Pavement Smoothness Index Relationships, Final ReportPUBLICATION NO. FHWA-RD-02-057
OCTOBER 2002U.S. Department of Transportation
Federal Highway Administration
Research, Development, and Technology
Turner-Fairbank Highway Research Center
6300 Georgetown Pike
McLean, VA 22101-2296
ForewordA 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
NoticeThis 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 Page1. Report No.: FHWA-RD-02-057
2. Government Accession No.:
3. Recipient's Catalog No.:
4. Title and Subtitle: PAVEMENT SMOOTHNESS INDEX RELATIONSHIPS
5. Report Date: OCTOBER 2002
6. Performing Organization Code: C6B
7. Author(s): Kelly L. Smith, Leslie Titus-Glover, Lynn D. Evans
8. Performing Organization Report No.:
9. Performing Organization Name and Address: ERES Division of Applied Research Associates, Inc., 505 W. University Avenue, Champaign, IL 61820.
10. Work Unit No. (TRAIS):
11. Contract or Grant No.: GS-10F-0298K
12. Sponsoring Agency Name and Address: Office of Pavement Technology, Federal Highway Administration, 400 Seventh St., S.W., Washington, D.C. 20590
13. Type of Report and Period Covered: Final Report, February - November 2001
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 smooth 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-inch) 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 these 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 an 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, VA 22161.
19. Security Classification (of this report): Unclassified
20. Security Classification (of this page): Unclassified
21. No. of Pages: 116
22. Price:
Form DOT F 1700.7 (8-72)
Reproduction of completed page authorized
SI* (MODERN METRIC) CONVERSION FACTORSApproximate Conversions to SI Units
Length:
inches (in) multiply by 25.4 to get millimeters (mm)
feet (ft) multiply by 0.305 to get meters (m)
yards (yd) multiply by 0.914 to get meters (m)
miles (mi) multiply by 1.61 to get kilometers (km)Area:
square inches (in2) multiply by 645.2 to get square millimeters (mm2)
square feet (ft2) multiply by 0.093 to get square meters (m2)
square yard (yd2) multiply by 0.836 to get square meters (m2)
acres (ac) multiply by 0.405 to get hectares (ha)
square miles (mi2) multiply by 2.59 to get square kilometers (km2)Volume:
fluid ounces (fl oz) multiply by 29.57 to get milliliters (mL)
gallons (gal) multiply by 3.785 to get liters (L)
cubic feet (ft3) multiply by 0.028 to get cubic meters (m3)
cubic yards (yd3) multiply by 0.765 to get cubic meters (m3)
NOTE: volumes greater than 1000 L shall be shown in m3Mass:
ounces (oz) multiply by 28.35 to get grams (g)
pounds (lb) multiply by 0.454 to get kilograms (kg)
short tons - 2000 lb (T) multiply by 0.907 to get megagrams or "metric ton" (Mg or "t")Temperature (exact degrees):
Fahrenheit (°F) multiply by 5 (F-32)/9 or (F-32)/1.8 to get Celsius (°C)Illumination:
foot-candles (fc) multiply by 10.76 to get lux (lx)
foot-Lamberts (fl) multiply by 3.426 to get candela/m2 (cd/m2)Force and Pressure or Stress:
poundforce (lbf) multiply by 4.45 to get newtons (N)
poundforce per square inch (lbf/in2) multiply by 6.89 to get kilopascals (kPa)Approximate Conversions From SI Units
Length:
millimeters (mm) multiply by 0.039 to get inches (in)
meters (m) multiply by 3.28 to get feet (ft)
meters (m) multiply by 1.09 to get yards (yd)
kilometers (km) multiply by 0.621 to get miles (mi)Area:
square millimeters (mm2) multiply by 0.0016 to get square inches (in2)
square meters (m2) multiply by 10.764 to get square feet (ft2)
square meters (m2) multiply by 1.195 to get square yards (yd2)
hectares (ha) multiply by 2.47 to get acres (ac)
square kilometers (km2) multiply by 0.386 to get square miles (mi2)Volume:
milliliters (mL) multiply by 0.034 to get fluid ounces (fl oz)
liters (L) multiply by 0.264 to get gallons (gal)
cubic meters (m3) multiply by 35.314 to get cubic feet (ft3)
cubic meters (m3) multiply by 1.307 to get cubic yards (yd3)Mass:
grams (g) multiply by 0.035 to get ounces (oz)
kilograms (kg) multiply by 2.202 to get pounds (lb)
megagrams or "metric ton" (Mg or "t") multiply by 1.103 to get short tons - 2000 lb (T)Temperature (exact degrees):
Celsius (°C) multiply by 1.8C+32 to get Fahrenheit (°F)Illumination:
lux (lx) multiply by 0.0929 to get foot-candles (fc)
candela/m2 (cd/m2) multiply by 0.2919 to get foot-Lamberts (fl)Force and Pressure or Stress:
newtons (N) multiply by 0.225 to get poundforce (lbf)
kilopascals (kPa) multiply by 0.145 to get poundforce per square inch (lbf/in2)*SI is the symbol for the International System of Units. Appropriate rounding should be made to comply with Section 4 of ASTM E380.
(Revised March 2002)
TABLE OF CONTENTSBackgroundChapter 2. Literature Review
Problem Statement
Study ObjectivesIntroductionChapter 3. LTPP Data Collection and Project Database Development
Past Studies on PI-IRI Relationships
SummaryIntroductionChapter 4. Development of LTPP-Based Smoothness Index Relationships
Collection of LTPP Profile Data
Conversion of Profile Data to Simulated PI Values
Populating the Project DatabaseIntroductionChapter 5. Adaptation of LTPP-Based Models to Current State Smoothness Specifications
Step 1 -- Preliminary Evaluation
Step 2 -- Selection of Appropriate Model Form
Step 3 -- Group Data into Sets with Similar Smoothness Relations
Steps 4 and 5 -- Develop Tentative Models and Assess Models for Reasonableness
Step 6 -- Select Final ModelsIntroductionChapter 6. Conclusions and Recommendations
Overview of State Smoothness Specifications
Development of Recommended Initial IRI and PI0.0 LevelsConclusionsAppendix A: IRI and PI Relationships for AC Pavements
RecommendationsAppendix B: IRI and PI Relationships for PCC Pavements
LIST OF FIGURES1. Sensitivity of simulated profilograph to spatial frequency.
2. Relationship between IRI and manually generated PI in PTI profilograph calibration study.
3. Relationship between IRI and computer-generated PI in PTI profilograph calibration study.
4. Correlation of IRI and PI in Arizona pavement smoothness study.
5. IRI-PI5-mm (PI0.2-inch) correlations established in Florida's ride quality equipment study.
7. Relationship between IRI and computer-simulated PI values in TTI equipment comparison study.
11. Graphical comparison of documented PI5-mm-IRI smoothness relationships.
12. Graphical comparison of documented PI2.5-mm-IRI smoothness relationships.
13. Graphical comparison of documented PI0.0-IRI smoothness relationships.
14. Flow chart for developing pavement smoothness models.
15. Histogram showing the distribution of IRI data used in model development (all AC pavements).
16. Histogram showing the distribution of IRI data used in model development (all PCC pavements).
17. Histogram showing the distribution of PI5-mm data used in model development (all AC pavements).
18. Histogram showing the distribution of PI5-mm data used in model development (all PCC pavements).
19. IRI vs. PI0.0 for all AC pavements and climates.
20. PI0.0 vs. PI5-mm for all PCC pavements and climates.
21. IRI vs. PI0.0 by AC pavement type for all climates.
22. PI0.0 vs. PI5-mm by climate for all PCC pavement types.
23. Graphical comparison of PI5-mm-IRI smoothness relationships for AC pavements.
24. Graphical comparison of PI5-mm-IRI smoothness relationships for PCC pavements.
25. Graphical comparison of PI2.5-mm-IRI smoothness relationships for AC pavements.
26. Graphical comparison of PI0.0-IRI smoothness relationships for AC pavements.
27. Graphical comparison of PI0.0-IRI smoothness relationships for PCC pavements.
28. Conceptual plot showing relationships of smoothness indices within and between cells.
LIST OF TABLES1. Summary of documented PI-IRI relationships.
2. Breakdown of test sections by LTPP experiment.
3. Breakdown of test sections by State.
4. Summary of basic statistics of data used in model development.
5. Matrix of scatter plots created for model development.
6. Factorial of cells used for model development.
7. ANOVA results on the effect of pavement type and climate on PI-IRI relationship for AC pavements.
9. Summary of groupings (merged cells) used for model development.
10. PI-to-IRI index conversion equations and variability indices for AC pavements.
11. PI-to-PI index conversion equations and variability indices for AC pavements.
12. PI-to-IRI index conversion equations and variability indices for PCC pavements.
13. PI-to-PI index conversion equations and variability indices for PCC pavements.
14. State agency smoothness specifications for asphalt pavements.
15. State agency smoothness specifications for concrete pavements.
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 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 kilometers/hour [15 miles/hour] versus 5 km/hr [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-millimeter (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.
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 aphalt 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.
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-inch) 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-inch) 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-inch).
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.
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-inch) between 0 and 0.24 m/km (15 inches per 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-inch) 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-inch) 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 high-speed 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.
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-inch) 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-inch) 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.
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-inch) 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-inch). 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-inch) 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 California-type 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 (Kansas DOT 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.
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 the 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 taken by two of the lightweight profilers (Ames 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.
Figure 9. Relationship between IRI and California profilograph PI0.0 in KDOT lightweight profiler comparison study.
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-inch). 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-inch) 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-inch) values largely in the range of 0.4 to 1.0 m/km (25 to 63 inches per mile) -- due to the fact that bridges are often much rougher than pavements.
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 California-type 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.IRI vs. PI5-mm:
Study (Year) Pavement Types No. of Test Sections Remarks Linear Regression Equation, m/km Linear Regression Equation, inches per mile PTI (1988) AC and PCC 26 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 Florida DOT (1996) AC 12 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:
Study (Year) Pavement Types No. of Test Sections Remarks Linear Regression Equation, m/km Linear Regression Equation, inches per mile Florida DOT (1996) AC 12 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
Study (Year) Pavement Types No. of Test Sections Remarks Linear Regression Equation, m/km Linear Regression Equation, inches per mile TTI (1996) AC overlays 48 Computer-simulated PI, Laser-type inertial profiler IRI = 2.14*PI + 0.31 IRI = 2.14*PI + 19.33 Florida DOT (1996) AC 12 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 Kansas DOT (1999/2000) PCC 8 Manual profilograph PI, Full-sized and lightweight inertial profilers IRI = 2.00*PI + 0.56 IRI = 2.00*PI + 35.50 Kansas DOT (1999/2000) PCC 8 Computer-simulated PI, Full-sized inertial profiler IRI = 2.76*PI + 0.36 IRI = 2.76*PI + 22.82 Kansas DOT (1999/2000) PCC 8 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
Figure 11. Graphical illustration of documented PI5-mm -- IRI smoothness relationships.
Figure 12. Graphical illustration of documented PI2.5-mm -- IRI smoothness relationships.
Figure 13. Graphical illustration of documented PI0.0-IRI smoothness relationships.
Chapter 3. LTPP Data Collection and Project Database DevelopmentIntroduction
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 van-mounted 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-inch) 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.
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-inch) 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 per 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 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 right-wheelpath 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.
Chapter 4. Development of LTPP-Based Smoothness Index RelationshipsIntroduction
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:
- Perform preliminary evaluation of the assembled database, including a detailed check of data quality, appropriate data cleaning, and development of comprehensive scatter plots.
- 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.
- 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).
- Develop tentative models for the smoothness indices relationships.
- Assess tentative models for reasonableness (e.g., assess model diagnostic statistics, such as correlation coefficient [R2] and the standard error of the estimate [SEE]).
- 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 EvaluationIn 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 AC- and 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 per mile], PI5-mm between 0 and 235 mm/km [0 and 15 inches per 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).
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).
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).
Table 4. Summary of basic statistics of data used in model development.
Notes: - = No data available; a = New construction only.
Pavement Type Climate N IRI Mean IRI Std. Dev. PI0.0 Mean PI0.0 Std. Dev. PI2.5-mm Mean PI2.5-mm Std. Dev. PI5-mm Mean PI5-mm Std. Dev. AC DF 2,720 1,252.5 671.5 373.8 216.9 190.3 187.8 91.0 137.6 AC DNF 1,740 1,031.4 518.7 297.7 179.1 142.0 139.4 63.8 89.9 AC 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 AC/AC DF 1,856 1,397.6 629.0 412.3 218.2 220.4 187.3 106.4 130.8 AC/AC DNF 1,502 1,011.1 422.2 274.6 141.9 106.5 103.0 40.1 63.4 AC/AC 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 AC/PC DF 90 1,072.4 196.9 336.4 88.2 152.8 115.3 63.2 72.7 AC/PC DNF 0 - - - - - - - -AC/PC 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 JPCa DF 2,154 1,536.8 545.2 475.2 210.6 226.4 195.8 99.9 139.5 JPCa DNF 1,270 1,464.9 500.1 394.8 175.1 162.1 168.3 68.9 117.8 JPCa WF 6,542 1,639.8 703.4 572.3 294.8 334.2 286.5 180.4 236.1 JPCa WNF 2,196 1,737.3 612.7 594.1 228.3 337.7 216.0 161.5 174.1 JRCa DF 0 - - - - - - - -JRCa DNF 0 - - - - - - - -JRCa WF 1,950 1,955.7 508.0 780.0 233.1 531.2 231.2 321.9 197.4 JRCa WNF 349 2,053.8 349.0 785.4 144.4 546.3 142.8 328.9 129.2 CRCa DF 39 1,330.3 54.0 508.8 25.8 336.3 55.3 160.8 29.0 CRCa DNF 120 1,275.2 397.6 395.0 207.2 180.3 200.7 98.0 142.2 CRCa WF 722 1,575.6 478.0 541.7 202.2 332.9 204.0 181.7 166.7 CRCa WNF 358 1,620.7 457.5 563.4 193.3 343.6 188.6 189.2 153.7
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 Dry-Freeze IRI vs. PI0.0 Yes Yes Yes Yes Yes Yes - Yes Dry-Freeze IRI vs. PI2.5-mm Yes Yes Yes Yes Yes Yes - Yes Dry-Freeze IRI vs. PI5-mm Yes Yes Yes Yes Yes Yes - Yes Dry-Nonfreeze IRI vs. PI0.0 Yes Yes Yes - Yes Yes - Yes Dry-Nonfreeze IRI vs. PI2.5-mm Yes Yes Yes - Yes Yes - Yes Dry-Nonfreeze IRI vs. PI5-mm Yes Yes Yes - Yes Yes - Yes Wet-Freeze IRI vs. PI0.0 Yes Yes Yes Yes Yes Yes Yes Yes Wet-Freeze IRI vs. PI2.5-mm Yes Yes Yes Yes Yes Yes Yes Yes Wet-Freeze IRI vs. PI5-mm Yes Yes Yes Yes Yes Yes Yes Yes Wet-Nonfreeze IRI vs. PI0.0 Yes Yes Yes Yes Yes Yes Yes Yes Wet-Nonfreeze IRI vs. PI2.5-mm Yes Yes Yes Yes Yes Yes Yes Yes Wet-Nonfreeze IRI vs. PI5-mm Yes Yes Yes Yes Yes Yes Yes Yes All IRI vs. PI0.0 Yes Yes Yes Yes Yes Yes Yes Yes All IRI vs. PI2.5-mm Yes Yes Yes Yes Yes Yes Yes Yes All IRI vs. PI5-mm Yes Yes Yes Yes Yes Yes Yes Yes Dry-Freeze PI0.0 vs. PI5-mm Yes Yes Yes Yes Yes Yes - Yes Dry-Freeze PI0.0 vs. PI2.5-mm Yes Yes Yes Yes Yes Yes - Yes Dry-Freeze PI2.5-mm vs. PI5-mm Yes Yes Yes Yes Yes Yes - Yes Dry-Nonfreeze PI0.0 vs. PI5-mm Yes Yes Yes - Yes Yes - Yes Dry-Nonfreeze PI0.0 vs. PI2.5-mm Yes Yes Yes - Yes Yes - Yes Dry-Nonfreeze PI2.5-mm vs. PI5-mm Yes Yes Yes - Yes Yes - Yes Wet-Freeze PI0.0 vs. PI5-mm Yes Yes Yes Yes Yes Yes Yes Yes Wet-Freeze PI0.0 vs. PI2.5-mm Yes Yes Yes Yes Yes Yes Yes Yes Wet-Freeze PI2.5-mm vs. PI5-mm Yes Yes Yes Yes Yes Yes Yes Yes Wet-Nonfreeze PI0.0 vs. PI5-mm Yes Yes Yes Yes Yes Yes Yes Yes Wet-Nonfreeze PI0.0 vs. PI2.5-mm Yes Yes Yes Yes Yes Yes Yes Yes Wet-Nonfreeze PI2.5-mm vs. PI5-mm Yes Yes Yes Yes Yes Yes Yes Yes All PI0.0 vs. PI5-mm Yes Yes Yes Yes Yes Yes Yes Yes All PI0.0 vs. PI2.5-mm Yes Yes Yes Yes Yes Yes Yes Yes All PI2.5-mm vs. PI5-mm Yes Yes Yes Yes Yes Yes Yes Yes Note: - = No data available.
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.
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.
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 per 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.
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.
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.
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. Continuosly Reinforced Concrete (CRC)) and the climatic region in which the pavement was located. The model form selected is shown as equation 1.
IRI = alpha + beta*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).
alpha, beta = 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.
Block Pavement Type Climatic Region IRI vs PI (0.0) IRI vs PI (2.5-mm) IRI vs PI (5-mm) PI (0.0) vs PI (2.5-mm) PI (0.0) vs PI (5-mm) PI (2.5-mm) vs PI (5-mm) Block 1(AC-surfaced pavements) AC DF 1 2 3 4 5 6 Block 1(AC-surfaced pavements) AC DNF 7 8 9 10 11 12 Block 1(AC-surfaced pavements) AC WF 13 14 15 16 17 18 Block 1(AC-surfaced pavements) AC WNF 19 20 21 22 23 24 Block 1(AC-surfaced pavements) AC/AC DF 25 26 27 28 29 30 Block 1(AC-surfaced pavements) AC/AC DNF 31 32 33 34 35 36 Block 1(AC-surfaced pavements) AC/AC WF 37 38 39 40 41 42 Block 1(AC-surfaced pavements) AC/AC WNF 43 44 45 46 47 48 Block 1(AC-surfaced pavements) AC/PC DF 49 50 51 52 53 54 Block 1(AC-surfaced pavements) AC/PC DNF 55 56 57 58 59 60 Block 1(AC-surfaced pavements) AC/PC WF 61 62 63 64 65 66 Block 1(AC-surfaced pavements) AC/PC WNF 67 68 69 70 71 72 Block 2(PCC-surfaced pavements) JPC DF 73 74 75 76 77 78 Block 2(PCC-surfaced pavements) JPC DNF 79 80 81 82 83 84 Block 2(PCC-surfaced pavements) JPC WF 85 86 87 88 89 90 Block 2(PCC-surfaced pavements) JPC WNF 91 92 93 94 95 96 Block 2(PCC-surfaced pavements) JRC DF 97 98 99 100 101 102 Block 2(PCC-surfaced pavements) JRC DNF 103 104 105 106 107 108 Block 2(PCC-surfaced pavements) JRC WF 109 110 111 112 113 114 Block 2(PCC-surfaced pavements) JRC WNF 115 116 117 118 119 120 Block 2(PCC-surfaced pavements) CRC DF 121 122 123 124 125 126 Block 2(PCC-surfaced pavements) CRC DNF 127 128 129 130 131 132 Block 2(PCC-surfaced pavements) CRC WF 133 134 135 136 137 138 Block 2(PCC-surfaced pavements) CRC WNF 139 140 141 142 143 144
- 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, Jointed Reinforced Concrete (JRC)) and climatic region. For PCC pavements, the categories of surface type were limited to JPC, 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 = alpha + beta*PI would be used).
- Develop database with all the beta's for each of the cells.
- Compute mean slope (mean values of beta) 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 (beta) 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.
Figure 28. Conceptual plot showing relationships of smoothness indices within and between 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.
- Developing ANOVA models for evaluating the effects of the independent variable beta) 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 (beta) 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:
Beta = gamma(1) + gamma(2)*CLIMATE + gamma(3)*PVMT, Eq. 2
where:
Beta = Slope of PI-IRI or PI-PI linear model.
CLIMATE = Test pavement climate location.
PVMT = Pavement type.
gamma(1), gamma(2), gamma(3) = 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: mu(A) = mu(B)).
- Alternative hypothesis -- The mean slopes from cells A and B are significantly different (HA: mu(A) does not equal mu(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 Variablea Grouping Variableb N F-Statistic Value Probability > F (p-value) Slope of PI0.0-IRI linear relationship Pavement Type 2,395 2.24 0.1061c Slope of PI0.0-IRI linear relationship Climate 2,395 7.61 0.0001d Slope of PI2.5-mm-IRI linear relationship Pavement Type 2,395 5.91 0.0028d Slope of PI2.5-mm-IRI linear relationship Climate 2,395 3.87 0.0089d Slope of PI5-mm-IRI linear relationship Pavement Type 2,395 0.72 0.4870e Slope of PI5-mm-IRI linear relationship Climate 2,395 0.95 0.4177e Notes:
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.
Table 8. ANOVA results on the effect of pavement type and climate on PI-IRI relationship for PCC pavements.
Dependent Variablea Grouping Variableb N F-Statistic Value Probability > F (p-value) Slope of PI0.0-IRI linear relationship Pavement Type 1,123 3.44 0.0630d Slope of PI0.0-IRI linear relationship Climate 1,123 4.82 0.0024d Slope of PI2.5-mm-IRI linear relationship Pavement Type 1,123 5.51 0.0190d Slope of PI2.5-mm-IRI linear relationship Climate 1,123 13.68 0.0001d Slope of PI5-mm-IRI linear relationship Pavement Type 1,119 2.96 0.0850c Slope of PI5-mm-IRI linear relationship Climate 1,119 12.46 0.00014 Notes:
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 per mile) for IRI and 21 to 79 mm/km (1.3 to 5.0 inches per 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.
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 Block 1 (AC-surfaced pavements) AC DF 1 2 3 4 5 6 Block 1 (AC-surfaced pavements) AC DNF 1 2 3 7 8 9 Block 1 (AC-surfaced pavements) AC WF 1 2 3 4 5 6 Block 1 (AC-surfaced pavements) AC WNF 1 2 3 7 8 9 Block 1 (AC-surfaced pavements) AC/AC DF 10 11 12 13 14 15 Block 1 (AC-surfaced pavements) AC/AC DNF 16 17 18 19 20 21 Block 1 (AC-surfaced pavements) AC/AC WF 22 23 24 25 26 27 Block 1 (AC-surfaced pavements) AC/AC WNF 22 23 24 25 26 27 Block 1 (AC-surfaced pavements) AC/PC DF 28 29 30 31 32 33 Block 1 (AC-surfaced pavements) AC/PC DNF 28 29 30 31 32 33 Block 1 (AC-surfaced pavements) AC/PC WF 28 29 30 31 32 33 Block 1 (AC-surfaced pavements) AC/PC WNF 28 29 30 31 32 33 Block 2 (PCC-surfaced pavements) JPC DF 34 35 36 37 38 39 Block 2 (PCC-surfaced pavements) JPC DNF 40 41 42 43 44 45 Block 2 (PCC-surfaced pavements) JPC WF 34 35 36 46 47 48 Block 2 (PCC-surfaced pavements) JPC WNF 49 50 51 52 53 54 Block 2 (PCC-surfaced pavements) JRC DF 34 35 36 37 38 39 Block 2 (PCC-surfaced pavements) JRC DNF 40 41 42 43 44 45 Block 2 (PCC-surfaced pavements) JRC WF 34 35 36 46 47 48 Block 2 (PCC-surfaced pavements) JRC WNF 49 50 51 52 53 54 Block 2 (PCC-surfaced pavements) CRC DF 34 35 36 37 38 39 Block 2 (PCC-surfaced pavements) CRC DNF 40 41 42 43 44 45 Block 2 (PCC-surfaced pavements) CRC 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.
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 R2 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 R2 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.
Table 12. PI-to-IRI index conversion equations and variability indices for PCC pavements.
Pavement Type Climatea Blanking Band (mm) Correlation Equation (IRI = mm/km, PI = mm/km) N SEE R2 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 R2 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 R2 greater than 70 percent. SEE ranged from 177 to 306 mm/km (11.2 to 19.4 inches per mile) for IRI and 21 to 79 mm/km (1.3 to 5.0 inches per 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 to 4,000 mm/km [19 to 253 inches per mile] and PI ranged from 0 to 1,700 mm/km [0 to 108 inches per 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 per mile] for PI and 177 to 308 [11.2 to 19.5 inches per 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 ± talpha/2sigma, Eq. 3
where:
CI = Confidence interval.
mean = Predicted smoothness index.
t = Value of t-statistic at a given significance level.
alpha = Significance level (usually 90 or 95 percent).
sigma = 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 per 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 per 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 per 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 per mile). The SEE values associated with the PI-PI models in tables 11 and 13 ranged from 25 to 58 mm/km (1.6 to 3.7 inches per mile). These SEE values are reasonable, considering the inherent differences in the way surface wavelengths are processed for IRI and PI.
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 California-type 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.
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 per mile).
- PI2.5-mm: 150 to 505 mm/km (9.5 to 32 inches per mile).
- PI0.0: 161 to 536 mm/km (10.1 to 34 inches per 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 per mile).
- PI5-mm: 0 to 205 mm/km (0 to 13 inches per mile).
- PI2.5-mm: 0 to 250 mm/km (0 to 16 inches per mile).
- PI0.0: 161 to 536 mm/km (10.1 to 34 inches per 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.
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 kma (0.1 mi) <32 mm/km (<2 inches per mile) 32 - 63 mm/km (2 - 3.9 inches per mile) 64 - 160 mm/km (4 - 10 inches per mile) >160 mm/km (<10 inches per mile) AK -- -- -- -- -- -- --AZ GM-type profiler MRN 0.16 kma (0.1 mi) <520 mm/kma (<33 inches per mile) 520 - 710 mm/kma (33 - 45 inches per mile) 711 - 1578 mm/kma (46 - 100 inches per mile) <1578 mm/kma (>100 inches per mile) AR California-type profilograph, lightweight profiler PI5-mm 0.2 km (0.1 mi) </= 45 mm/km (</= 3 inches per mile) 46 - 75 mm/km (3.1 - 5 inches per mile) 76 - 110 mm/km (5.1 - 7 inches per mile) >110 mm/km (< 7 inches per mile) CA California-type profilograph PI5-mm 0.16 km (0.1 mi)a --</= 80 mm/km (</= 5 inches per mile)a -->80 mm/km (>5 inches per mile)a CO California-type profilograph PI2.5-mm 0.15 km (0.095 mi) </= 222 mm/km (</= 14 inches per mile) 222.1 - 252 mm/km (14.1 - 16 inches per mile) 252.1 - 378 mm/km (16.1 - 24 inches per mile) >378 mm/km (>24 inches per mile) CT ARAN inertial profiler IRI 0.16 kma (0.1 mi) >950 mm/kma (<60 inches per mile) 950 - 1260 mm/kma (60 - 80 inches per mile) 1261 - 1894 mm/kma (80.1 - 120 inches per mile) >1894 mm/kma (>120 inches per mile) DE Rolling straightedge -- -- -- -- -- --FL Rolling straightedge -- -- -- -- -- --GA Inertial profiler IRI 1.6 km(1.0 mi) --</= 750 mm/km (</= 47.5 inches per mile)a -->750 mm/km (>47.5 inches per mile)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/0.1 mi) IL California-type profilograph PI5-mm 0.16 km (0.1 mi) </= 8 mm/km (</= 0.5 inches per mile)b 9 - 160 mm/km (0.6 - 10 inches per mile) 161 - 235 mm/km (10.1 - 15 inches per mile) >235 mm/km (>15 inches per mile) IN California-type profilograph PI5-mm 0.16 km (0.1 mi) --</= 30 mm/0.16 km (</= 1.2 in/0.1 mi) 31 - 38 mm/0.16 km (1.21 - 1.5 in/0.1 mi) >38 mm/0.16 km (>1.5 in/0.1 mi) IA California-type profilograph PI5-mm 0.16 km (0.1 mi) </= 48 mm/km (</= 3 inches per mile) 49 - 110 mm/km (3.1 - 7 inches per mile) 111 - 160 mm/km (7.1 - 10 inches per mile) >160 mm/km (>10 inches per mile) KS California-type profilograph PI0.0 0.1 km (0.1 mi) </= 160 mm/km (</= 10 inches per mile) 161 - 475 mm/km (10.1 - 30 inches per mile) 476 - 630 mm/km (30.1 - 40 inches per mile)c >630 mm/km (>40 inches per mile) KY Inertial profiler RI 1.6 kma (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 inches per mile) 48 - 95 mm/km (3.1 - 6 inches per mile) >95 mm/km (>6 inches per mile) ME Rolling dipstick profiler IRI 0.2 km (0.12 mi) </= 945 mm/kma (</= 60 inches per mile) 946 - 1105 mm/kma (60.1 - 70 inches per mile) 1106 - 1260 mm/kma (70.1 - 80 inches per mile) >1260 mm/kma (>80 inches per mile) MD California-type profilograph PI5-mm 0.16 kma (0.1 mi) </= 63 mm/kma (</= 4.0 inches per mile) 64 - 110 mm/kma (4.1 - 7 inches per mile) 111 - 190 mm/kma (7.1 - 12 inches per mile) >191 mm/kma (>12 inches per mile) MA Inertial Profiler IRI 0.2 km (0.12 mi)a * * * *MI California-type profilograph or GM-type inertial profiler PI5-mmRQId 0.16 kma (0.1 mi) </= 63 mm/kma (</= 4 inches per mile)or RQI <45 64 - 158 mm/kma (4.1 - 10 inches per mile) or 45 </= RQI </= 53 -->158 mm/kma (>10 inches per mile)or RQI > 53 MN California-type profilograph PI5-mm 0.1 km (0.1 mi) </= 38.7 mm/km (</= 2.4 inches per mile) 38.8 - 78.9 mm/km (2.5 - 5 inches per mile) 79 - 118.3 m/km (5.1 - 7.5 inches per mile) >118.3 mm/km (>7.5 inches per mile) MS California-type profilograph PI5-mm 0.16 kma (0.1 mi) </= 79 mm/kma (</= 5 inches per mile) 80 - 110 mm/kma (5.1 - 7 inches per mile) 111 - 158 m/kma (7.1 - 10 inches per mile) >158 mm/kma (>10 inches per mile) MO California-type profilograph PI0.0 0.1 km (0.1 mi) </= 284 mm/km (</= 18 inches per mile) 285 - 395 mm/km (18.1 - 25 inches per mile) 396 - 711 m/km (25.1 - 45 inches per mile) >712 mm/km (>45 inches per mile) MT -- -- -- -- -- -- --NE California-type profilograph PI5-mm 0.2 km (0.1 mi) </= 75 mm/km (</= 5 inches per mile) 76 - 110 mm/km (5.1 - 7 inches per mile) 111 - 155 mm/km (7.1 - 10 inches per mile) >155 mm/km (>10 inches per mile) NV California-type profilograph PI5-mm 0.1 km (0.1 mi) --</= 80 mm/km (</= 5 inches per mile) -->80 mm/km (>5 inches per mile) NH GM-typeinertial profiler RN 0.16 kma (0.1 mi) ** ** ** **NJ Rolling straightedge -- -- -- -- -- --NM California-type profilograph PI5-mm 0.1 km (0.1 mi) </= 65 mm/km (</= 4 inches per mile) 66 - 80 mm/km (4.1 - 5 inches per mile) 81 - 160 m/km (5.1 - 10 inches per mile) >160 mm/km (>10 inches per mile) 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 kma (0.1 mi) </= 63 mm/kma (</= 4 inches per mile) 64 - 110 mm/kma (4.1 - 7 inches per mile) 111 - 190 m/kma (7.1 - 12 inches per mile) >190 mm/kma (>12 inches per mile) OK California-type profilograph PI5-mm 0.16 kma (0.1 mi) </= 79 mm/kma (</= 5 inches per mile) 80 - 110 mm/kma (5.1 - 7 inches per mile) 111 - 190 m/kma (7.1 - 12 inches per mile) >190 mm/kma (>12 inches per mile) OR California-type profilograph PI5-mm 016 kma (0.1 mi) </= 80 mm/kma (</= 5 inches per mile) 81 - 110 mm/kma (5.1 - 7 inches per mile) 111 - 155 mm/kma (7.1 - 10 inches per mile) >155 mm/kma (>10 inches per mile) PA California-type profilograph PI0.0 0.16 kma (0.1 mi) </= 442 mm/kma (</= 28 inches per mile) 443 - 536 mm/kma (28.1 - 34 inches per mile) 537 - 726 mm/kma (34.1 - 46 inches per mile) >726 mm/kma (>46 inches per mile) PR California-type profilograph PI5-mm 0.16 kma (0.1 mi) </= 110 mm/kma (</= 7 inches per mile) 111 - 205 mm/kma (7.1 - 13 inches per mile) - >205 mm/kma (>13 inches per mile) RI -- -- -- -- -- -- --SC Maysmeter MRN 1.6 kma (1.0 mi) </= 552 mm/kma (</= 35 inches per mile) 553 - 630 mm/kma (35.1 - 40 inches per mile) 631 - 868 mm/kma (40.1 - 55 inches per mile) >868 mm/kma (>55 inches per mile) SD Inertial profiler IRI 0.16 kma (0.1 mi) </= 868 mm/kma (</= 55 inches per mile) 869 - 1105 mm/kma (55.1 - 70 inches per mile) 1106 - 1262 mm/kma (70.1 - 80 inches per mile) >1262 mm/kma (>80 inches per mile) TN Maysmeter MRN 1.6 kma (1.0 mi) </= 315 mm/km a (</= 20 inches per mile) 316 - 475 mm/kma (20.1 - 30 inches per mile) 476 - 950 mm/kma (30.1 - 60 inches per mile) >950 mm/kma (>60 inches per mile) TX California-type profilograph PI0.0 0.16 kma (0.1 mi) </= 237 mm/kma (</= 15 inches per mile) 238 - 315 mm/kma (15.1 - 20 inches per mile) 316 - 630 m/kma (20.1 - 40 inches per mile) >630 mm/kma (>40 inches per mile) UT California-type profilograph PI5-mm 0.2 km (0.12 mi) a --</= 110 mm/km (</=7 inches per mile) a -->110 mm/km (>7 inches per mile)a VT Maysmeter IRI 0.32 kma (0.2 mi) <950 mm/kma (<60 inches per mile) 950 - 1090 mm/kma (60 - 69 inches per mile) 1091 - 1500 mm/kma (70 - 95 inches per mile) >1500 mm/kma (>95 inches per mile) VA South Dakota-type profiler IRI 0.16 kma (0.1 mi) </= 868 mm/kma (</= 55 inches per mile) 869 - 1105 mm/kma (55.1 - 70 inches per mile) 1106 - 1578 kma (70.1 - 100 inches per mile) >1578 mm/kma (>100 inches per mile) WAe Lightweight inertial profiler IRI 0.1 km (0.1 mi)a </= 946 mm/kma (</= 60 inches per mile) 947 - 1500 mm/kma (60.1 - 95 inches per mile) 1501 - 1815 mm/kma (95.1 - 115 inches per mile) >1815 mm/kma (>115 inches per mile) WV Maysmeter or inertial profiler MRN 0.16 km (0.1 mi) --</= 1000 mm/km (</= 65 inches per mile) 1001 - 1500 mm/km (66 - 97.5 inches per mile) >1500 mm/km (>97.5 inches per mile) WI California-type profilograph PI5-mm 0.16 kma (0.1 mi) --</= 158 mm/kma (</= 10 inches per mile) 159 - 237 m/kma (10.1 - 15 inches per mile) >237 mm/kma (>15 inches per mile) WY Inertial profiler IRI 0.16 kma (0.1 mi) *** *** *** ****Percent Within Limits Specification: Upper Spec Limit = 1500 m/km (95 inches per mile)
**Percent Within Limits Specification: Lower Spec Limit = RN = 4.1
***Statistical Based Specification: Full Pay approximately equal to 868-1105 mm/km (55-70 inches per mile)
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 inches per mile) and 630 mm/km (40 inches per mile), must also grind to 475 mm/km (30 inches per mile) or below.
d RQI: Ride quality index.
e Draft specification.
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 inches per mile) 45 -- 94 mm/km (3 -- 5.9 inches per mile) 95 - 160 mm/km (6 - 10 inches per mile) >160 mm/km (>10 inches per mile) AK -- -- -- -- -- -- --AZ California-type profilograph PI5-mm 0.16 kma(0.1 mi) <110 mm/kma(<7 inches per mile) 110 -- 142 mm/kma(7 - 9 inches per mile) -->142 mm/kma(>9 inches per mile) AR California-typeprofilograph, lightweight profiler PI5-mm 0.2 km (0.1 mi) </= 90 mm/km (</= 6 inches per mile) 91 - 110 mm/km (6.1 - 7 inches per mile) -->110 mm/km (>7 inches per mile) CA California-type profilograph PI5-mm 0.1 km (0.06 mi)a --</= 110 mm/km (</= 7 inches per mile)a -->110 mm/km (>7 inches per mile)a CO California-type profilograph PI2.5-mm 0.15 km (0.095 mi) </= 222 mm/kma (</= 14 inches per mile) 222.1 - 252 mm/kma (14.1 - 16 inches per mile) 252.1 - 378 mm/kma (16.1 - 24 inches per mile) >378 mm/kma (>24 inches per mile) CT California-type profilograph PI5-mm 0.15 km (0.1 mi)a </=160 mm/km (10 inches per mile)a 161 - 190 mm/km (10.1 - 12 inches per mile)a 191 - 315 mm/km (12.1 - 20 inches per mile)a >315 mm/km (>20 inches per mile)a DE CA profilograph or rolling straightedge PI5-mm 0.16 kma(0.1 mi) <50 mm/kma(<3.2 inches per mile) 50 - 200 mm/kma(3.2 - 12.7 inches per mile) -->200 mm/kma(>12.7 inches per mile) FL California-type profilograph PI5-mm 0.1 km (0.1 mi) </= 80 mm/km (</= 5 inches per mile) 81 - 95 mm/km (5.1 - 6 inches per mile) 96 - 110 mm/km (6.1 - 7 inches per mile) >110 mm/km (>7 inches per mile) GA Rainhart profilograph PI2.5-mm 0.4 kma(0.25 mi) --</= 110 mm/kma(</= 7 inches per mile) -->110 mm/kma(>7 inches per mile) HI California-type profilograph PI5-mm 0.16 kma(0.1 mi) --</= 157 mm/kma(</= 10 inches per mile) 158 - 236 mm/kma(10.1 - 15 inches per mile) >236 mm/kma(>15 inches per mile) ID California-type profilograph PI5-mm 0.1 km (0.1 mi) --</= 8 mm/0.1 km (</= 0.5 in/0.1mi) -->8 mm/0.1 km (>0.5 in/0.1mi) IL California-type profilograph PI5-mm 0.16 km (0.1 mi) </= 67 mm/km (</= 4.25 inches per mile) b 68 - 160 mm/km (4.26 - 10 inches per mile) 161 - 235 mm/km (10.01 - 15 inches per mile) >235 mm/km (>15 inches per mile) IN California-type profilograph PI5-mm 0.16 km (0.1 mi) </= 23mm/0.16 km (</= 0.9 in/0.1mi) 23 - 25 mm/0.16km (0.9 - 1.0 in/0.1 mi) -->25 mm/0.16 km (>1.0 in/0.1 mi) IA California-type profilograph PI5-mm 0.16 km (0.1 mi) </= 48 mm/km (�3 inches per mile) 49 - 110 mm/km (3.1 - 7 inches per mile) 111 - 160 mm/km (7.1 - 10 inches per mile) >160 mm/km (>10 inches per mile) KS California-type profilograph PI0.0 0.1 km (0.1 mi) </= 285 mm/km (</= 18 inches per mile) 286 -- 475 mm/km (18.1 -- 30 inches per mile) 476 -- 630 mm/km (30.1 -- 40 inches per mile)c >630 mm/km (>40 inches per mile) KY Rainhart profilographand inertial profiler PI2.5-mmRI 0.3 kma(0.19 mi) RI >/= 4.05 </=125 mm/kma (</= 8 inches per mile) 126 - 190 mm/kma(8.1 - 12 inches per mile) >190 mm/kma(>12 inches per mile) LA California-type profilograph PI5-mm Lot - �94 mm/km (</= 6 inches per mile) 95 - 126 mm/km (6.1 - 8 inches per mile) >126 mm/km (>8 inches per mile) ME -- -- -- -- -- -- --MD California-type profilograph PI5-mm 0.16 kma(0.1 mi) </= 63 mm/kma (</= 4.0 inches per mile) 64 - 110 mm/kma(4.1 - 7 inches per mile) 111 - 190 mm/kma(7.1 - 12 inches per mile) >191 mm/kma(>12 inches per mile) MA -- -- -- -- -- -- --MI California-type profilograph or GM-type inertial profiler PI5-mmRQI d 0.16 kma(0.1 mi) </= 63 mm/kma (</= 4 inches per mile)or RQI < 45 64 - 158 mm/kma (4.1 - 10 inches per mile)or 45 </=RQI </= 53 -->158 mm/kma(>10 inches per mile)or RQI > 53 MN California-type profilograph PI5-mm 0.16 km (0.1 mi) </= 63 mm/km (</= 4 inches per mile) 64 - 94 mm/km (4.1 - 6 inches per mile) 95 - 126 m/km (6.1 - 8 inches per mile) >126 mm/km (>8 inches per mile) MS California-type profilograph PI5-mm 0.16 kma(0.1 mi) --</= 110 mm/kma(</= 7 inches per mile) 111 - 190 m/kma(7.1 - 12 inches per mile) >190 mm/kma(>12 inches per mile) MO California-type profilograph PI0.0 0.1 km (0.1 mi) </= 284 mm/km (</= 18 inches per mile) 285 - 395 mm/km (18.1 - 25 inches per mile) 396 - 711 m/km (25.1 - 45 inches per mile) >712 mm/km (>45 inches per mile) MT California-type profilograph PI5-mm 0.16 kma(0.1 mi) </= 94 mm/kma(�6 inches per mile) 95 - 158 mm/kma(6.1 - 10 inches per mile) 159 - 237 m/kma (10.1 - 15 inches per mile) >237 mm/kma(>15 inches per mile) NE California-type profilograph PI5-mm 0.2 km (0.1 mi) </= 75 mm/km (</= 5 inches per mile) 76 - 155 mm/km (5.1 - 10 inches per mile) 156 - 230 mm/km (10.1 - 15 inches per mile) >230 mm/km (>15 inches per mile) NV California-type profilograph PI5-mm 0.1 km (0.1 mi) --</= 80 mm/km (</= 5 inches per mile) -->80 mm/km (>5 inches per mile) NH -- -- -- -- -- -- --NJ Rolling straightedge -- -- -- -- -- --NM California-type profilograph PI5-mm 0.1 km (0.1 mi) </= 80 mm/km (</= 5 inches per mile) 81 - 110 mm/km (5.1 - 7 inches per mile) 111 - 190 m/km (7.1 - 12 inches per mile) >190 mm/km (>12 inches per mile) NY California-type profilograph PI5-mm 0.16 km (0.1 mi)a </= 79 mm/kma(</= 5 inches per mile) 80 - 190 mm/kma(5.1 - 12 inches per mile) -->190/kma(>12 inches per mile) NC Rainhart profilograph PI5-mm 0.18 kma(0.11 mi) --</= 63 mm/kma(</= 4 inches per mile) -->63 mm/kma(>4 inches per mile) ND California-type profilograph PI5-mm 0.16 kma(0.1 mi) <8mm/0.16 kma(<0.3 in/0.1mi) 8 - 13 mm/0.16 kma(0.3 - 0.5 in/0.1mi) 14 -- 23 mm/0.16 kma(0.51 - 0.9 in/0.1mi) >23 mm/0.16 kma(>0.9 in/0.1mi) OH California-type profilograph PI5-mm 0.16 kma(0.1 mi) </= 78 mm/kma(</= 5 inches per mile) 79 - 110 mm/kma(5.1 - 7 inches per mile) 111 - 190 m/kma(7.1 - 12 inches per mile) >190 mm/kma(>12 inches per mile) OK California-type profilograph PI5-mm 0.16 kma(0.1 mi) </= 79 mm/kma(</= 5 inches per mile) 80 - 110 mm/kma(5.1 - 7 inches per mile) 111 - 190 m/kma(7.1 - 12 inches per mile) >190 mm/kma(>12 inches per mile) OR California-type profilograph PI5-mm 0.2 km (0.1 mi)a </= 80 mm/km (</= 5 inches per mile)a 81 - 110 mm/km (5.1 - 7 inches per mile)a -->110 mm/km (>7 inches per mile)a PA California-type profilograph PI0.0 0.16a(0.1 mi) </= 568 mm/kma(</= 36 inches per mile) -- -->568 mm/kma(>36 inches per mile) PR California-type profilograph PI5-mm 0.16 kma(0.1 mi) </= 110 mm/kma(</= 7 inches per mile) 111 - 205 mm/kma(7.1 - 13 inches per mile) -->205 mm/kma(>13 inches per mile) RI -- -- -- -- -- -- --SC Rainhart profilograph PI5-mm 0.4 kma(0.25 mi) --</= 158 mm/kma(</= 10 inches per mile) -->158 mm/kma(>10 inches per mile) SD California-type profilograph PI0.0 0.1 km (0.1 mi) </= 395 mm/km (</= 25 inches per mile) 396 - 550 mm/km (25.1 - 35 inches per mile) 551 - 630 mm/km (35.1 - 40 inches per mile) >630 mm/km (>40 inches per mile) TN Rainhartprofilograph PI2.5-mm 0.1 km (0.1 mi) --</= 160 mm/km (</= 10 inches per mile) 161 - 235 mm/km (10.1 - 15 inches per mile) >235 mm/km (>15 inches per mile) TX California-type profilograph PI0.0 0.16 kma(0.1 mi) 237 mm/kma(</= 15 inches per mile) 238 - 315 mm/kma(15.1 - 20 inches per mile) 316 -- 630 m/kma(20.1 - 40 inches per mile) >630 mm/kma(>40 inches per mile) UT California-type profilograph PI5-mm 0.2 km (0.12 mi)a --</= 110 mm/km (</= 7 inches per mile)a -->110 mm/km (>7 inches per mile) a VT -- -- -- -- -- -- --VA South Dakota-type profiler IRI 0.16 kma (0.1 mi) </= 946 mm/kma (</= 60 inches per mile) 947 - 1262 mm/kma (60.1 - 80 inches per mile) 1263 - 1578 km a(80.1 - 100 inches per mile) >1578 mm/kma (>100 inches per mile) WA California-type profilograph PI7.5-mm 0.1 km (0.1 mi) a </= 60 mm/km (</= 3.8 inches per mile)a 61 -- 100 mm/km (3.9 - 6.3 inches per mile) a >100 mm/km (>6.3 inches per mile) a,e --WV Maysmeter or inertial profiler MRN 0.16 km (0.1 mi) --</= 1000 mm/km (</= 65 inches per mile) 1001 - 1500 mm/km (66 - 97.5 inches per mile) >1500 mm/km (>97.5 inches per mile) WI California-type profilograph PI01-inch 0.16 kma (0.1 mi) </= 400 mm/kma (</= 25.3 inches per mile) 401 - 700 mm/kma (25.4 - 44.3 inches per mile) 701 - 800 m/kma (44.4 - 50.7 inches per mile) f >800 mm/kma (>50.7 inches per mile) WY California-type profilograph PI5-mm * * * * * * Perf. Related Spec (PCC thickness, strength, smoothness) >80 mm/km (>5.0 inches per mile).
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 inches per mile) and 630 mm/km (40 inches per mile), must also grind to 475 mm/km (30 inches per mile) or below.
d RQI: Ride quality index.
e For PI greater than 100 mm/km (6.3 inches per mile), must also grind to 100 mm/km (6.3 inches per mile) or less.
f For PI greater than 700 mm/km (44.3 inches per mile), must also grind to 700 mm/km (44.3 inches per mile) or less.
Table 16. Estimated equivalent PI0.0 and IRI values for PI-based smoothness specifications for new AC pavement.PI5-mm Specification:
Agency Existing Full-Pay Range, mm/km Climatic Zone Estimated PI0.0 Full-Pay Range, mm/km SEE, mm/kma Estimated PI0.0 Estimated IRI Full-Pay Range, mm/km Estimated IRI SEE, mm/kma 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 </= 80b DF,WF </= 384 84 </= 1190 292 IL 9 - 160 WF 288 - 493 84 922 - 1493 292 IN </= 187c 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:
Agency Existing Full-Pay Range, mm/km Climatic Zone Estimated PI0.0 Full-Pay Range, mm/km SEE, mm/kma Estimated PI0.0 Estimated IRI Full-Pay Range, mm/km Estimated IRI SEE, mm/kma CO 222.1 - 252 DF,WF 415 - 446 48 1295 - 1381 232 PI0.0 Specification:
Agency Existing Full-Pay Range, mm/km Climatic Zone Estimated PI0.0 Full-Pay Range, mm/km SEE, mm/kma Estimated PI0.0 Estimated IRI Full-Pay Range, mm/km Estimated IRI SEE, mm/kma 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.
Table 17. Estimated equivalent PI0.0 and IRI values for PI-based smoothness specifications for AC overlays on AC pavement.PI5-mm Specification:
Agency Existing Full-Pay Range, mm/km Climatic Zone Estimated PI0.0 Full-Pay Range, mm/km SEE, mm/kma Estimated PI0.0 Estimated IRI Full-Pay Range, mm/km Estimated IRI SEE, mm/kma 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 </= 80b DF,WF < 376 74 < 1298 288 IL 9 - 160 WF 247 - 467 72 908 - 1425 266 IN </= 187c 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:
Agency Existing Full-Pay Range, mm/km Climatic Zone Estimated PI0.0 Full-Pay Range, mm/km SEE, mm/kma Estimated PI0.0 Estimated IRI Full-Pay Range, mm/km Estimated IRI SEE, mm/kma CO 222.1 - 252 DF,WF 413 - 446 43 1399 - 1490 230 PI0.0 Specification:
Agency Existing Full-Pay Range, mm/km Climatic Zone Estimated PI0.0 Full-Pay Range, mm/km SEE, mm/kma Estimated PI0.0 Estimated IRI Full-Pay Range, mm/km Estimated IRI SEE, mm/kma 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.
TFHRC Home | FHWA Home | Feedback
|