

IRI, m/km

0.0

0.5

1.0

1.5

2.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI5-mm, m/km

Simulated PI0.0, m/km
28 

Figure 21. IRI vs. PI0.0 by AC pavement type for all climates. 

Figure 22. PI0.0 vs. PI5-mm by climate for all PCC pavement types. 

AC

AC/AC AC/PCC

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI0.0, m/km

IRI, m/km

All AC

Dry-Freeze

Dry-Nonfreeze

Wet-Freeze

Wet-Nonfreeze

0.0

0.5

1.0

1.5

2.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI5-mm, m/km

Simulated PI0.0, m/km
29

Figures 23 through 27 provide for a direct visual comparison of the smoothness relationships 

developed in past studies with the LTPP-derived relationships. In each figure, a bandwidth 

envelope centered one standard deviation around the LTPP-derived regression line has been 

transposed over the individual regression lines of the past documented smoothness studies. 

Though the LTPP relationships represent a wide range of smoothness, only the levels typical of 

new construction (IRI < 2.0 m/km [127 inches/mile]) are illustrated in figures 23 through 27.

As can be seen, in most instances, the LTPP regression envelope covers the individual PI–IRI 

relationships. In the case of the PI5-mm–IRI relationships for asphalt (figure 23), the PTI 

relationship and one of the Florida relationships (IRI using ultrasonic profiler) extended outside 

the LTPP envelope. Equipment is likely a contributing factor with respect to the Florida 

relationship, as ultrasonic sensors were used as opposed to the infrared sensors used in the LTPP 

program). And, as noted in the figure, the PTI relationship was based on measurements for both 

AC and PCC pavements.

In the PI5-mm–IRI relationships for concrete (figure 23), the Arizona relationship contrasted 

sharply with the LTPP relationship. The fact that the Arizona relationship was based on 

measurements from only 12 concrete pavement sections may help explain this departure. 

However, other factors, such as sensor type (Arizona used optical sensors), are likely to have 

also contributed to this phenomenon.

Figure 23. Graphical comparison of PI5-mm–IRI smoothness relationships for AC pavements.

0.00

0.50

1.00

1.50

2.00

2.50

0.00 0.05 0.10 0.15 0.20 0.25

PI5-mm, m/km

IRI, m/km

1996 FLDOT (AC, laser IRI) 1996 FLDOT (AC, ultrasonic IRI)

1996 TTI (ACOL, computer-simulated PI) 1988 PTI (AC & PCC, manual PI)

1988 PTI (AC & PCC, computerized PI) 2000 ILDOT (Bridges, computer-simulated PI)

2001 LTPP (All AC)

Band Width Envelope on LTPP Regression Line
30

Figure 24. Graphical comparison of PI5-mm–IRI smoothness relationships for PCC pavements.

Figure 25. Graphical comparison of PI2.5-mm–IRI smoothness relationships for AC pavements.

0.00

0.50

1.00

1.50

2.00

2.50

0.00 0.05 0.10 0.15 0.20 0.25

PI5-mm, m/km

IRI, m/km

1992 AZDOT (PCC) 1992 Univ. of Texas (PCC)

1988 PTI (AC & PCC, manual PI) 1988 PTI (AC & PCC, computerized PI)

2000 ILDOT (Bridges, computer-simulated PI) 2001 LTPP (All PCC)

Band Width Envelope on LTPP Regression Line

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80

2.00

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40

PI2.5-mm, m/km

IRI, m/km

1996 FLDOT (AC, laser IRI) 1996 FLDOT (AC, ultrasonic IRI) 2001 LTPP (All AC)

Band Width Envelope on LTPP Regression Line
31

Figure 26. Graphical comparison of PI0.0–IRI smoothness relationships for AC pavements.

Figure 27. Graphical comparison of PI0.0–IRI smoothness relationships for PCC pavements.

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80

2.00

0.00 0.10 0.20 0.30 0.40 0.50 0.60

PI0.0 , m/km

IRI, m/km

1996 FLDOT (AC, laser IRI) 1996 FLDOT (AC, ultrasonic IRI)

1996 TTI (ACOL, computer-simulated PI) 2001 LTPP (All AC)

Band Width Envelope on LTPP Regression Line
32

Step 2—Selection of Appropriate Model Form

Model development began with the selection of the most suitable functional form that best 

describes the relationship between IRI and PI. As indicated by the scatter plots presented in 

figures A-1 through A-37 and B-1 to B-37 in appendixes A and B, the PI–IRI relationship is 

virtually linear and, thus, a linear function with IRI as the dependent variable and PI as the 

independent variable was adopted.

A similar functional form was selected for developing the PI0.0 versus PI models, as indicated by 

the corresponding scatter plots in appendixes A and B. The linear relationship as shown in the 

figures was true for both AC and PCC surface pavements. The magnitude of the slope, however, 

varied according to pavement type (AC vs. PCC, or Jointed Plain Concrete (JPC) vs. 

Continuously Reinforced Concrete (CRC)) and the climatic region in which the pavement was 

located. The model form selected is shown as equation 1.

IRI = " + $*PIX Eq. 1

where: IRI = International roughness index, mm/km.

PIX = Profile index for blanking band X (X = 0.0, 2.5, or 5.0 mm).

", $ = regression constants.

Step 3—Group Data into Sets with Similar Smoothness Relations

Ideally, a single model could be developed to relate the various smoothness indices (e.g., IRI 

versus PI0.0 for all pavement types, climatic regions). However, unless the influences of different 

climatic zones and pavement types were statistically insignificant, this would result in the 

development of models with low prediction capabilities and the introduction of significant levels 

of error in predicted indices.

On the other hand, developing models for all the different combinations of pavement types (e.g., 

AC, AC/AC, JPC, CRC) would result in the development of a minimum of 144 models, as 

illustrated in table 6. So many models is not only impractical from a user’s point of view, but 

could not be developed with the level of accuracy required, due to the lack of sufficient amounts 

of data in some of the cells in table 6.

For this study, it was deemed important to merge cells within the two main blocks (AC- and 

PCC-surfaced pavements) in table 6 with similar relationships between the different smoothness 

indices. Models were developed for a total of six combinations of smoothness indices as 

follows:
Table 6. Factorial of cells used for model development.

Smoothness Index Relationship Models Block Pavement 

Type

Climatic 

Region IRI vs PI0.0 IRI vs PI2.5-mm IRI vs PI5-mm PI0.0 vs PI2.5-mm PI0.0 vs PI5-mm PI2.5-mm vs PI5-mm

DF 1 2 3 4 5 6

DNF 7 8 9 10 11 12

WF 13 14 15 16 17 18

AC

WNF 19 20 21 22 23 24

DF 25 26 27 28 29 30

DNF 31 32 33 34 35 36

WF 37 38 39 40 41 42

AC/AC

WNF 43 44 45 46 47 48

DF 49 50 51 52 53 54

DNF 55 56 57 58 59 60

WF 61 62 63 64 65 66

Block 1

(AC-surfaced 

pavements)

AC/PC

WNF 67 68 69 70 71 72

DF 73 74 75 76 77 78

DNF 79 80 81 82 83 84

WF 85 86 87 88 89 90

JPC

WNF 91 92 93 94 95 96

DF 97 98 99 100 101 102

DNF 103 104 105 106 107 108

WF 109 110 111 112 113 114

JRC

WNF 115 116 117 118 119 120

DF 121 122 123 124 125 126

DNF 127 128 129 130 131 132

WF 133 134 135 136 137 138

Block 2

(PCC-surfaced 

pavements)

CRC

WNF 139 140 141 142 143 144

33
34

• IRI versus PI0.0.

• IRI versus PI2.5-mm.

• IRI versus PI5.0-mm.

• PI0.0 versus PI5.0-mm.

• PI0.0 versus PI2.5-mm.

• PI2.5-mm versus PI5.0-mm.

Similar relationships between the smoothness indices listed above was defined as cells with the 

same surface type with statistically insignificant differences in mean slope or gradient of a linear 

model developed relating the two given indices. This is shown conceptually in figure 28.

Thus, cells with similar PI–IRI or PI–PI relationships were merged for model development, so as 

to limit the number of models. As shown in table 6, cells were defined according to pavement 

type (e.g., AC, AC/PCC, JRC) and climatic region. For PCC pavements, the categories of 

surface type were limited to JPC, Jointed Reinforced Concrete (JRC), and CRC because there 

were an insufficient number of PCC overlays (e.g., JPC/JPC) to perform a detailed and thorough 

analysis.

The procedures used to compute mean slopes for the smoothness index relationships for each cell 

in table 6 are as follows:

• Develop linear models for each pavement section (with multiple test data within a 

uniform construction event) for the specific smoothness indices. (e.g., for cell 1 in table 

6, the model form IRI = " + $*PI would be used).

• Develop database with all the $’s for each of the cells.

• Compute mean slope (mean values of $) and other relevant statistics, such as standard 

deviation, for each cell.

Figures 29 through 32 show, for both AC- and PCC-surfaced pavements, examples of the 

distribution of slopes for IRI versus PI0.0 and PI0.0 versus PI5-mm.

The next step involved testing for similarities or differences in the mean slopes ($) among cells. 

This analysis was limited to cells within each pavement category, as it was assumed that there 

were differences in slopes between AC and PCC pavements. To determine potential similarities 

or differences among cells, analysis of variance (ANOVA) was performed at the following two 

levels:

• Level 1—Checks for differences in mean slopes for cells within the two blocks.

• Level 2—Merging together statistically similar cells.

Level 1 Analysis

Level 1 analysis involved the following tasks:

• Determining classes and levels of the independent variables (pavement type and climate) 

used to define the cells to be analyzed.
35

Figure 28. Conceptual plot showing relationships of smoothness indices

 within and between cells.

Figure 29. Histogram showing the distribution of slope for IRI versus PI0.0

(for cells in block 1, AC-surfaced pavements).

0

50

100

150

200

250

0.25 0.75 1.25 1.75 2.25 2.75 3.25 3.75 4.25 4.75 5.25 >5.25

Slope of IRI vs PI0.0mm linear relationship

Frequency

.00%

20.00%

40.00%

60.00%

80.00%

100.00%

Frequency

Cumulative percent

Mean slope for cell 1

IRI = "1 + $1*PI0.0

Mean slope for cell 2

IRI = "2 + $2*PI0.0

PI0.0, mm/km

IRI, mm/km

Cell 1 and 2 may be merged if $1 and $2are

not significantly different

Mean slope for cell 1

IRI = "1 + $1*PI0.0

Mean slope for cell 2

IRI = "2 + $2*PI0.0

PI0.0, mm/km

IRI, mm/km

Cell 1 and 2 may be merged if $1 and $2are

not significantly different
36

Figure 30. Histogram showing the distribution of slope for IRI versus PI0.0

(for cells in block 2, PCC-surfaced pavements).

Figure 31. Histogram showing the distribution of slope for PI0.0 versus PI5-mm

 (for cells in block 1, AC-surfaced pavements).

0

10

20

30

40

50

60

70

80

90

100

0.25 0.75 1.25 1.75 2.25 2.75 3.25 3.75 4.25 4.75 5.25 >5.25

Slope of IRI vs PI0.0mm linear relationship

Frequency

.00%

20.00%

40.00%

60.00%

80.00%

100.00%

Frequency

Cumulative percent

0

50

100

150

200

250

0.25 0.75 1.25 1.75 2.25 2.75 3.25 3.75 4.25 4.75 5.25 >5.25

Slope of PI0.0mm vs PI5.0mm linear relationship

Frequency

.00%

20.00%

40.00%

60.00%

80.00%

100.00%

Frequency

Cumulative %
37

Figure 32. Histogram showing the distribution of slope for PI0.0 versus PI5-mm

 (for cells in block 2, PCC-surfaced pavements).

• Developing ANOVA models for evaluating the effects of the independent variable ($) on 

the dependent variables.

• Performing test of hypotheses using the models and assembled data to determine whether 

there were significant differences in mean slopes for the cell under evaluation.

The data used in level 1 analysis were as follows:

• Dependent variable—Slope ($) of IRI–PI and PI–PI relationships computed for each 

pavement type, test section, and for a uniform construction event. A uniform 

construction event implies that for the period for which the IRI–PI and PI–PI slopes are 

computed, no major maintenance or rehabilitation event occurred. Typically, data were 

available for 2 to 4 years. Each test section had approximately 22 data points consisting 

of repeated test runs and time-series data.

• Independent variables—Climate (dry-freeze [DF], dry-nonfreeze [DNF], wet-freeze 

[WF], and wet-nonfreeze [WNF]) and pavement type (3 categories each for blocks 1 and 

2 [block 1—AC, AC/AC, AC/PC; block 2—JPCP, JRCP, CRCP]).

The basic ANOVA type I statistical model was used in analysis. Like other basic regression 

models, it was a linear statistical relation between the independent variables and the dependent 

variable. The model is presented as follows:

$ = γ1 + γ2*CLIMATE + γ3*PVMT Eq. 2

0

20

40

60

80

100

120

140

160

0.25 0.75 1.25 1.75 2.25 2.75 3.25 3.75 4.25 4.75 5.25 > 5.25

Slope of PI0.0mm vs PI5.0mm linear relationship

Frequency

.00%

20.00%

40.00%

60.00%

80.00%

100.00%

Frequency

Cumulative percent
38

where: $ = Slope of PI–IRI or PI–PI linear model.

CLIMATE = Test pavement climate location.

PVMT = Pavement type.

γ1, γ2, γ3 = Regression constants.

The following hypothesis was tested under the level 1 analysis:

• Null hypothesis—The mean slopes from cells A and B are not significantly different 

(HO: :A = :B).

• Alternative hypothesis—The mean slopes from cells A and B are significantly different 

(HA: :A ≠ :B).

Acceptance or rejection of the null hypothesis was accomplished by computing the level of 

significance (p-value) for each of the independent classification variables in equation 2 and 

comparing it to a pre-determined level of significance. For this study, a 95 percent level of 

significance (p-value = 5 percent) was used. Thus, a computed p-value of 0.05 or less would 

cause the null hypothesis to be rejected, whereas a p-value greater than 0.05 would confirm the 

null hypothesis.

The results of the ANOVA are presented in tables 7 and 8 for AC and PCC pavements, 

respectively. The results show that both pavement type and climate had a significant effect on 

the PI–IRI and PI–PI relationships. That is, the ANOVA F-test results indicated that one or more 

of the mean slopes for the different cells in the matrix presented in table 6 were significantly 

different.

Table 7. ANOVA results on the effect of pavement type and climate

 on PI–IRI relationship for AC pavements.

Dependent Variable a Grouping Variable b N

F-Statistic 

Value

Probability > 

F (p-value)

Pavement Type 2,395 2.24 0.1061 cSlope of PI0.0–IRI linear 

relationship Climate 2,395 7.61 0.0001 d

Pavement Type 2,395 5.91 0.0028 dSlope of PI2.5-mm–IRI linear 

relationship Climate 2,395 3.87 0.0089 d

Pavement Type 2,395 0.72 0.4870 eSlope of PI5-mm–IRI linear 

relationship Climate 2,395 0.95 0.4177 e

a

 Computed for each wheelpath within a given pavement section within a uniform construction period.

b

 Pavement type considered—AC, AC/AC, and AC/PCC and climate types—DF, DNF, WF, and WNF.

c

 Borderline significance at the 10 percent significance level.

d

 Significant at the 5 percent significance level.

e

 Not significant.
39

Table 8. ANOVA results on the effect of pavement type and climate

 on PI–IRI relationship for PCC pavements.

Dependent Variable a Grouping Variable b N

F-Statistic 

Value

Probability > 

F (p-value)

Pavement Type 1,123 3.44 0.0630 dSlope of PI0.0–IRI linear 

relationship Climate 1,123 4.82 0.0024 d

Pavement Type 1,123 5.51 0.0190 dSlope of PI2.5-mm–IRI linear 

relationship Climate 1,123 13.68 0.0001 d

Pavement Type 1,119 2.96 0.0850 cSlope of PI5-mm–IRI linear 

relationship Climate 1,119 12.46 0.00014

a

 Computed for each wheelpath within a given pavement section within a uniform construction period.

b

 Pavement type considered—AC, AC/AC, and AC/PCC and climate types—DF, DNF, WF, and WNF.

c

 Borderline significance at the 10 percent significance level.

d

 Significant at the 5 percent significance level.

e

 Not significant.

Level 2 Analysis

Although the ANOVA F-test results listed in tables 7 and 8 indicate significant differences in 

mean slope for the various cells evaluated, they do not show which cells were similar or how the 

cells differed from each other. This information is required in order to merge cells that have 

similar slopes or trends in their PI–IRI and PI–PI relationships, so as to optimize and reduce the 

number of models to be developed.

Duncan’s multiple comparison method in ANOVA was used to group cells with similarities 

among their mean slopes at a 95 percent significance level. Table 9 provides a summary of the 

grouping based on the Duncan’s multiple comparison tests. The final groupings were based not 

only on the results of the statistical analysis, but also on the practicality of the groupings and 

engineering judgment.

Steps 4 and 5—Develop Tentative Models and Assess Models for Reasonableness 

Linear regression models for all of the groupings (merged cells) in table 9 were developed and 

are presented in tables 10 through 13. Each model was verified for accuracy and reasonableness 

by evaluating diagnostic statistics, such as the standard estimate of the error (SEE), coefficient of 

determination (R2), and the number of data points used in model development.

In general, the models appeared to be reasonable. For AC-surfaced pavement models, R2 was 

typically greater than 70 percent, with only 3 out of 33 models having reported R2 values less 

than 70 percent. SEE ranged from 178 to 308 mm/km (11.2 to 19.5 inches/mile) for IRI and 21 

to 79 mm/km (1.3 to 5.0 inches/mile) for PI. These models contained the largest number of data 

points to date for modeling the PI–IRI relationships, ranging from 1,800 to 14,170 data points 

per model.
Table 9. Summary of groupings (merged cells) used for model development.

Models Block Pavement 

Type Climatic Region

IRI vs PI0.0 IRI vs PI2.5-mm IRI vs PI5-mm PI0.0 vs PI2.5-mm PI0.0 vs PI5-mm PI2.5-mm vs PI5-mm

DF 4 5 6

DNF 7 8 9

WF 4 5 6

AC

WNF

1 2 3

7 8 9

DF 10 11 12 13 14 15

DNF 16 17 18 19 20 21

WF

AC/AC

WNF 22 23 24 25 26 27

DF

DNF

WF

Block 1

(AC-surfaced 

pavements)

AC/PC

WNF

28 29 30 31 32 33

DF 34 35 36 37 38 39

DNF 40 41 42 43 44 45

WF 34 35 36 46 47 48

JPC

WNF 49 50 51 52 53 54

DF 34 35 36 37 38 39

DNF 40 41 42 43 44 45

WF 34 35 36 46 47 48

JRC

WNF 49 50 51 52 53 54

DF 34 35 36 37 38 39

DNF 40 41 42 43 44 45

WF 34 35 36 46 47 48

Block 2

(PCC-surfaced 

pavements)

CRC

WNF 49 50 51 52 53 54

 Note: Cells with the same numbers share the same model.

40
41

Table 10. PI-to-IRI index conversion equations and variability indices for AC pavements.

Pavement 

Type Climatea Blanking Band (mm)

Correlation Equation

(IRI = mm/km, PI = mm/km) N SEE R

2

AC 1,2,3,4 0.0 IRI = 2.66543*PI0.0 + 213.01 14,170 200.17 0.89

AC 1,2,3,4 2.5 IRI = 2.97059*PI2.5-mm + 638.74 14,160 231.69 0.86

AC 1,2,3,4 5.0 IRI = 3.78601*PI5-mm + 887.51 13,775 292.26 0.77

AC/AC 1 0.0 IRI = 2.74599*PI0.0 + 265.42 1,854 191.97 0.91

AC/AC 2 0.0 IRI = 2.68169*PI0.0 + 274.67 1,494 184.64 0.81

AC/AC 3,4 0.0 IRI = 2.42295*PI0.0 + 301.90 5,126 178.81 0.84

AC/AC 1 2.5 IRI = 3.12622*PI2.5-mm + 708.56 1,854 230.03 0.87

AC/AC 2 2.5 IRI = 3.33564*PI2.5-mm + 655.67 1,494 246.64 0.66

AC/AC 3,4 2.5 IRI = 2.68324*PI2.5-mm + 660.34 5,126 216.98 0.76

AC/AC 1 5.0 IRI = 4.25316*PI5-mm + 957.80 1,824 288.17 0.79

AC/AC 2 5.0 IRI = 4.39478*PI5-mm + 883.20 1,345 308.23 0.45

AC/AC 3,4 5.0 IRI = 3.42671*PI5-mm + 876.80 4,906 265.85 0.63

AC/PCC 1,2,3,4 0.0 IRI = 2.40300*PI0.0 + 292.93 4,156 205.58 0.79

AC/PCC 1,2,3,4 2.5 IRI = 2.78217*PI2.5-mm + 716.87 4,156 229.68 0.73

AC/PCC 1,2,3,4 5.0 IRI = 3.94665*PI5-mm + 939.22 4,052 259.58 0.65

a Climatic zones: 1=DF, 2=DNF, 3=WF, 4=WNF.

Table 11. PI-to-PI index conversion equations and variability indices for AC pavements.

Pavement 

Type Climatea Correlation Equation(PI = mm/km) N SEE R

2

AC 1,3 PI0.0 = 1.08722*PI2.5-mm + 174.42 5,744 47.73 0.96

AC 1,3 PI0.0 = 1.35776*PI5-mm + 275.48 5,684 83.58 0.88

AC 1,3 PI2.5-mm = 1.28213*PI5-mm + 87.79 5,684 46.62 0.95

AC 2,4 PI0.0 = 1.12338*PI2.5-mm + 152.84 8,418 45.23 0.95

AC 2,4 PI0.0 = 1.46417*PI5-mm + 240.09 8,093 71.73 0.86

AC 2,4 PI2.5-mm = 1.34055*PI5-mm + 73.13 8,093 38.64 0.95

AC/AC 1 PI0.0 = 1.14153*PI2.5-mm + 160.70 1,856 43.41 0.96

AC/AC 1 PI0.0 = 1.56038*PI5-mm + 250.89 1,826 73.74 0.88

AC/AC 1 PI2.5-mm = 1.39462*PI5-mm + 75.55 1,826 40.47 0.95

AC/AC 2 PI0.0 = 1.28067*PI2.5-mm + 138.15 1,496 52.26 0.86

AC/AC 2 PI0.0 = 1.75837*PI5-mm + 222.84 1,347 79.32 0.66

AC/AC 2 PI2.5-mm = 1.52523*PI5-mm + 56.60 1,347 34.14 0.89

AC/AC 3,4 PI0.0 = 1.11926*PI2.5-mm + 145.85 5,128 44.86 0.93

AC/AC 3,4 PI0.0 = 1.45876*PI5-mm + 233.59 4,908 71.53 0.81

AC/AC 3,4 PI2.5-mm = 1.36739*PI5-mm + 71.17 4,908 38.12 0.93

AC/PCC 1,2,3,4 PI0.0 = 1.15412*PI2.5-mm + 177.08 4,158 44.46 0.93

AC/PCC 1,2,3,4 PI0.0 = 1.61123*PI5-mm + 271.11 4,054 71.07 0.81

AC/PCC 1,2,3,4 PI2.5-mm = 1.44895*PI5-mm + 76.83 4,054 36.99 0.93

a

 Climatic zones: 1=DF, 2=DNF, 3=WF, 4=WNF.
42

Table 12. PI-to-IRI index conversion equations and variability indices for PCC pavements.

Pavement 

Type Climatea Blanking Band (mm)

Correlation Equation

(PI = mm/km) N SEE R

2

PCC 1,3 0.0 IRI = 2.12173*PI0.0 + 439.76 12,039 259.63 0.84

PCC 2 0.0 IRI = 2.58454*PI0.0 + 423.09 1,448 176.54 0.88

PCC 4 0.0 IRI = 2.3582*PI0.0 + 317.19 2,888 236.51 0.84

PCC 1,3 2.5 IRI = 2.15316*PI2.5-mm + 947.05 12,039 278.69 0.81

PCC 2 2.5 IRI = 2.5921*PI2.5-mm + 1024.73 1,448 226.53 0.80

PCC 4 2.5 IRI = 2.40731*PI2.5-mm + 888.10 2,888 264.46 0.79

PCC 1,3 5.0 IRI = 2.62558*PI5-mm + 1205.73 11,946 305.96 0.77

PCC 2 5.0 IRI = 3.51673*PI5-mm + 1226.35 1,364 268.70 0.72

PCC 4 5.0 IRI = 2.87407*PI5-mm + 1229.63 2,885 297.37 0.74

a

 Climatic zones: 1=DF, 2=DNF, 3=WF, 4=WNF.

Table 13. PI-to-PI index conversion equations and variability indices for PCC pavements.

Pavement 

Type

Climatea Correlation Equation

(PI = mm/km) N SEE R

2

PCC 1 PI0.0 = 1.39512*PI5-mm + 343.08 2,182 71.19 0.87

PCC 2 PI0.0 = 1.36715*PI5-mm + 313.25 1,366 66.42 0.86

PCC 3 PI0.0 = 1.20723*PI5-mm + 367.91 9,764 86.73 0.91

PCC 4 PI0.0 = 1.19909*PI5-mm + 390.49 2,885 85.19 0.85

PCC 1 PI0.0 = 1.04364*PI2.5-mm + 238.13 2,237 46.91 0.95

PCC 2 PI0.0 = 1.02028*PI2.5-mm + 229.78 1,448 44.34 0.94

PCC 3 PI0.0 = 1.01255*PI2.5-mm + 238.65 9,800 49.98 0.97

PCC 4 PI0.0 = 1.01320*PI2.5-mm + 244.81 2,888 56.94 0.94

PCC 1 PI2.5-mm = 1.36458*PI5-mm + 96.46 2,180 43.27 0.95

PCC 2 PI2.5-mm = 1.38376*PI5-mm + 74.90 1,364 39.84 0.95

PCC 3 PI2.5-mm = 1.20990*PI5-mm + 123.95 9,764 53.62 0.96

PCC 4 PI2.5-mm = 1.212677*PI5-mm + 138.43 2,885 42.99 0.96

a

 Climatic zones: 1=DF, 2=DNF, 3=WF, 4=WNF.

All of the PCC-surfaced pavement models had an R2 greater than 70 percent. SEE ranged from 

177 to 306 mm/km (11.2 to 19.4 inches/mile) for IRI and 21 to 79 mm/km (1.3 to 5.0 

inches/mile) for PI. These models contain the largest number of data points to date for modeling 

the PI–IRI relationships, ranging from 1,366 to 12,039 data points per model.

Step 6—Select Final Models

Fifteen models were developed for the PI–IRI relationships and 18 models were developed for 

the PI–PI relationships for AC-surfaced pavements. For PCC-surfaced pavements, 9 and 12 

models were developed for PI–IRI and PI–PI relationships, respectively. The models were 

developed using a database that represented a reasonable inference space (IRI ranged from 300 
43

to 4,000 mm/km [19 to 253 inches/mile] and PI ranged from 0 to 1,700 mm/km [0 to 108 

inches/mile] for all blanking bands). The number of data points used in model development 

ranged from 1,300 to 14,000.

In general, the models developed were adequate and predicted IRI and PI well. An evaluation of 

diagnostic statistics, such as SEE and R2, showed that there was a good correlation between the 

measured and predicted smoothness indices from the models (R2 was typically > 70 percent) 

with a reasonable level of error (ranged from 34 to 86.7 mm/km [2.1 to 5.5 inches/mile] for PI 

and 177 to 308 [11.2 to 19.5 inches/mile] for IRI).

The models presented in tables 10 through 13 predict the mean smoothness index (IRI or PI) for 

the sample LTPP data used in model development. In this case, the sample means are probably a 

reasonable estimate of means of the population of pavements within the limits of the reference 

data. However, they do not necessarily indicate the range of values within which the true 

population means lies.

The range of values within which the true population mean lies can be obtained by computing a 

confidence interval around the predicted sample mean. The confidence interval for the mean 

provides a range of values around the mean where one can expect the "true" (population) mean 

to be located (with a given level of certainty). Confidence interval can be computed using the 

following equation:

CI = mean + t"/2F Eq. 3

where: CI = Confidence interval.

mean = Predicted smoothness index.

t = Value of t-statistic at a given significance level.

" = Significance level (usually 90 or 95 percent).

F = Model standard error of estimate (SEE).

For example, if the predicted mean IRI (computed using models based on the LTPP data sample) 

is 1,000 mm/km (63.4 inches/mile), and the lower and upper limits at a significance level of 95 

percent are 900 and 1,100 mm/km (57.0 and 69.7 inches/mile) respectively, then it can be 

concluded that there is a 95 percent probability that the population mean is between 900 and 

1,100 mm/km (57.0 and 69.7 inches/mile). If the significance level is set to a smaller value (say 

99 percent), then the interval would become wider thereby increasing the certainty of the 

estimate, and vice versa.

In essence, the larger the sample size, the more reliable will be its mean, and the larger the 

variation (SEE), the less reliable will be the mean. Sample size used for development of both the 

LTPP PI–IRI and PI–PI models ranged from 1,347 to 14,170 data points. These numbers are 

greater than the generally required minimum of 100 and should provide reliable results.

The SEE values associated with the PI–IRI models in tables 10 and 12 ranged from 179 to 292 

mm/km (11.3 to 18 inches/mile). The SEE values associated with the PI–PI models in tables 11 
44

and 13 ranged from 25 to 58 mm/km (1.6 to 3.7 inches/mile). These SEE values are reasonable, 

considering the inherent differences in the way surface wavelengths are processed for IRI and PI.
45 

Chapter 5. Adaptation of LTPP-Based Models to Current State 

Smoothness Specifications 

Introduction 

The vast majority of U.S. highway agencies use smoothness specifications to ensure an adequate 

level of initial smoothness for newly constructed and resurfaced pavements. Smoothness 

specifications typically define the type of equipment and testing procedures to be used to 

measure initial smoothness, the method of identifying significant bumps to be removed, the type 

of smoothness statistics to be computed and reported, and the levels of smoothness required for 

full pay, bonuses, penalties, and corrective work. 

As discussed in chapter 1, most specifications are based on the PI smoothness statistic, as 

measured using a profilograph. Although these specifications differ primarily in terms of PI 

limits for acceptable smoothness and pay adjustment provisions, there are also differences in 

testing procedures and equipment. For instance, the length, location, and timeframe specified for 

testing may be different, as might the responsibility (i.e., contractor vs. agency) for testing. Also, 

there are various makes and models of profilographs (Ames, Cox, and McCracken Californiatype profilographs, Rainhart-type profilograph, and manual or computerized trace reduction), 

and different filters (3rd order Butterworth, 1st order Cox, and moving average), and blanking 

band sizes (0, 2.5, and 5 mm [0, 0.1, 0.2 inches]) that can be applied to compute PI. 

This chapter provides a summary of States’ current AC and PCC smoothness specifications and 

presents the results of an effort to develop recommended IRI smoothness limits that correspond 

to existing specified PI limits. The recommended IRI limits were derived using the PI–IRI 

conversion models developed and reported in chapter 4. 

Overview of State Smoothness Specifications 

In the last 10 years, at least five different national surveys have been conducted to show the 

status of State smoothness specifications. In each of these surveys, about half of the responding 

agencies use a California- or Rainhart-type profilograph for testing new AC pavements, whereas 

slightly more than three-fourths of the agencies use profilographs for new PCC. 

Usage of response-type testing devices (e.g., Mays meter) on AC pavements declined slightly 

during this time, from about 15 percent in the mid 1990s to about 10 percent now. In contrast, 

the use of inertial profilers on AC pavements increased appreciably, from about 6 percent in the 

early 1990s to about 24 percent now. For testing of PCC pavements, the use of response-type 

systems stayed the same (about 2 percent), while the use of inertial profilers increased from 

about 6 percent in 1992 to about 10 percent now. 
46

Tables 14 and 15 list some of the key aspects of current State smoothness specifications, 

including the type of equipment and smoothness index used, the testing interval, and the 

smoothness ranges specified for acceptance, correction, bonus, and penalty. The information 

contained in these tables is based largely on data compiled by the FHWA in 2000 (Rizzo) and on 

inquiries made to selected State agencies.

As can be seen in table 14, 26 of the 50 States and Puerto Rico have a PI-based smoothness 

specification for asphalt pavements. Of these 26 agencies, 21 use the 5-mm (0.2-inch) blanking 

band, 1 uses the 2.5-mm (0.1-inch) blanking band, and 4 use the zero blanking band. 

Collectively, the ranges for full pay are as follows:

• PI5-mm: 0 to 205 mm/km (0 to 13 inches/mile).

• PI2.5-mm: 150 to 505 mm/km (9.5 to 32 inches/mile).

• PI0.0: 161 to 536 mm/km (10.1 to 34 inches/mile).

For concrete pavements (table 15), 42 of the 50 States and Puerto Rico have a PI-based 

smoothness specification. Of these 42 agencies, 1 uses a 7.5-mm (0.3-inch) blanking band, 31 

use the 5-mm (0.2-inch) blanking band, 4 use the 2.5-mm (0.1-inch) blanking band, and 6 use the 

zero blanking band. The collective ranges for full pay are as follows:

• PI7.5-mm: 61 to 100 mm/km (3.9 to 6.3 inches/mile).

• PI5-mm: 0 to 205 mm/km (0 to 13 inches/mile).

• PI2.5-mm: 0 to 250 mm/km (0 to 16 inches/mile).

• PI0.0: 161 to 536 mm/km (10.1 to 34 inches/mile).

Development of Recommended Initial IRI and PI0.0 Levels

To assist agencies in transitioning from their existing PI-type specification to a PI0.0 or IRI 

specification, the LTPP-based correlation models developed and presented in chapter 4 were 

applied to the full-pay PI limits given in tables 14 and 15. For each State with a PI specification, 

the respective correlation model was used to develop best estimates of the full-pay PI0.0 and IRI 

limits for new AC, new PCC, AC overlays on AC, and AC overlays on PCC.

The results of this effort are summarized in tables 16 through 19. Each table lists, for a given 

State, its currently reported PI5-mm, PI2.5-mm, or PI0.0 full-pay smoothness limits, its different 

climate types, and the estimated equivalent PI0.0 and IRI values, computed using the PI–IRI 

model reflective of the State’s predominant climate (highlighted in column 3). These estimated 

equivalent PI0.0 and IRI values can be used as a starting point for developing specifications based 

on one of these two indices.

Because the IRI and PI indices are not exactly correlated, tables 16 through 19 include a 90 

percent standard error of the estimate range for the projected specification limit. This error 

rating should assist specification writers in defining their limits. It also can be used as a basis for 

refining the specification on an ongoing basis.
47 

Table 14. State agency smoothness specifications for asphalt pavements. 

State Testing Device Index Testing 

Interval Bonus Range Full Pay Range Penalty Range Correction Range 

AL California-type 

profilograph PI5-mm

0.16 km a

(0.1 mi) 

<32 mm/km 

(<2 in/mi) 

32 - 63 mm/km 

(2 - 3.9 in/mi) 

64 - 160 mm/km 

(4 - 10 in/mi) 

>160 mm/km 

(>10 in/mi) 

AK — — — — — — — 

AZ GM-type profiler MRN 0.16 km a

(0.1 mi) 

<520 mm/km a

(<33 in/mi) 

520 - 710 mm/km a

(33 - 45 in/mi) 

711 - 1578 mm/km a

(46 - 100 in/mi) 

>1578 mm/km a

(>100 in/mi) 

AR 

California-type 

profilograph, 

lightweight profiler 

PI5-mm

0.2 km 

(0.1 mi) 

≤45 mm/km 

(≤3 in/mi) 

46 - 75 mm/km 

(3.1 - 5 in/mi) 

76 - 110 mm/km 

(5.1 - 7 in/mi) 

>110 mm/km 

(>7 in/mi) 

CA California-type 

profilograph PI5-mm

0.16 km 

(0.1 mi) a — ≤80 mm/km 

(≤5 in/mi) a — >80 mm/km 

(>5 in/mi) a

CO California-type 

profilograph PI2.5-mm

0.15 km 

(0.095 mi) 

≤222 mm/km 

(≤14 in/mi) 

222.1 - 252 mm/km 

(14.1 - 16 in/mi) 

252.1 - 378 mm/km 

(16.1 - 24 in/mi) 

>378 mm/km 

(>24 in/mi) 

CT ARAN inertial profiler IRI 0.16 km a

(0.1 mi) 

<950 mm/km a

 (<60 in/mi) 

950 - 1260 mm/km a

(60 - 80 in/mi) 

1261 - 1894 mm/km a

(80.1 - 120 in/mi) 

>1894 mm/km a

(>120 in/mi) 

DE Rolling straightedge — — — — — — 

FL Rolling straightedge — — — — — — 

GA Inertial profiler IRI 1.6 km 

(1.0 mi) — ≤750 mm/km 

(≤47.5 in/mi) a — >750 mm/km 

(>47.5 in/mi) a

HI — — — — — — — 

ID California-type 

profilograph PI5-mm

0.1 km 

(0.1 mi) — ≤8 mm/0.1km 

(≤0.5 in/0.1mi) — >8 mm/0.1km 

(>0.5 in/0.1mi) 

IL California-type 

profilograph PI5-mm

0.16 km 

(0.1 mi) 

≤8 mm/km 

(≤0.5 in/mi) b

9 - 160 mm/km 

(0.6 - 10 in/mi) 

161 - 235 mm/km 

(10.1 - 15 in/mi) 

>235 mm/km 

(>15 in/mi) 

IN California-type 

profilograph PI5-mm

0.16 km 

(0.1 mi) — ≤30 mm/0.16km 

(≤1.2 in/0.1mi) 

31 - 38 mm/0.16km 

(1.21 - 1.5 in/0.1mi) 

>38 mm/0.16km 

(>1.5 in/0.1mi) 

IA California-type 

profilograph PI5-mm

0.16 km 

(0.1 mi) 

≤48 mm/km 

(≤3 in/mi) 

49 - 110 mm/km 

(3.1 - 7 in/mi) 

111 - 160 mm/km 

(7.1 - 10 in/mi) 

>160 mm/km 

(>10 in/mi) 

KS California-type 

profilograph PI0.0

0.1 km 

(0.1 mi) 

≤160 mm/km 

(≤10 in/mi) 

161 - 475 mm/km 

(10.1 - 30 in/mi) 

476 - 630 mm/km 

(30.1 - 40 in/mi) c

>630 mm/km 

(>40 in/mi) 

KY Inertial profiler RI 1.6 km a

(1.0 mi) RI ≥4.05 3.70 ≤ RI < 4.05 3.45 ≤ RI < 3.70 RI < 3.45 

LA California-type 

profilograph PI5-mm Lot — ≤47 mm/km 

(≤3 in/mi) 

48 - 95 mm/km 

(3.1 - 6 in/mi) 

>95 mm/km 

(>6 in/mi) 

ME Rolling dipstick 

profiler IRI 0.2 km (0.12 mi) 

≤945 mm/km a

(≤60 in/mi) 

946 - 1105 mm/km a

(60.1 - 70 in/mi) 

1106 - 1260 mm/km a

(70.1 - 80 in/mi) 

>1260 mm/km a

(>80 in/mi) 

MD California-type 

profilograph PI5-mm

0.16 km a

(0.1 mi) 

≤63 mm/km a

(≤4.0 in/mi) 

64 - 110 mm/km a

(4.1 - 7 in/mi) 

111 - 190 mm/km a

(7.1 - 12 in/mi) 

>191 mm/km a

(>12 in/mi) 

MA Inertial Profiler IRI 0.2 km 

(0.12 mi) a Percent Within Limits Specification: Upper Spec Limit = 1500 m/km (95 in/mi) 

MI 

California-type 

profilograph or 

GM-type inertial 

profiler 

PI5-mm 

RQI d

0.16 km a

(0.1 mi) 

≤63 mm/km a

(≤4 in/mi) 

or 

RQI < 45 

64 - 158 mm/km a

(4.1 - 10 in/mi) 

or 

45 ≤ RQI ≤ 53 

— 

>158 mm/km a

(>10 in/mi) 

or 

RQI > 53 

MN California-type 

profilograph PI5-mm

0.1 km 

(0.1 mi) 

≤38.7 mm/km 

(≤2.4 in/mi) 

38.8 - 78.9 mm/km 

(2.5 - 5 in/mi) 

79 - 118.3 m/km 

(5.1 - 7.5 in/mi) 

>118.3 mm/km 

(>7.5 in/mi) 

MS California-type 

profilograph PI5-mm

0.16 km a

(0.1 mi) 

≤79 mm/km a

 (≤5 in/mi) 

80 - 110 mm/km a

(5.1 - 7 in/mi) 

111 - 158 m/km a

(7.1 - 10 in/mi) 

>158 mm/km a

(>10 in/mi) 

MO California-type 

profilograph PI0.0

0.1 km 

(0.1 mi) 

≤284 mm/km 

(≤18 in/mi) 

285 - 395 mm/km 

(18.1 - 25 in/mi) 

396 - 711 m/km 

(25.1 - 45 in/mi) 

>712 mm/km 

(>45 in/mi) 
48 

Table 14. State agency smoothness specifications for asphalt pavements (continued). 

State Testing Device Index Testing 

Interval Bonus Range Full Pay Range Penalty Range Correction Range 

MT — — — — — — — 

NE California-type 

profilograph PI5-mm

0.2 km 

(0.1 mi) 

≤75 mm/km 

(≤5 in/mi) 

76 - 110 mm/km 

(5.1 - 7 in/mi) 

111 - 155 mm/km 

(7.1 - 10 in/mi) 

>155 mm/km 

(>10 in/mi) 

NV California-type 

profilograph PI5-mm

0.1 km 

(0.1 mi) — ≤80 mm/km 

(≤5 in/mi) — >80 mm/km 

(>5 in/mi) 

NH GM-type 

inertial profiler RN 0.16 km a

(0.1 mi) Percent Within Limits Specification: Lower Spec Limit = RN = 4.1 

NJ Rolling straightedge — — — — — — 

NM California-type 

profilograph PI5-mm

0.1 km 

(0.1 mi) 

≤65 mm/km 

(≤4 in/mi) 

66 - 80 mm/km 

(4.1 - 5 in/mi) 

81 - 160 m/km 

(5.1 - 10 in/mi) 

>160 mm/km 

(>10 in/mi) 

NC Hearne straightedge CSI 0.76 km 

(0.47 mi) CSI=10,20 CSI=30,40 CSI=11,21,31,41,50, 

51,60,61 — 

ND — — — — — — — 

OH California-type 

profilograph PI5-mm

0.16 km a

(0.1 mi) 

≤63 mm/km a

(≤4 in/mi) 

64 - 110 mm/km a

(4.1 - 7 in/mi) 

111 - 190 m/km a

(7.1 – 12 in/mi) 

>190 mm/km a

(>12 in/mi) 

OK California-type 

profilograph PI5-mm

0.16 km a

(0.1 mi) 

≤79 mm/km a

(≤5 in/mi) 

80 - 110 mm/km a

(5.1 - 7 in/mi) 

111 - 190 m/km a

(7.1 - 12 in/mi) 

>190 mm/km a

(>12 in/mi) 

OR California-type 

profilograph PI5-mm

016 km a

(0.1 mi) 

≤80 mm/km a

(≤5 in/mi) 

81 - 110 mm/km a

(5.1 - 7 in/mi) 

111 - 155 mm/km a

(7.1 - 10 in/mi) 

>155 mm/km a

(>10 in/mi) 

PA California-type 

profilograph PI0.0

0.16 km a

(0.1 mi) 

≤442 mm/km a

(≤28 in/mi) 

443 - 536 mm/km a

(28.1 - 34 in/mi) 

537 - 726 mm/km a

(34.1 - 46 in/mi) 

>726 mm/km a

(>46 in/mi) 

PR California-type 

profilograph PI5-mm

0.16 km a

(0.1 mi) 

≤110 mm/km a

(≤7 in/mi) 

111 - 205 mm/km a

(7.1 - 13 in/mi) — >205 mm/km a(>13 in/mi) 

RI — — — — — — — 

SC Maysmeter MRN 1.6 km a

(1.0 mi) 

≤552 mm/km a

(≤35 in/mi) 

553 - 630 mm/km a

(35.1 - 40 in/mi) 

631 - 868 mm/km a

(40.1 - 55 in/mi) 

>868 mm/km a

(>55 in/mi) 

SD Inertial profiler IRI 0.16 km a

(0.1 mi) 

≤868 mm/km a

(≤55 in/mi) 

869 - 1105 mm/km a

(55.1 - 70 in/mi) 

1106 - 1262 mm/km a

(70.1 - 80 in/mi) 

>1262 mm/km a

(>80 in/mi) 

TN Maysmeter MRN 1.6 km a

(1.0 mi) 

≤315 mm/km a

(≤20 in/mi) 

316 - 475 mm/km a

(20.1 - 30 in/mi) 

476 - 950 mm/km a

(30.1 - 60 in/mi) 

>950 mm/km a

(>60 in/mi) 

TX California-type 

profilograph PI0.0

0.16 km a

(0.1 mi) 

≤237 mm/km a

(≤15 in/mi) 

238 - 315 mm/km a

(15.1 - 20 in/mi) 

316 - 630 m/km a

(20.1 - 40 in/mi) 

>630 mm/km a

(>40 in/mi) 

UT California-type 

profilograph PI5-mm

0.2 km 

(0.12 mi) a — ≤110 mm/km 

(≤7 in/mi) a — >110 mm/km 

(>7 in/mi) a

VT Maysmeter IRI 0.32 km a

(0.2 mi) 

<950 mm/km a

(<60 in/mi) 

950 - 1090 mm/km a

(60 - 69 in/mi) 

1091 - 1500 mm/km a

(70 - 95 in/mi) 

>1500 mm/km a

(>95 in/mi) 

VA South Dakota-type 

profiler IRI 0.16 km a

(0.1 mi) 

≤868 mm/km a

(≤55 in/mi) 

869 - 1105 mm/km a

(55.1 - 70 in/mi) 

1106 - 1578 km a

(70.1 - 100 in/mi) 

>1578 mm/km a

(>100 in/mi) 

WAe Lightweight inertial 

profiler IRI 0.1 km (0.1 mi) a

≤946 mm/km a

(≤60 in/mi) 

947 - 1500 mm/km a

(60.1 - 95 in/mi) 

1501 - 1815 mm/km a

(95.1 - 115 in/mi) 

>1815 mm/km a

(>115 in/mi) 

WV Maysmeter or inertial 

profiler MRN 0.16 km 

(0.1 mi) — ≤1000 mm/km 

(≤65 in/mi) 

1001 - 1500 mm/km 

(66 - 97.5 in/mi) 

>1500 mm/km 

(>97.5 in/mi) 

WI California-type 

profilograph PI5-mm

0.16 km a

(0.1 mi) — ≤158 mm/km a

(≤10 in/mi) 

159 - 237 m/km a

(10.1 - 15 in/mi) 

>237 mm/km a

(>15 in/mi) 

WY Inertial profiler IRI 0.16 km a

(0.1 mi) Statistical Based Specification: Full Pay ≈ 868-1105 mm/km (55-70 in/mi) 

a

 Limits are a direct English-Metric conversion from counterpart limits. Actual limits given by the Agency were not available.

b

 Based on average profile index for entire project. 

c

 For PI between 476 mm/km (30.1 in/mi) and 630 mm/km (40 in/mi), must also grind to 475 mm/km (30 in/mi) or below. 

d

 RQI: Ride quality index. 

e

 Draft specification. 
49 

Table 15. State agency smoothness specifications for concrete pavements. 

State Testing Device Index Testing 

Interval Bonus Range Full Pay Range Penalty Range Correction Range

AL California-type 

profilograph PI5-mm

0.16 km 

(0.1 mi) 

<45 mm/km 

(<3 in/mi) 

45 - 94 mm/km 

(3 - 5.9 in/mi) 

95 - 160 mm/km 

(6 - 10 in/mi) 

>160 mm/km 

(>10 in/mi) 

AK — — — — — — — 

AZ California-type 

profilograph PI5-mm

0.16 km a

(0.1 mi) 

<110 mm/km a

(<7 in/mi) 

110 - 142 mm/km a

(7 - 9 in/mi) — >142 mm/km a(>9 in/mi) 

AR 

California-type 

profilograph, 

lightweight profiler 

PI5-mm

0.2 km 

(0.1 mi) 

≤90 mm/km 

(≤6 in/mi) 

91 - 110 mm/km 

(6.1 - 7 in/mi) — >110 mm/km 

(>7 in/mi) 

CA California-type 

profilograph PI5-mm

0.1 km 

(0.06 mi) a — ≤110 mm/km 

(≤7 in/mi) a — >110 mm/km 

(>7 in/mi) a

CO California-type 

profilograph PI2.5-mm

0.15 km 

(0.095 mi) 

≤222 mm/km a

(≤14 in/mi) 

222.1 - 252 mm/km a

(14.1 - 16 in/mi) 

252.1 - 378 mm/km a

(16.1 - 24 in/mi) 

>378 mm/km a

(>24 in/mi) 

CT California-type 

profilograph PI5-mm

0.15 km 

(0.1 mi) a

≤160 mm/km 

(≤10 in/mi) a

161 - 190 mm/km 

(10.1 - 12 in/mi) a

191 - 315 mm/km 

(12.1 - 20 in/mi) a

>315 mm/km 

(>20 in/mi) a

DE CA profilograph or 

rolling straightedge PI5-mm

0.16 km a

(0.1 mi) 

<50 mm/km a

(<3.2 in/mi) 

50 - 200 mm/km a

(3.2 - 12.7 in/mi) — >200 mm/km a(>12.7 in/mi) 

FL California-type 

profilograph PI5-mm

0.1 km 

(0.1 mi) 

≤80 mm/km 

(≤5 in/mi) 

81 - 95 mm/km 

(5.1 - 6 in/mi) 

96 - 110 mm/km 

(6.1 - 7 in/mi) 

>110 mm/km 

(>7 in/mi) 

GA Rainhart profilograph PI2.5-mm

0.4 km a

(0.25 mi) — ≤110 mm/km a

(≤7 in/mi) — >110 mm/km a

(>7 in/mi) 

HI California-type 

profilograph PI5-mm

0.16 km a

(0.1 mi) — ≤157 mm/km a

(≤10 in/mi) 

158 - 236 mm/km a

(10.1 - 15 in/mi) 

>236 mm/km a

(>15 in/mi) 

ID California-type 

profilograph PI5-mm

0.1 km 

(0.1 mi) — ≤8 mm/0.1km 

(≤0.5 in/0.1mi) — >8 mm/0.1km 

(>0.5 in/0.1mi) 

IL California-type 

profilograph PI5-mm

0.16 km 

(0.1 mi) 

≤67 mm/km 

(≤4.25 in/mi) b

68 - 160 mm/km 

(4.26 - 10 in/mi) 

161 - 235 mm/km 

(10.01 - 15 in/mi) 

>235 mm/km 

(>15 in/mi) 

IN California-type 

profilograph PI5-mm

0.16 km 

(0.1 mi) 

≤23mm/0.16km 

(≤0.9 in/0.1mi) 

23 - 25 mm/0.16km 

(0.9 - 1.0 in/0.1mi) — >25 mm/0.16km (>1.0 in/0.1mi) 

IA California-type 

profilograph PI5-mm

0.16 km 

(0.1 mi) 

≤48 mm/km 

(≤3 in/mi) 

49 - 110 mm/km 

(3.1 - 7 in/mi) 

111 - 160 mm/km 

(7.1 - 10 in/mi) 

>160 mm/km 

(>10 in/mi) 

KS California-type 

profilograph PI0.0

0.1 km 

(0.1 mi) 

≤285 mm/km 

(≤18 in/mi) 

286 - 475 mm/km 

(18.1 - 30 in/mi) 

476 - 630 mm/km 

(30.1 - 40 in/mi) c

>630 mm/km 

(>40 in/mi) 

KY Rainhart profilograph 

and inertial profiler 

PI2.5-mm 

RI 

0.3 km a

(0.19 mi) RI ≥4.05 ≤125 mm/km a

(≤8 in/mi) 

126 - 190 mm/km a

(8.1 - 12 in/mi) 

>190 mm/km a

(>12 in/mi) 

LA California-type 

profilograph PI5-mm Lot — ≤94 mm/km 

(≤6 in/mi) 

95 - 126 mm/km 

(6.1 - 8 in/mi) 

>126 mm/km 

(>8 in/mi) 

ME — — — — — — — 

MD California-type 

profilograph PI5-mm

0.16 km a

(0.1 mi) 

≤63 mm/km a

(≤4.0 in/mi) 

64 - 110 mm/km a

(4.1 - 7 in/mi) 

111 - 190 mm/km a

(7.1 - 12 in/mi) 

>191 mm/km a

(>12 in/mi) 

MA — — — — — — — 

MI 

California-type 

profilograph or 

GM-type inertial 

profiler 

PI5-mm 

RQI d

0.16 km a

(0.1 mi) 

≤63 mm/km a

(≤4 in/mi) 

or 

RQI < 45 

64 - 158 mm/km a

(4.1 - 10 in/mi) 

or 

45 ≤ RQI ≤ 53 

— 

>158 mm/km a

(>10 in/mi) 

or 

RQI > 53 

MN California-type 

profilograph PI5-mm

0.16 km 

(0.1 mi) 

≤63 mm/km 

(≤4 in/mi) 

64 - 94 mm/km 

(4.1 - 6 in/mi) 

95 - 126 m/km 

(6.1 - 8 in/mi) 

>126 mm/km 

(>8 in/mi) 

MS California-type 

profilograph PI5-mm

0.16 km a

(0.1 mi) — ≤110 mm/km a

(≤7 in/mi) 

111 - 190 m/km a

(7.1 - 12 in/mi) 

>190 mm/km a

(>12 in/mi) 

MO California-type 

profilograph PI0.0

0.1 km 

(0.1 mi) 

≤284 mm/km 

(≤18 in/mi) 

285 - 395 mm/km 

(18.1 - 25 in/mi) 

396 - 711 m/km 

(25.1 - 45 in/mi) 

>712 mm/km 

(>45 in/mi) 
50 

Table 15. State agency smoothness specifications for concrete pavements (continued). 

State Testing Device Index Testing 

Interval Bonus Range Full Pay Range Penalty Range Correction Range

MT California-type 

profilograph PI5-mm

0.16 km a

(0.1 mi) 

≤94 mm/km a

(≤6 in/mi) 

95 - 158 mm/km a

(6.1 - 10 in/mi) 

159 - 237 m/km a

(10.1 - 15 in/mi) 

>237 mm/km a

(>15 in/mi) 

NE California-type 

profilograph PI5-mm

0.2 km 

(0.1 mi) 

≤75 mm/km 

(≤5 in/mi) 

76 - 155 mm/km 

(5.1 - 10 in/mi) 

156 - 230 mm/km 

(10.1 - 15 in/mi) 

>230 mm/km 

(>15 in/mi) 

NV California-type 

profilograph PI5-mm

0.1 km 

(0.1 mi) — ≤80 mm/km 

(≤5 in/mi) — >80 mm/km 

(>5 in/mi) 

NH — — — — — — — 

NJ Rolling straightedge — — — — — — 

NM California-type 

profilograph PI5-mm

0.1 km 

(0.1 mi) 

≤80 mm/km 

(≤5 in/mi) 

81 - 110 mm/km 

(5.1 - 7 in/mi) 

111 - 190 m/km 

(7.1 - 12 in/mi) 

>190 mm/km 

(>12 in/mi) 

NY California-type 

profilograph PI5-mm

0.16 km 

(0.1 mi) a

≤79 mm/km a

(≤5 in/mi) 

80 - 190 mm/km a

(5.1 - 12 in/mi) — >190/km a(>12 in/mi) 

NC Rainhart profilograph PI5-mm

0.18 km a

(0.11 mi) — ≤63 mm/km a

(≤4 in/mi) — >63 mm/km a

(>4 in/mi) 

ND California-type 

profilograph PI5-mm

0.16 km a

(0.1 mi) 

<8mm/0.16km a

(<0.3 in/0.1mi) 

8 - 13 mm/0.16km a

(0.3 - 0.5 in/0.1mi) 

14 - 23 mm/0.16km a

(0.51 - 0.9 in/0.1mi) 

>23 mm/0.16km a

(>0.9 in/0.1mi) 

OH California-type 

profilograph PI5-mm

0.16 km a

(0.1 mi) 

≤78 mm/km a

(≤5 in/mi) 

79 - 110 mm/km a

(5.1 - 7 in/mi) 

111 - 190 m/km a

(7.1 - 12 in/mi) 

>190 mm/km a

(>12 in/mi) 

OK California-type 

profilograph PI5-mm

0.16 km a

(0.1 mi) 

≤79 mm/km a

(≤5 in/mi) 

80 - 110 mm/km a

(5.1 - 7 in/mi) 

111 - 190 m/km a

(7.1 - 12 in/mi) 

>190 mm/km a

(>12 in/mi) 

OR California-type 

profilograph PI5-mm

0.2 km 

(0.1 mi) a

≤80 mm/km 

(≤5 in/mi) a

81 - 110 mm/km 

(5.1 - 7 in/mi) a — >110 mm/km 

(>7 in/mi) a

PA California-type 

profilograph PI0.0

0.16 a

(0.1 mi) 

≤568 mm/km a

(≤36 in/mi) — — >568 mm/km a

(>36 in/mi) 

PR California-type 

profilograph PI5-mm

0.16 km a

(0.1 mi) 

≤110 mm/km a

(≤7 in/mi) 

111 - 205 mm/km a

(7.1 - 13 in/mi) — >205 mm/km a(>13 in/mi) 

RI — — — — — — — 

SC Rainhart profilograph PI5-mm

0.4 km a

(0.25 mi) — ≤158 mm/km a

(≤10 in/mi) — >158 mm/km a

(>10 in/mi) 

SD California-type 

profilograph PI0.0

0.1 km 

(0.1 mi) 

≤395 mm/km 

(≤25 in/mi) 

396 - 550 mm/km 

(25.1 - 35 in/mi) 

551 - 630 mm/km 

(35.1 - 40 in/mi) 

>630 mm/km 

(>40 in/mi) 

TN Rainhart 

profilograph PI2.5-mm

0.1 km 

(0.1 mi) — ≤160 mm/km 

(≤10 in/mi) 

161 - 235 mm/km 

(10.1 - 15 in/mi) 

>235 mm/km 

(>15 in/mi) 

TX California-type 

profilograph PI0.0

0.16 km a

(0.1 mi) 

≤237 mm/km a

(≤15 in/mi) 

238 - 315 mm/km a

(15.1 - 20 in/mi) 

316 - 630 m/km a

(20.1 - 40 in/mi) 

>630 mm/km a

(>40 in/mi) 

UT California-type 

profilograph PI5-mm

0.2 km 

(0.12 mi) a — ≤110 mm/km 

(≤7 in/mi) a — >110 mm/km 

(>7 in/mi) a

VT — — — — — — — 

VA South Dakota-type 

profiler IRI 0.16 km a

(0.1 mi) 

≤946 mm/km a

(≤60 in/mi) 

947 - 1262 mm/km a

(60.1 - 80 in/mi) 

1263 - 1578 km a

(80.1 - 100 in/mi) 

>1578 mm/km a

(>100 in/mi) 

WA California-type 

profilograph PI7.5-mm

0.1 km 

(0.1 mi) a

≤60 mm/km 

(≤3.8 in/mi) a

61 - 100 mm/km 

(3.9 - 6.3 in/mi) a

>100 mm/km 

(>6.3 in/mi) a,e — 

WV Maysmeter or inertial 

profiler MRN 0.16 km (0.1 mi) — ≤1000 mm/km 

(≤65 in/mi) 

1001 - 1500 mm/km 

(66 - 97.5 in/mi) 

>1500 mm/km 

(>97.5 in/mi) 

WI California-type 

profilograph PI0.01-in

0.16 km a

(0.1 mi) 

≤400 mm/km a

(≤25.3 in/mi) 

401 - 700 mm/km a

(25.4 - 44.3 in/mi) 

701 - 800 m/km a

(44.4 - 50.7 in/mi) f

>800 mm/km a

(>50.7 in/mi) 

WY California-type 

profilograph PI5-mm Sublot Perf. Related Spec (PCC thickness, strength, smoothness) >80 mm/km (>5.0 in/mi) 

a

 Limits are a direct English-Metric conversion from counterpart limits. Actual limits given by the Agency were not available.

b

 Based on average profile index for entire project. 

c

 For PI between 476 mm/km (30.1 in/mi) and 630 mm/km (40 in/mi), must also grind to 475 mm/km (30 in/mi) or below. 

d

 RQI: Ride quality index. 

e

 For PI greater than 100 mm/km (6.3 in/mi), must also grind to 100 mm/km (6.3 in/mi) or less. 

f

 For PI greater than 700 mm/km (44.3 in/mi), must also grind to 700 mm/km (44.3 in/mi) or less. 
51 

Table 16. Estimated equivalent PI0.0 and IRI values for PI-based smoothness specifications 

 for new AC pavement. 

Estimated PI0.0 Estimated IRI 

Agency 

Existing FullPay Range, 

mm/km 

Climatic Zone Full-Pay Range, 

mm/km 

SEE, 

mm/km a

Full-Pay Range, 

mm/km 

SEE, 

mm/km a

PI5-mm Specification 

AL 32 - 63 WNF 287 - 332 72 1009 - 1126 292 

AR 46 - 75 WF,WNF 307 - 350 72 1062 - 1171 232 

CA ≤ 80 DNF,WF,WNF ≤ 357 72 ≤ 1190 292 

ID ≤ 80 b DF,WF ≤ 384 84 ≤ 1190 292 

IL 9 - 160 WF 288 - 493 84 922 - 1493 292 

IN ≤ 187 c WF ≤ 529 84 ≤ 1595 292 

IA 49 - 110 WF 342 - 425 84 1073 - 1304 292 

LA ≤ 47 WNF ≤ 309 72 ≤ 1065 292 

MD 64 - 110 WF 362 - 425 84 1130 - 1304 292 

MI 64 - 158 WF 362 - 490 84 1130 - 1486 292 

MN 38.8 - 78.9 WF 328 - 383 84 1035 - 1187 292 

MS 80 - 110 WNF 357 - 401 72 1190 - 1304 292 

NE 76 - 110 DF,WF 379 - 425 84 1175 - 1304 292 

NV ≤ 80 DF < 384 84 < 1190 292 

NM 66 - 80 DF,DNF 337 - 357 84 1137 - 1190 292 

OH 64 - 110 WF 362 - 425 84 1130 - 1304 292 

OK 80 - 110 DF,WF,WNF 357 - 401 72 1190 - 1304 292 

OR 81 -110 DF,WNF 385 - 425 84 1194 - 1304 292 

PR 111 - 205 WNF 403 - 540 72 1308 - 1664 292 

UT ≤ 110 DF,WF < 425 84 < 1304 292 

WI ≤ 158 WF < 490 84 < 1486 292 

PI2.5-mm Specification

CO 222.1 - 252 DF,WF 415 - 446 48 1295 - 1381 232 

PI0.0 Specification

KS 161 - 475 DF,WF 642 - 1479 200 

MO 285 - 395 WF 973 - 1266 200 

PA 443 - 536 WF 1394 - 1642 200 

TX 238 - 315 DF,DNF,WF,WNF 847 - 1053 200 a

 SEE = Standard error of the estimate. Range of values with 90 percent confidence. 

b

 Extrapolated from actual specification, which calls for PI ≤ 8 mm per 0.1 km. c

 Extrapolated from actual specification, which calls for PI ≤ 30 mm per 0.16 km. 
52 

Table 17. Estimated equivalent PI0.0 and IRI values for PI-based smoothness specifications 

 for AC overlays on AC pavement. 

Estimated PI0.0 Estimated IRI 

Agency 

Existing FullPay Range, 

mm/km 

Climatic Zone Full-Pay Range, 

mm/km 

SEE, 

mm/km a

Full-Pay Range, 

mm/km 

SEE, 

mm/km a

PI5-mm Specification 

AL 32 - 63 WNF 280 - 325 72 986 - 1093 266 

AR 46 - 75 WF,WNF 301 - 343 72 1034 - 1134 217 

CA ≤ 80 DNF,WF,WNF < 364 79 < 1235 308 

ID ≤ 80 b DF,WF < 376 74 < 1298 288 

IL 9 - 160 WF 247 - 467 72 908 - 1425 266 

IN ≤ 187 c WF < 506 72 < 1518 266 

IA 49 - 110 WF 305 - 394 72 1045 - 1254 266 

LA ≤ 47 WNF < 302 72 < 1038 266 

MD 64 - 110 WF 327 - 394 72 1096 - 1254 266 

MI 64 - 158 WF 327 - 464 72 1096 - 1418 266 

MN 38.8 - 78.9 WF 290 - 349 72 1010 - 1148 266 

MS 80 - 110 WNF 350 - 394 72 1151 - 1254 266 

NE 76 - 110 DF,WF 369 - 423 74 1281 - 1426 288 

NV ≤ 80 DF < 376 74 < 1298 288 

NM 66 - 80 DF,DNF 339 - 364 79 1173 - 1235 308 

OH 64 - 110 WF 327 - 394 72 1096 - 1254 266 

OK 80 - 110 DF,WF,WNF 350 - 394 72 1151 - 1254 266 

OR 81 -110 DF,WNF 377 - 423 74 1302 - 1426 288 

PR 111 - 205 WNF 396 - 533 72 1257 - 1579 266 

UT ≤ 110 DF,WF < 423 74 < 1426 288 

WI ≤ 158 WF < 464 72 < 1418 266 

PI2.5-mm Specification

CO 222.1 - 252 DF,WF 413 - 446 43 1399 - 1490 230 

PI0.0 Specification

KS 161 - 475 DF,WF 708 - 1570 191 

MO 285 - 395 WF 992 - 1259 179 

PA 443 - 536 WF 1375 - 1601 179 

TX 238 - 315 DF,DNF,WF,WNF 913 - 1119 185 a

 SEE = Standard error of the estimate. Range of values with 90 percent confidence. 

b

 Extrapolated from actual specification, which calls for PI ≤ 8 mm per 0.1 km. c

 Extrapolated from actual specification, which calls for PI ≤ 30 mm per 0.16 km. 
53 

Table 18. Estimated equivalent PI0.0 and IRI values for PI-based smoothness specifications 

 for AC overlays on PCC pavement. 

Estimated PI0.0 Estimated IRI 

Agency 

Existing FullPay Range, 

mm/km 

Climatic Zone Full-Pay Range, 

mm/km 

SEE, 

mm/km a

Full-Pay Range, 

mm/km 

SEE, 

mm/km a

PI5-mm Specification 

AL 32 - 63 WNF 323 - 373 71 1066 - 1188 260 

AR 46 - 75 WF,WNF 345 - 392 71 1121 - 1235 260 

CA ≤ 80 DNF,WF,WNF < 400 71 < 1255 260 

ID ≤ 80 b DF,WF < 400 71 1255 260 

IL 9 - 160 WF 286 - 529 71 975 - 1571 260 

IN ≤ 187 c WF < 572 71 < 1677 260 

IA 49 - 110 WF 350 - 448 71 1133 - 1373 260 

LA ≤ 47 WNF < 347 71 < 1125 260 

MD 64 - 110 WF 374 - 448 71 1192 - 1373 260 

MI 64 - 158 WF 374 - 526 71 1192 - 1563 260 

MN 38.8 - 78.9 WF 334 - 398 71 1093 - 1251 260 

MS 80 - 110 WNF 400 - 448 71 1255 - 1373 260 

NE 76 - 110 DF,WF 394 - 448 71 1239 - 1373 260 

NV ≤ 80 DF < 400 71 < 1255 260 

NM 66 - 80 DF,DNF 377 - 400 71 1200 - 1255 260 

OH 64 - 110 WF 374 - 448 71 1192 - 1373 260 

OK 80 - 110 DF,WF,WNF 400 - 448 71 1255 - 1373 260 

OR 81 -110 DF,WNF 402 - 448 71 1259 - 1373 260 

PR 111 - 205 WNF 450 - 601 71 1377 - 1748 260 

UT ≤ 110 DF,WF < 448 71 < 1373 260 

WI ≤ 158 WF < 526 71 < 1563 260 

PI2.5-mm Specification

CO 222.1 - 252 DF,WF 432 - 466 45 1332 - 1412 230 

PI0.0 Specification

KS 161 - 475 DF,WF 680 - 1434 206 

MO 285 - 395 WF 978 - 1242 206 

PA 443 - 536 WF 1357 - 1581 206 

TX 238 - 315 DF,DNF,WF,WNF 865 - 1050 206 a

 SEE = Standard error of the estimate. Range of values with 90 percent confidence. 

b

 Extrapolated from actual specification, which calls for PI ≤ 8 mm per 0.1 km. c

 Extrapolated from actual specification, which calls for PI ≤ 30 mm per 0.16 km. 
54 

Table 19. Estimated equivalent PI0.0 and IRI values for PI-based smoothness specifications 

 for new PCC pavement and PCC overlays. 

Estimated PI0.0 Estimated IRI 

Agency 

Existing FullPay Range, 

mm/km 

Climatic Zone Full-Pay Range, 

mm/km 

SEE, 

mm/km a

Full-Pay Range, 

mm/km 

SEE, mm/km a

PI5-mm Specification 

AL 45 - 94 WNF 444 - 503 85 1359 - 1500 297 

AZ 110 - 142 DF,DNF 464 - 507 66 1613 - 1726 269 

AR 91 - 110 WF, WNF 500 - 522 85 1491 - 1546 297 

CA ≤ 110 DNF,WF,WNF ≤ 464 66 ≤ 1613 269 

CT 161 - 190 WF 562 - 597 87 1628 - 1705 306 

DE 50 - 200 WF 428 - 609 87 1337 - 1731 306 

FL 81 - 95 WNF 382 - 587 66 1402 - 1930 269 

HI ≤ 157 WNF ≤ 579 85 ≤ 1681 297 

ID ≤ 80 b DF,WF ≤ 445 71 ≤ 1416 306 

IL 68 - 160 WF 450 - 561 87 1384 - 1626 306 

IN 144 - 156 c WF 542 - 556 87 1584 - 1615 306

IA 49 - 110 WF 427 - 501 87 1334 - 1495 306

LA ≤ 94 WNF ≤ 503 85 ≤ 1500 297

MD 64 - 110 WF 445 - 501 87 1374 - 1495 306

MI 64 - 158 WF 445 - 559 87 1374 - 1621 306

MN 64 - 94 WF 445 - 481 87 1374 - 1453 306

MS ≤ 110 WNF ≤ 522 85 ≤ 1546 297

MT 95 - 158 WF 483 - 559 87 1455 - 1621 306

NE 76 - 155 DF,WF 449 - 559 71 1405 - 1613 306

NV ≤ 80 DF ≤ 455 71 ≤ 1416 306

NM 81 - 110 DF,DNF 424 - 464 66 1511 - 1613 269

NY 80 - 190 WF 464 - 597 87 1416 - 1705 306

NC ≤ 63 WF,WNF ≤ 466 85 ≤ 1411 297

ND 50 - 81 d DF,WF 413 - 456 71 1337 - 1418 306

OH 79 - 110 WF 463 - 501 87 1413 - 1495 306

OK 80 - 110 DNF,WF,WNF 486 - 522 85 1460 - 1546 297

OR 81 -110 DF,WNF 456 -497 71 1418 - 1495 306

PR 111 - 205 WNF 524 - 636 85 1549 - 1819 297

SC ≤ 158 WNF ≤ 580 85 ≤ 1684 297 

UT ≤ 110 DF,WF ≤ 497 71 ≤ 1495 306

WY ≤ 80 DF ≤ 455 71 ≤ 1416 306

PI2.5-mm Specification

CO 222.1 - 252 DF,WF 469 -499 47 1423 - 1485 279

GA ≤ 110 WF,WNF ≤ 469 57 ≤ 1420 264 

KY ≤ 125 WF ≤ 365 50 ≤ 1216 279 

TN ≤ 160 WF,WNF ≤ 407 57 ≤ 1273 317 

PI0.0 Specification

KS 286 - 475 DF,WF 1047 - 1448 306

MO 285 - 395 WF 1044 - 1278 306

PA 568 e WF ≤ 1645 306

TX 396 - 550 DF,DNF,WF,WNF 1038 - 1237 269

WI 401 - 700 WF 1291 - 1925 306 a

 SEE = Standard error of the estimate. Range of values with 90 percent confidence. 

b

 Extrapolated from actual specification, which calls for PI ≤ 8 mm per 0.1 km. c

 Extrapolated from actual specification, which calls for 23 ≤ PI ≤ 25 mm per 0.16 km. d

 Extrapolated from actual specification, which calls for 8 ≤ PI ≤ 13 mm per 0.16 km. e

 Actual specification calls for incentives for PI≤568 mm/km (36 in/mi) and correction for PI>568 in/mi (36 in/mi). 
55 

For example, if Maryland (a wet-freeze climatic zone state) entertained thoughts of switching 

from PI5-mm to PI0.0, it could refer to table 16 to identify the comparable PI0.0 range for the current 

PI5-mm full-pay range 64 to 110 mm/km (4 to 7 inches/mile). This range is estimated to be 321 to 

403 mm/km (20.3 to 25.5 inches/mile). With a standard error of the estimate (SEE) of 72 

mm/km (4.6 inches/mile) for this relationship, the specification writer can assume that variability 

within the relationship results in a reasonable range for comparable PI0.0 values of 321 to 403 

mm/km (20.3 to 25.5 inches/mile). 

If, on the other hand, the agency desired to transition to an IRI specification, it could use the 

comparable IRI range of 1,096 to 1,254 mm/km [70 to 79.5 inches/mile]). The SEE for this 

relationship is 266 mm/km (16.9 inches/mile). 

Direct State-to-State comparisons of derived specification limits may not be appropriate due to 

individual agencies’ implementation practices. Factors that may affect the specification limits 

for a specific agency include segment length, whether an agency aggregates segments, scope of 

application (new pavements or overlays, and type facilities), and method of index computation 

(half-car roughness index, individual wheelpath IRI, or average IRI). 

57 

Chapter 6. Conclusions and Recommendations 

Conclusions 

In the search for reasonable, practical relationships that link IRI with PI5-mm, PI2.5-mm, and PI0.0, 

and PI0.0 with PI5-mm and PI2.5-mm, a comprehensive evaluation was made of trends documented in 

past pavement smoothness studies, as well as trends developed in this study from vast amounts 

of LTPP profile and smoothness data. The background and results of these studies were 

presented and discussed in previous chapters of this report. 

Although past documented PI–IRI relationships were rather limited (particularly with respect to 

PI2.5-mm–IRI relationships) and showed varying degrees of disparity, factors such as pavement 

type, equipment characteristics, and filtering methods contributed significantly to these 

disparities. 

A much broader and more controlled evaluation using over 43,000 LTPP smoothness data points 

showed generally similar PI–IRI trends as the past study trends. The data points consisted of IRI 

and simulated PI values computed from the same longitudinal profiles measured multiple times 

for 1,793 LTPP pavement test sections. 

Detailed statistical analyses of IRI and simulated PI data indicated a reasonable correlation 

between IRI and PI (PI5-mm, PI2.5-mm, and PI0.0) and between PI0.0 and PI (PI5-mm and PI2.5-mm). 

However, it was determined that pavement type (i.e., AC, JPC, AC/PCC) and climatic conditions 

(i.e., dry-freeze, wet-nonfreeze) are significant factors in the relationship between IRI and PI. 

The effects of these variables were taken into consideration in the development of PI-to-IRI and 

PI-to-PI conversion models. A total of 15 PI-to-IRI models and 18 PI-to-PI models covering all 

three PI blanking band sizes (5, 2.5, and 0 mm [0.2, 0.1, and 0 inches]) and all four climatic 

zones (dry-freeze, dry-nonfreeze, wet-freeze, and wet-nonfreeze) were developed for ACsurfaced pavements. Similarly, for PCC-surfaced pavements, 9 PI-to-IRI models and 12 PI-to-PI 

models were developed. 

The equations, estimated standard errors, and other relevant statistics for all 54 models are 

summarized in tables 10 through 13 in chapter 4. These equations can be used to assist highway 

agency personnel in transitioning smoothness specification limits from PI to IRI or to PI with a 

tighter blanking band. Chapter 5 of this report illustrated the results of applying these conversion 

equations to existing State smoothness specifications. Each State’s PI5-mm, PI2.5-mm, or PI0.0 fullpay range was converted to an estimated equivalent IRI range, and each State’s PI5-mm or PI2.5-mm

full-pay range was converted to an estimated equivalent PI0.0. Results of this exercise are 

summarized in tables 16 through 19 in chapter 5. 
58 

Recommendations 

The major goal of this research was to develop a practical tool to assist in the transition from PI 

to PI0.0 or IRI specifications. Correlation and error estimates have been provided to allow 

agencies to estimate the level of IRI and PI smoothness that is associated with their current 

specifications. To make this research useful, agencies are asked to: 

• Evaluate the validity of the research results based on agency conditions and experiences. 

• Use the correlation equations and variability information to estimate the required level of 

smoothness for a specification that transitions to IRI or to PI with a tighter blanking band. 

It is recommended that the derived specification limits be adjusted to reflect agency 

implementation practices such as segment length, segment averaging, scope of 

application, and index computation method. 

• Track the results of the new smoothness specification and adjust the smoothness 

requirements to meet the increasing abilities of contractors and the smoothness levels 

desired by the agency. 
59 

References 

Baus, R., and W. Hong. 1999. “Investigation and Evaluation of Roadway Rideability 

Equipment and Specifications,” Federal Highway Administration/South Carolina Department of 

Transportation Report FHWA-SC-99-05, Columbia, South Carolina. 

Fernando, E.G. 2000. “Evaluation of Relationship Between Profilograph and Profile-Based 

Roughness Indices,” Paper Prepared for the 79th Annual Meeting of the Transportation Research 

Board, Washington, D.C. 

Florida Department of Transportation (FLDOT). 1997. “Comparison of Ride Quality 

Monitoring Equipment Used for New Construction,” Draft Report, Pavement Systems 

Evaluation Study 97-2, Florida Department of Transportation State Materials Office, 

Tallahassee, Florida. 

Hossain, M., M. Akhter, J. Hancock, and J. Boyer. 2000. “Evaluation of Performance of 

Lightweight Profilometers,” Final Report prepared for Kansas Department of Transportation, 

Kansas State University, Manhatten, Kansas. 

Kombe, E.M. and S.A. Kalevela. 1993. “Evaluation of Initial Pavement Smoothness for the 

Development of PCCP Construction Specifications,” Phase I Final Report, Arizona Department 

of Transportation, Phoenix, Arizona. 

Ksaibati , K., R. McNamera, W. Miley, and J. Armaghani. 1999. “Pavement Roughness Data 

Collection and Utilization,” Transportation Research Record 1655, Transportation Research 

Board, Washington, D.C. 

Kulakowski, B.T. and J.C. Wambold. 1989. “Development of Procedures for the Calibration of 

Profilographs.” Final Report, Pennsylvania Transportation Institute (PTI), Pennsylvania State 

University, University Park, Pennsylvania. 

Rizzo, R. 2000. “Survey of State Smoothness Specifications,” Federal Highway 

Administration, Michigan Division, Lansing, Michigan. 

Rufino, D., K. Baraka, and M.I. Darter. 2000. “Development of a Bridge Smoothness 

Specification for Illinois DOT,” Interim Report prepared for Illinois Department of 

Transportation, Report No. FHWA-IL-UI-279, University of Illinois, Urbana-Champaign, 

Illinois. 

Scofield, L. 1993. “Profilograph Limitations, Correlations, and Calibration Criteria for 

Effective Performance-Based Specifications,” NCHRP Project 20-7 Task 53 Draft Report, 

Transportation Research Board, Washington, D.C. 

Appendix A

IRI and PI Relationships for AC Pavements
A-1

Figure A-1. IRI vs. PI0.0 for all AC pavement types and climatic zones.

Figure A-2. IRI vs. PI2.5-mm for all AC pavement types and climatic zones.

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI0.0, m/km

IRI, m/km

Full-Pay Range (typ)

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI2.5-mm, m/km

IRI, m/km

Full-Pay Range (typ)
A-2

Figure A-3. IRI vs. PI5-mm for all AC pavement types and climatic zones.

Figure A-4. IRI vs. PI0.0, PI2.5-mm, and PI5-mm for all AC pavement types and climatic zones.

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI5-mm, m/km

IRI, m/km

Full-Pay Range (typ)

Zero Blanking Band

2.5-mm Blanking Band

5-mm Blanking Band

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI, m/km

IRI, m/km
A-3

Figure A-5. IRI vs. PI0.0 by AC pavement type for all climatic zones.

Figure A-6. IRI vs. PI2.5-mm by AC pavement type for all climatic zones.

AC

AC/AC AC/PCC

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI0.0, m/km

IRI, m/km

All AC

AC

AC/AC

AC/PCC

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI2.5-mm, m/km

IRI, m/km

All AC
A-4

Figure A-7. IRI vs. PI5-mm by AC pavement type for all climatic zones.

Figure A-8. IRI vs. PI0.0 by climatic zone for AC pavements.

AC

AC/AC

AC/PCC

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI5-mm, m/km

IRI, m/km

All AC

Dry-Freeze

Dry-Nonfreeze

Wet-Freeze

Wet-Nonfreeze

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI0.0, m/km

IRI, m/km
A-5

Figure A-9. IRI vs. PI2.5-mm by climatic zone for AC pavements.

Figure A-10. IRI vs. PI5-mm by climatic zone for AC pavements.

Dry-Freeze

Dry-Nonfreeze

Wet-Freeze

Wet-Nonfreeze

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI2.5-mm, m/km

IRI, m/km

Dry-Freeze

Dry-Nonfreeze

Wet-Freeze

Wet-Nonfreeze

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI5-mm, m/km

IRI, m/km
A-6

Figure A-11. IRI vs. PI0.0 by climatic zone for AC/AC pavements.

Figure A-12. IRI vs. PI2.5-mm by climatic zone for AC/AC pavements.

Dry-Freeze

Dry-Nonfreeze

Wet-Freeze

Wet-Nonfreeze

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI0.0, m/km

IRI, m/km

Dry-Freeze

Dry-Nonfreeze

Wet-Freeze

Wet-Nonfreeze

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI2.5-mm, m/km

IRI, m/km
A-7

Figure A-13. IRI vs. PI5-mm by climatic zone for AC/AC pavements.

Figure A-14. IRI vs. PI0.0 by climatic zone for AC/PCC pavements.

Dry-Freeze

Dry-Nonfreeze Wet-Freeze

Wet-Nonfreeze

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI5-mm, m/km

IRI, m/km

Dry-Freeze

Wet-Freeze

Wet-Nonfreeze

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI0.0, m/km

IRI, m/km
A-8

Figure A-15. IRI vs. PI2.5-mm by climatic zone for AC/PCC pavements.

Figure A-16. IRI vs. PI5-mm by climatic zone for AC/PCC pavements.

Dry-Freeze

Wet-Freeze

Wet-Nonfreeze

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI2.5-mm, m/km

IRI, m/km

Dry-Freeze

Wet-Freeze

Wet-Nonfreeze

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI5-mm, m/km

IRI, m/km
A-9

Figure A-17. IRI vs. PI0.0 by climatic zone for all AC pavement types.

Figure A-18. IRI vs. PI2.5-mm by climatic zone for all AC pavement types.

Dry-Freeze

Dry-Nonfreeze

Wet-Freeze

Wet-Nonfreeze

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI0.0, m/km

IRI, m/km

Dry-Freeze

Dry-Nonfreeze

Wet-Freeze

Wet-Nonfreeze

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI2.5-mm, m/km

IRI, m/km
A-10

Figure A-19. IRI vs. PI5-mm by climatic zone for all AC pavement types.

Figure A-20. PI0.0 vs. PI5-mm for all AC pavement types and climatic zones.

0.0

0.5

1.0

1.5

2.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI5-mm, m/km

Simulated PI0.0, m/km

Full-Pay Range (typ)

Dry-Freeze

Dry-Nonfreeze

Wet-Freeze

Wet-Nonfreeze

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI5-mm, m/km

IRI, m/km
A-11

Figure A-21. PI0.0 vs. PI2.5-mm for all AC pavement types and climatic zones.

Figure A-22. PI2.5-mm vs. PI5-mm for all AC pavement types and climatic zones.

0.0

0.5

1.0

1.5

2.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI2.5-mm, m/km

Simulated PI0.0, m/km

Full-Pay Range (typ)

0.0

0.5

1.0

1.5

2.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI5-mm, m/km

Simulated PI2.5-mm, m/km

Full-Pay Range (typ)
A-12

Figure A-23. PI0.0 vs. PI5-mm by AC pavement type for all climatic zones.

Figure A-24. PI0.0 vs. PI2.5-mm by AC pavement type for all climatic zones.

AC

AC/AC

AC/PCC

0.0

0.5

1.0

1.5

2.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI5-mm, m/km

Simulated PI0.0, m/km

AC

AC/AC

AC/PCC

0.0

0.5

1.0

1.5

2.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI2.5-mm, m/km

Simulated PI0.0, m/km
A-13

Figure A-25. PI2.5-mm vs. PI5-mm by AC pavement type for all climatic zones.

Figure A-26. PI0.0 vs. PI5-mm by climatic zone for all AC pavement types.

AC

AC/AC

AC/PCC

0.0

0.5

1.0

1.5

2.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI5-mm, m/km

Simulated PI2.5-mm, m/km

Dry-Freeze

Dry-Nonfreeze

Wet-Freeze

Wet-Nonfreeze

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI5-mm, m/km

Simulated PI0.0, m/km
A-14

Figure A-27. PI0.0 vs. PI2.5-mm by climatic zone for all AC pavement types.

Figure A-28. PI2.5-mm vs. PI5-mm by climatic zone for all AC pavement types.

Dry-Freeze

Dry-Nonfreeze

Wet-Freeze

Wet-Nonfreeze

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI2.5-mm, m/km

Simulated PI0.0, m/km

Dry-Freeze

Dry-Nonfreeze

Wet-Freeze

Wet-Nonfreeze

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI5-mm, m/km

Simulated PI2.5-mm, m/km
A-15

Figure A-29. PI0.0 vs. PI5-mm by climatic zone for AC pavements.

Figure A-30. PI0.0 vs. PI2.5-mm by climatic zone for AC pavements.

Dry-Freeze

Dry-Nonfreeze

Wet-Freeze

Wet-Nonfreeze

0.0

0.5

1.0

1.5

2.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI5-mm, m/km

Simulated PI0.0, m/km

Dry-Freeze

Dry-Nonfreeze

Wet-Freeze

Wet-Nonfreeze

0.0

0.5

1.0

1.5

2.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI2.5-mm, m/km

Simulated PI0.0, m/km
A-16

Figure A-31. PI2.5-mm vs. PI5-mm by climatic zone for AC pavements.

Figure A-32. PI0.0 vs. PI5-mm by climatic zone for AC/AC pavements.

Dry-Freeze

Dry-Nonfreeze

Wet-Freeze

Wet-Nonfreeze

0.0

0.5

1.0

1.5

2.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI5-mm, m/km

Simulated PI2.5-mm, m/km

Dry-Freeze

Dry-Nonfreeze

Wet-Freeze Wet-Nonfreeze

0.0

0.5

1.0

1.5

2.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI5-mm, m/km

Simulated PI0.0, m/km
A-17

Figure A-33. PI0.0 vs. PI2.5-mm by climatic zone for AC/AC pavements.

Figure A-34. PI2.5-mm vs. PI5-mm by climatic zone for AC/AC pavements.

Dry-Freeze

Dry-Nonfreeze

Wet-Freeze

Wet-Nonfreeze

0.0

0.5

1.0

1.5

2.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI2.5-mm, m/km

Simulated PI0.0, m/km

Dry-Freeze

Dry-Nonfreeze Wet-Freeze

Wet-Nonfreeze

0.0

0.5

1.0

1.5

2.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI5-mm, m/km

Simulated PI2.5-mm, m/km
A-18

Figure A-35. PI0.0 vs. PI5-mm by climatic zone for AC/PCC pavements.

Figure A-36. PI0.0 vs. PI2.5-mm by climatic zone for AC/PCC pavements.

Dry-Freeze

Wet-Freeze Wet-Nonfreeze

0.0

0.5

1.0

1.5

2.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI5-mm, m/km

Simulated PI0.0, m/km

Dry-Freeze

Wet-Freeze

Wet-Nonfreeze

0.0

0.5

1.0

1.5

2.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI2.5-mm, m/km

Simulated PI0.0, m/km
A-19

Figure A-37. PI2.5-mm vs. PI5-mm by climatic zone for AC/PCC pavements.

Dry-Freeze

Wet-Freeze

Wet-Nonfreeze

0.0

0.5

1.0

1.5

2.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI5-mm, m/km

Simulated PI2.5-mm, m/km
Appendix B

IRI and PI Relationships for PCC Pavements
B-1

Figure B-1. IRI vs. PI0.0 for all PCC pavement types and climatic zones.

Figure B-2. IRI vs. PI2.5-mm for all PCC pavement types and climatic zones.

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI0.0, m/km

IRI, m/km

Full Pay Range (typ)

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI2.5-mm, m/km

IRI, m/km

Full Pay Range (typ)
B-2

Figure B-3. IRI vs. PI5-mm for all PCC pavement types and climatic zones.

Figure B-4. IRI vs. PI0.0, PI2.5-mm, and PI5-mm for all PCC pavement types and climatic zones.

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI5-mm, m/km

IRI, m/km

Full Pay Range (typ)

Zero Blanking Band

2.5-mm Blanking Band

5-mm Blanking Band

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI, m/km

IRI, m/km
B-3

Figure B-5. IRI vs. PI0.0 by PCC pavement type for all climatic zones.

Figure B-6. IRI vs. PI2.5-mm by PCC pavement type for all climatic zones.

CRC

JPC

JRC

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI0.0, m/km

IRI, m/km

All PCC

CRC

JPC

JRC

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI2.5-mm, m/km

IRI, m/km

All PCC
B-4

Figure B-7. IRI vs. PI5-mm by PCC pavement type for all climatic zones.

Figure B-8. IRI vs. PI0.0 by climatic zone for CRC pavements.

CRC

JPC

JRC

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI5-mm, m/km

IRI, m/km

All PCC

Dry-Freeze

Dry-Nonfreeze

Wet-Nonfreeze Wet-Freeze

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI0.0, m/km

IRI, m/km
B-5

Figure B-9. IRI vs. PI2.5-mm by climatic zone for CRC pavements.

Figure B-10. IRI vs. PI5-mm by climatic zone for CRC pavements.

Dry-Freeze

Dry-Nonfreeze

Wet-Nonfreeze Wet-Freeze

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI2.5-mm, m/km

IRI, m/km

Dry-Freeze

Dry-Nonfreeze

Wet-Freeze

Wet-Nonfreeze

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI5-mm, m/km

IRI, m/km
B-6

Figure B-11. IRI vs. PI0.0 by climatic zone for JPC pavements.

Figure B-12. IRI vs. PI2.5-mm by climatic zone for JPC pavements.

Dry-Freeze

Dry-Nonfreeze

Wet-Freeze

Wet-Nonfreeze

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI0.0, m/km

IRI, m/km

Dry-Freeze

Dry-Nonfreeze

Wet-Freeze

Wet-Nonfreeze

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI2.5-mm, m/km

IRI, m/km
B-7

Figure B-13. IRI vs. PI5-mm by climatic zone for JPC pavements.

Figure B-14. IRI vs. PI0.0 by climatic zone for JRC pavements.

Dry-Freeze

Dry-Nonfreeze

Wet-Freeze

Wet-Nonfreeze

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI5-mm, m/km

IRI, m/km

Wet-Freeze

Wet-Nonfreeze

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI0.0, m/km

IRI, m/km
B-8

Figure B-15. IRI vs. PI2.5-mm by climatic zone for JRC pavements.

Figure B-16. IRI vs. PI5-mm by climatic zone for JRC pavements.

Wet-Freeze

Wet-Nonfreeze

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI2.5-mm, m/km

IRI, m/km

Wet-Freeze

Wet-Nonfreeze

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI5-mm, m/km

IRI, m/km
B-9

Figure B-17. IRI vs. PI0.0 by climatic zone for all PCC pavement types.

Figure B-18. IRI vs. PI2.5-mm by climatic zone for all PCC pavement types.

Dry-Freeze

Dry-Nonfreeze

Wet-Freeze

Wet-Nonfreeze

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI0.0, m/km

IRI, m/km

Dry-Freeze

Dry-Nonfreeze

Wet-Freeze

Wet-Nonfreeze

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI2.5-mm, m/km

IRI, m/km
B-10

Figure B-19. IRI vs. PI5-mm by climatic zone for all PCC pavement types.

Figure B-20. PI0.0 vs. PI5-mm for all PCC pavement types and climatic zones.

Dry-Freeze

Dry-Nonfreeze

Wet-Nonfreeze Wet-Freeze

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI5-mm, m/km

IRI, m/km

0.0

0.5

1.0

1.5

2.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI5-mm, m/km

Simulated PI0.0, m/km

Full Pay Range (typ)
B-11

Figure B-21. PI0.0 vs. PI2.5-mm for all PCC pavement types and climatic zones.

Figure B-22. PI2.5-mm vs. PI5-mm for all PCC pavement types and climatic zones.

0.0

0.5

1.0

1.5

2.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI2.5-mm, m/km

Simulated PI0.0, m/km

Full Pay Range (typ)

0.0

0.5

1.0

1.5

2.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI5-mm, m/km

Simulated PI2.5-mm, m/km

Full Pay Range (typ)
B-12

Figure B-23. PI0.0 vs. PI5-mm by PCC pavement type for all climatic zones.

Figure B-24. PI0.0 vs. PI2.5-mm by PCC pavement type for all climatic zones.

CRC

JPC

JRC

0.0

0.5

1.0

1.5

2.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI5-mm, m/km

Simulated PI0.0, m/km

CRC

JPC

JRC

0.0

0.5

1.0

1.5

2.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI2.5-mm, m/km

Simulated PI0.0, m/km
B-13

Figure B-25. PI2.5-mm vs. PI5-mm by PCC pavement type for all climatic zones.

Figure B-26. PI0.0 vs. PI5-mm by climatic zone for all PCC pavement types.

CRC

JPC

JRC

0.0

0.5

1.0

1.5

2.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI5-mm, m/km

Simulated PI2.5-mm, m/km

Dry-Freeze

Dry-Nonfreeze

Wet-Freeze

Wet-Nonfreeze

0.0

0.5

1.0

1.5

2.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI5-mm, m/km

Simulated PI0.0, m/km
B-14

Figure B-27. PI0.0 vs. PI2.5-mm by climatic zone for all PCC pavement types.

Figure B-28. PI2.5-mm vs. PI5-mm by climatic zone for all PCC pavement types.

Dry-Freeze

Dry-Nonfreeze

Wet-Freeze

Wet-Nonfreeze

0.0

0.5

1.0

1.5

2.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI2.5-mm, m/km

Simulated PI0.0, m/km

Dry-Freeze

Dry-Nonfreeze

Wet-Freeze

Wet-Nonfreeze

0.0

0.5

1.0

1.5

2.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI5-mm, m/km

Simulated PI2.5-mm, m/km
B-15

Figure B-29. PI0.0 vs. PI5-mm by climatic zone for CRC pavements.

Figure B-30. PI0.0 vs. PI2.5-mm by climatic zone for CRC pavements.

Dry-Freeze

Dry-Nonfreeze

Wet-Freeze

Wet-Nonfreeze

0.0

0.5

1.0

1.5

2.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI5-mm, m/km

Simulated PI0.0, m/km

Dry-Freeze

Dry-Nonfreeze

Wet-Freeze

Wet-Nonfreeze

0.0

0.5

1.0

1.5

2.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI2.5-mm, m/km

Simulated PI0.0, m/km
B-16

Figure B-31. PI2.5-mm vs. PI5-mm by climatic zone for CRC pavements.

Figure B-32. PI0.0 vs. PI5-mm by climatic zone for JPC pavements.

Dry-Freeze

Dry-Nonfreeze

Wet-Freeze

Wet-Nonfreeze

0.0

0.5

1.0

1.5

2.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI5-mm, m/km

Simulated PI2.5-mm, m/km

Dry-Freeze

Dry-Nonfreeze

Wet-Nonfreeze Wet-Freeze

0.0

0.5

1.0

1.5

2.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI5-mm, m/km

Simulate PI 0.0, m/km
B-17

Figure B-33. PI0.0 vs. PI2.5-mm by climatic zone for JPC pavements.

Figure B-34. PI2.5-mm vs. PI5-mm by climatic zone for JPC pavements.

Dry-Freeze

Dry-Nonfreeze

Wet-Freeze

Wet-Nonfreeze

0.0

0.5

1.0

1.5

2.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI2.5-mm, m/km

Simulate PI 0.0, m/km

Dry-Freeze

Dry-Nonfreeze

Wet-Freeze

Wet-Nonfreeze

0.0

0.5

1.0

1.5

2.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI5-mm, m/km

Simulate PI 2.5-mm, m/km
B-18

Figure B-35. PI0.0 vs. PI5-mm by climatic zone for JRC pavements.

Figure B-36. PI0.0 vs. PI2.5-mm by climatic zone for JRC pavements.

Wet-Freeze

Wet-Nonfreeze

0.0

0.5

1.0

1.5

2.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI5-mm, m/km

Simulated PI0.0, m/km

Wet-Freeze

Wet-Nonfreeze

0.0

0.5

1.0

1.5

2.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI2.5-mm, m/km

Simulated PI0.0, m/km
B-19

Figure B-37. PI2.5-mm vs. PI5-mm by climatic zone for JRC pavements.

Wet-Freeze

Wet-Nonfreeze

0.0

0.5

1.0

1.5

2.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Simulated PI5-mm, m/km

Simulated PI2.5-mm, m/km