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Analysis of the Long-Term Pavement Performance Data for the Idaho GPS and SPS Sections
FINAL REPORT
NIATT Project No. KLK 481
ITD Project No. SPR-0003(014) RP 160
Prepared for
Idaho Transportation Department Mr. Michael Santi, PE
Assistant Material Engineer
Prepared by
Fouad Bayomy Professor of Civil Engineering and Principal Investigator
Hassan Salem
Graduate Research Assistant and
Lacy Vosti Undergraduate Research Assistant
National Institute for Advanced Transportation Technology Center for Transportation Infrastructure, CTI
University of Idaho
December 2006 (Revised June 2007)
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ABSTRACT
This project addresses the analysis of the Long-Term Pavement Performance (LTPP) data for
the LTPP sites in Idaho. The goal was to determine the performance trends for the pavement
in Idaho as found in the LTPP experiments. The research also investigate into the use of the
data to develop (as much possible) models that enable the prediction of the seasonal variation
effects on the pavement materials (soils and asphalt mixes). In addition, the project looks into
the applicability of the LTPP data in Idaho for the use and implementation of the new
Mechanistic-Empirical Pavement design Guide (MEPDG).
Idaho participates in the LTPP program with 13 sites of general pavement studies
(GPS) that include GPS-1, 3, 5 and 6A experiments. There is only specific pavement studies
(SPS) experiment in Idaho (SPS-3). Idaho participates in the SPS-3 with 12 sections. All data
from all the sites were obtained and accumulated on a mini database. Analysis of the Idaho
data was supplemented with data from few LTPP sites located in adjacent states with similar
environments.
Analysis of the performance data including roughness and rutting revealed that
Continuous concrete pavements performed best, followed by jointed concrete pavements.
The asphalt pavements on granular bases and existing asphalt overlays on asphalt pavements
showed mediocre performances. That was largely due to the big gap in data at these sites.
For SPS sites regarding cracking and rutting, the various types of surface treatments tested at
the SPS 3 experiment were not effective at improving pavement conditions. Results showed
that to improve pavement roughness, a thin overlay is the best treatment option, followed by
the placement of a slurry seal coat. Placing chip and crack seal treatments did not show
significant impact on pavement roughness.
As part of the outcomes of this project, a mini-LTPP database for the LTPP sections
in Idaho was developed in MDB file format and series of Excel files that include all Idaho
data. In addition, models were developed based on analysis of national data for the subgrade
and asphalt concrete moduli. An investigation into the implementation of the MEPDG in
Idaho indicated that the current performance data in the Idaho sites are not sufficient for any
meaningful calibration of the performance models in the new design guide.
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ACKNOWLEDGEMENTS
This project was funded by the Idaho Transportation Department (ITD) under a contract with
the National Institute for Advanced Transportation Technology (NIATT), project number
KLK481. The ITD and FHWA supports are greatly appreciated.
Many Individuals have contributed to the progress of this project. From ITD, thanks are due
to Mr. Mike Santi, PE, Assistant Materials Engineer and Mr. Bob Smith, PE who oversaw
the project in its initial stages. Thanks are also due to Mr. Jeff Miles for his continued
support of our research program and his insight into the practicality of the research outcomes.
Many undergraduate students at the University of Idaho contributed at various stages to the
analysis of data in this report. In addition to the co-author of this report (Ms. Lacy Vosti),
Mr. Frank Eckwright has contributed a great deal of effort into the computer runs of the
Mechanistic-Empirical Design Guide software, and developed a worksheet to facilitate data
input. He contributed greatly to the writing of Chapter 7 of this report. In addition, Mr.
Ahmad Abu Abdo, a graduate student at UI, helped a lot in training the undergraduate
students. Thanks to all these individuals and their efforts are greatly appreciated.
The support of the NIATT administrative staff is also acknowledged. Ms. Judy LaLonde and
Debbie Foster have provided close monitoring for the project progress reports and budget.
Mr. Roger Saunders reviewed and edited the initial draft of the manuscript of the final report.
Authors are very thankful to all their efforts and support.
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TABLE OF CONTENTS 1. INTRODUCTION.................................................................................. 1
1.1 Background............................................................................................................... 1 1.2 Objectives ................................................................................................................. 2 1.3 Scope......................................................................................................................... 3 1.4 Methodology............................................................................................................. 4
2. IDAHO MINI LTPP DATABASE ....................................................... 5 2.1 LTPP dataBase.......................................................................................................... 5 2.2 LTPP sites in Idaho................................................................................................... 5 2.3 Mini Database ........................................................................................................... 6
3. REVIEW OF CURRENT DATA ANALYSIS REPORTS.............. 10 3.1 introduction............................................................................................................. 10 3.2 LTPP Smoothness And Distress Studies: A Review .............................................. 10 3.3 ROUGHNESS STUDIES ....................................................................................... 11
3.3.1 Factors Affecting Pavement Smoothness ....................................................... 11 3.3.2 Roughness Development of AC Pavements ................................................... 12 3.3.3 Roughness Development of PCC Pavements ................................................. 13 3.3.4 Roughness Characteristics of Overlaid Pavements......................................... 16 3.3.5 Models to Predict Roughness Development ................................................... 16 3.3.6 Transverse, Seasonal and Daily Variations of IRI.......................................... 17 3.3.7 Relationships Between IRI and Profile Index (PI) ......................................... 17
3.4 FLEXIBLE PAVEMENT MAINTENANCE EFFECTIVENESS......................... 26 3.4.1 Distress Variability ......................................................................................... 26 3.4.2 Description of LTPP Experiment SPS-3......................................................... 27 3.4.3 SPS-3 Performance Findings from Previous Studies...................................... 28 3.4.4 Effects of Flexible Pavement Maintenance Treatment on Roughness ........... 33 3.4.5 Effects of Flexible Pavement Maintenance Treatment on Rutting................. 35 3.4.6 Effects of Flexible Pavement Maintenance Treatment on Fatigue Cracking . 35
4. DATA MINING AND ANALYSIS – IDAHO DATA ...................... 37 4.1 INTRODUCTION .................................................................................................. 37 4.2 Selected SITES ....................................................................................................... 37 4.3 Methodology........................................................................................................... 39 4.4 Analysis................................................................................................................... 40
4.4.1 GPS-1: Asphalt Pavements On Granular Bases.............................................. 41 4.4.2 GPS-3: Jointed Concrete Pavements .............................................................. 45 4.4.3 GPS-5: Continuous Concrete Pavements........................................................ 47 4.4.4 GPS-6A: Existing Asphalt Overlays on Asphalt Pavements.......................... 50 4.4.5 SPS-3: Pavement Treatment Performance...................................................... 54
4.5 Conclusions form Idaho Sites ................................................................................. 58 4.5.1 GPS Sites ........................................................................................................ 58 4.5.2 SPS Sites ......................................................................................................... 58
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5. SEASONAL VARIATION OF SUBGRADE RESILIENT MODULUS – NATIONAL LTPP DATA ...................................................................... 59
5.1 INTRODUCTION .................................................................................................. 59 5.2 Backgorund on the LTPP SMP Study .................................................................... 59 5.3 modulus-moisture relationship for subgrade soils .................................................. 61
5.3.1 Moisture Effects on Soil Resilient Modulus................................................... 61 5.3.2 Temperature Effects on Subgrade Soil Resilient Modulus............................. 63 5.3.3 Subgrade Moisture Prediction Using the Integrated Climatic Model (ICM) . 64 5.3.4 Seasonal Variation and Seasonal Adjustment Factors.................................... 65
5.4 LTPP-SMP DATA requisition and preparation...................................................... 66 5.5 DATA ANALYSIS................................................................................................. 67
5.5.1 Moisture and Modulus Variation with Time .................................................. 68 5.5.2 Model Development for Plastic Soils ............................................................. 68 5.5.3 Estimating Seasonal Adjustment Factors........................................................ 73
5.6 Conclusions of SMP Data Analysis for Subgrade Soils ......................................... 78 6. SEASONAL VARIATION OF THE ASPHALT CONCRETE MODULUS – NATIONAL LTPP DATA................................................. 79
6.1 INTRODUCTION .................................................................................................. 79 6.2 modulus-Temperature relationship for AC layer.................................................... 79
6.2.1 Seasonal Variations in the AC Layer Elastic Modulus................................... 79 6.2.2 Relating Temperature Variation to AC Modulus............................................ 80 6.2.3 Pavement Temperature Prediction Models..................................................... 81
6.3 LTPP DATA ACQUISITION and preparation ...................................................... 82 6.3.1 DATA ANALYSIS......................................................................................... 84 6.3.2 Temperature and Modulus Variation with Time ............................................ 84 6.3.3 AC Layer Temperature at Various Depths Versus Modulus .......................... 84 6.3.4 AC Modulus Versus Mid-Depth Temperature ............................................... 86 6.3.5 AC Layer Modulus Prediction Models ........................................................... 91 6.3.6 Estimating the Seasonal Adjustment Factor ................................................... 94
6.4 Conclusions of the SMP Data Analysis for Aspahlt Modulus................................ 96 7. APPLICABILITY OF THE IDAHO LTPP DATA FOR THE IMPLEMENTATION OF MEPDG.......................................................... 98
7.1 Introduction............................................................................................................. 98 7.2 Backgorund on the MEPDG ................................................................................... 98 7.3 MEPDG Inputs and Availability in the LTPP Database for Idaho......................... 99 7.4 Recommendation for Implementation at the State level....................................... 100
8. CONCLUSIONS ................................................................................ 106 9. REFERENCES................................................................................... 109 10. APPENDICES .................................................................................... 116
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LIST OF FIGURES
Figure 2-1: LTPP Sites in Idaho ............................................................................................... 7 Figure 2-2 GPS and SPS Sites in Neighboring States that were Used in Analysis ................. 8 Figure 3-1: Relationship between the inertial profiler IRI and the PI5-mm (PI0.2-in) .......... 18 Figure 3-2: Relationships between the profilograph PI5-mm (PI0.2-in) and the IRI values.. 21 Figure 3-3: Relationships between the IRI and the simulated PI response............................. 22 Figure 3-4: Correlation analysis of the IRI and simulated PI5-mm (PI0.2-in) values produced by the lightweight profiler. ..................................................................................................... 24 Figure 4-1: GPS-1 Roughness Trends in Idaho ...................................................................... 42 Figure 4-2: GPS-1 Rutting Trends in Idaho............................................................................ 43 Figure 4-3: GPS-3 Roughness Trends in Idaho ...................................................................... 46 Figure 4-4: GPS-3 Rutting Trends in Idaho and Washington................................................. 47 Figure 4-5: GPS-5 Roughness Trends in Idaho ...................................................................... 48 Figure 4-6: GPS-5 Rutting Trends in Idaho and Oregon........................................................ 49 Figure 4-7: GPS-6A Roughness Trends in Idaho ................................................................... 51 Figure 4-8: GPS-6A Rutting Trends in Idaho, Washington and Wyoming............................ 52 Figure 5-1: Variation of Modulus and Moisture with Time for Various Soil Types at the Selected LTPP Sites................................................................................................................ 69 Figure 5-2: Model Development for Non-Plastic Soils .......................................................... 72 Figure 5-3: Modulus-Moisture Relationships for Non-Plastic Soils. ..................................... 75 Figure 5-4: Variation of the Seasonal Adjustment Factor with the Moisture Ratio for Different Soil Types................................................................................................................ 77 Figure 6-1: Variation of Modulus and Temperature with Time for Three Different LTPP Sites......................................................................................................................................... 85 Figure 6-2: Modulus Versus Pavement Temperature at Various Depths. .............................. 87 Figure 6-3: Modulus - Temperature Relationship for Five Sites from Nonfreezing Zones. .. 88 Figure 6-4: Modulus – Temperature Relationship for Six Sites from Freezing Zones .......... 90 Figure 6-5: Comparing The Models to Data from Different Zones........................................ 93 Figure 6-6: Estimated AC Layer Modulus Shift Factor for Both Nonfreezing and Freezing Zones....................................................................................................................................... 96
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LIST OF TABLES
Table 2-1: Available LTPP Sites in Idaho and Their Locations............................................... 9 Table 2-2: Selected sites in Neighboring States that were used in the Analysis ...................... 9 Table 3-1: 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) , Kelly et al. (2002)...................................................................................................................................... 25 Table 3-2: SPS-3 Core Experimental Sections. ...................................................................... 27 Table 4-1: Pavement Information for GPS-1 Sites ................................................................. 37 Table 4-2 Climatic Information for GPS and SPS Sites in Idaho........................................... 38 Table 4-3: Pavement Information for GPS-3, 5, 6A and SPS-3 Sites .................................... 38 Table 4-4: Specific Location and Climate Information for Non-Idaho Sites ......................... 39 Table 4-5: Pavement Information for Non-Idaho Sites .......................................................... 39 Table 5-1: Experimental Design and Data Elements for the LTPP Seasonal Monitoring Program (Rada et al, 1994) ..................................................................................................... 60 Table 5-2: Selected LTPP Sites and Subgrade Soil Characterizations ................................... 67 Table 5-3: SAS Output for Regression Analyses for Modulus-Moisture Relationship for Plastic Soils............................................................................................................................. 71 Table 5-4: SAS Output for Regression Analyses for Modulus-Moisture Relationship for Non-Plastic Soils............................................................................................................................. 74 Table 5-5: Parameters k1 and k2 for the SAF Model (Equation 7) ........................................ 76 Table 6-1: Selected LTPP Sites and Their AC Layer Properties............................................ 82 Table 6-2: Estimated Constants of The Exponential Function for The different Sites........... 89 Table 7-1 Example of Inputs for the MEPDG Using Data from Idaho Site 16-1001 .......... 101
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1. INTRODUCTION
1.1 BACKGROUND
Pavement performance is a critical factor in the operation, planning, and engineering of
highway facilities. It affects the safety and comfort of the highway user. It is also an
economic factor in that engineers want to extend the pavement life to the most economical
extent possible. Understanding "why" some pavements perform better than others is key to
building and maintaining a cost-effective highway system. That's why in 1987, the Long-
Term Pavement Performance (LTPP) program - a comprehensive 20-year study of in-service
pavements - began a series of rigorous long-term field experiments monitoring more than
2,400 asphalt and Portland cement concrete pavement test sections across the U.S. and
Canada. LTPP was designed as a partnership with the States and Provinces. One of its goals
was to help the States and Provinces make decisions that will lead to better performing and
more cost-effective pavements.
The LTPP research program is an outgrowth of the Strategic Highway Research Program
(SHRP) which initiated the original LTPP program in 1987 to study the long-term
performance of the in-service pavements. At the completion of SHRP program, in 1992, the
Federal Highway Administration (FHWA) continued and expanded the LTPP program.
Under FHWA, a seasonal monitoring program (SMP) was initiated within the LTPP program
to focus on the effects of seasonal changes on pavement performance. Data collected from
the various studies in the LTPP program are accessible via the LTPP Datapave online web
site (http://www.ltpp-products.com/DataPave/index.asp), which allows access to almost all
data in the national LTPP database.
The Idaho Transportation Department (ITD) is sponsoring research into the pavement
performance specific to the state of Idaho. Such specific research is needed to understand the
seasonal changes on a number of pavement types in the state. This report is a compilation of
University of Idaho research into the data collected on pavements in Idaho, in particular, and
the uses of national pavement data for modeling pavement performance, in general.
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Specifically, this research project focuses on the analysis of the data from LTPP sits in Idaho.
The Idaho sites include 13 general pavement studies (GPS) sites. The GPS experiments in
Idaho are GPS-1 (9 sites), GPS-3 (2 sites), GPS-5 (one site) and GPS-6A (one site). All these
experiments are studies of in-service asphalt concrete pavements. GPS-1 sites are asphalt
pavements built on granular bases, GPS-3 sites are for jointed concrete pavements, GPS- 5
site is for continuous concrete pavement, and the GPS-6A site is for existing asphalt overlay
over asphalt pavement.
Idaho also participates in the LTPP specific pavement studies (SPS) in the experiment of
preventive maintenance effectiveness for asphalt pavements designated as SPS-3. The SPS-3
experiment design matrix, in Idaho, includes 12 sections, which are located in three sites.
Each site is divided into four sections for different treatment methods (crack seal, chip seal,
slurry seal and thin overlay). Data have been collected by both the state and the FHWA-
LTPP program. However, very limited analysis has been done to address pavement
performance problems specific to the state conditions. The SPS-3 experiments are of great
importance since they address current maintenance techniques adopted by ITD. Analysis of
data available in the LTPP information management system (IMS) shall help the state
evaluates the effectiveness of these techniques in Idaho environment.
This project represents the only activity related to data analysis of the Idaho LTPP sections at
the state level. At the national level, there are few NCHRP projects that address LTPP data
analysis. However, the NCHRP projects are neither particular nor focused on the Idaho
sections, especially that there is no current NCHRP project for preventive maintenance.
Review of all previous data analysis reports developed by FHWA contractors or by NCHRP
contractors is presented later in this report.
1.2 OBJECTIVES
The overall objective of this project is to analyze the LTPP data related to the Idaho sections
for the following purposes:
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• Develop a mini-database that includes Idaho LTPP data.
• Study the performance characteristics of pavements in Idaho in general and the
effectiveness of the preventive maintenance techniques used in the Idaho SPS-3 sites.
• Develop pavement performance models, as far as the data allows. These models
should incorporate the effect of pavement structure, material properties, and
environment and traffic loading conditions.
• Investigate how the Idaho LTPP data can support the implementation of the new
Mechanistic-Empirical Pavement Design Guide (MEPDG) that is expected to be
adopted by AASHTO in the near future.
1.3 SCOPE
The scope of this project is limited to procure all LTPP data for Idaho sections from the
national database via the DataPave software. Analyze the data to develop performance
trends, and investigate the applicability of previously developed models, or established trends
from the national studies, to the pavement sections in Idaho. As mentioned earlier, the
majority of GPS sections in Idaho are for the GPS-1 experiment. Thus, the focus is on the
performance characteristics of pavements built on granular bases, which is the main feature
of the pavements in GPS-1 experiment. For the Specific Pavement Studies (SPS) sections,
only sections for the SPS-3 (Preventative Maintenance Effectiveness) experiment are
available in Idaho. While all data will be analyzed, the focus is on developing performance
trends for:
• Smoothness or Roughness variability, • Distress development.
The variability of these performance indicators may be investigated in relation to:
• Structural support parameters (layer thicknesses and elastic moduli) • Site conditions, such as freeze or non-freeze, dry or wet.
For the SPS sections, the target is to identify how significant are the various maintenance
techniques adopted at these sections.
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1.4 METHODOLOGY
The following tasks were undertaken to address the objectives and scope of the project:
Task 1: Develop Idaho Mini LTPP Database: All LTPP data related to Idaho sections
was procured from the national IMS. It was done by means of DataPave software. At early
stages of the project, DataPave version 3.0 was used, and when the FHWA moved to the
online version, data was acquired via the DataPave online. In addition, Microsoft Access
Database files were procured directly from LTPP headquarters. LTPP general data release
and management protocols were followed. The procured data was collected in one mini-
database (in series of Excel files) to facilitate analysis. The database also includes the MS-
Access files for future reference. In addition to the raw data files, analyzed data are also
included in the various database files. The files are provided in electronic format on a CD.
Task 2: Review Current Data Analysis Reports: Relevant reports published by
FHWA and NCHRP were reviewed to establish a methodology for the analysis.
Task 3: Data Mining and Analysis: The collected data was analyzed using basic
statistical tools. The data was analyzed to establish performance trends, and investigate the
level of its applicability to available performance models.
Task 4: Develop an Implementation Plan for new AASHTO 2002 Design Guide,
which is referred to later as the Mechanistic-Empirical Pavement Design Guide (MEPDG).
Depending upon the released information on the AASHTO 2002 design guide and as much
as the data available allowed, the analyzed data was used to determine its applicability for the
use in the MEPDG.
This report was prepared to address the results of these tasks, and document the conclusions
of the data analysis. The following chapters are organized to address the tasks listed above.
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2. IDAHO MINI LTPP DATABASE
2.1 LTPP DATABASE
The LTPP program includes more than 2400 sites in North America. These sites include
about 800 GPS sites and about 1600 SPS sites. The national database of all these sites is
housed in a central database, referred to the Information Management system, IMS), which
that is accessible via the DataPave online software. The national database includes more than
500 in 22 modules with thousands of data fields. Additional data are stored offline, which
can be accessed via the LTPP headquarters. However, these additional data files are massive
and are not needed for many of the analysis purposes. For example, there is a separate central
traffic database that houses all the traffic data that states provide to the LTPP. Summary of
the traffic information is stored in the national main IMS. The database is updated annually
since the data collection process is a continuous process since the launch of the LTPP
program. For the majority fo data users, data can be obtained from the DataPave Online.
2.2 LTPP SITES IN IDAHO
The state of Idaho participates in the FHWA long-term pavement performance (LTPP)
program, with 13 general pavement studies (GPS) sites. The GPS experiments in Idaho are
GPS-1 (9 sites), GPS-3 (2 sites), GPS-5 (one site) and GPS-6A (one site). All these
experiments are studies of in-service asphalt concrete pavements. GPS-1 sites are asphalt
pavements built on granular bases, GPS-3 sites are for jointed concrete pavements, GPS-5
site is for continuous concrete pavement, and the GPS-6A site is for existing asphalt overlay
over asphalt pavement. Also, the state participates in the LTPP specific pavement studies
(SPS) in the experiment of preventive maintenance effectiveness for asphalt pavements
designated as SPS-3. The SPS-3 experiment design matrix, in Idaho, includes 12 sections,
which are located in three sites. Each site is divided into four sections for different treatment
methods (crack seal, chip seal, slurry seal and thin overlay). Figure 2-1 shows the different
site locations in Idaho.
6
The main sites specific information like; the site latitude, longitude, elevation, highway class,
route number, county located in, construction date, freezing index, average precipitation and
number of days above 32 C are all presented in Table 2-1.
Due to limited data for some experiments, two GPS-5 sites in Oregon and two GPS-6A sites
from both Washington and Wyoming were used in the data analysis of some criteria. The
location of these GPS and SPS sites can be seen in Figure 2-2 while specific site information
for non-Idaho sites are presented in Table 2-2
2.3 MINI DATABASE
For the purpose of this project, all data in the LTPP Database for all Idaho sites have been
obtained and updated. The versions of the data release that were used to retrieve LTPP data
for the analysis in this project included the DataPave 3 that was released in 2002, and
subsequent standard data release versions 17, 18, 19 and 20 for the years 2003, 2004, 2005,
and 2006.
All data retrieved from the LTPP Database were accumulated in one single MS-Access
database file, and stored in a folder named “ID_LTTP Mini Database” and is provided in
the project CD (attached to this report). The main database file (ID_LTPP Data.mdb)
includes all the raw data from LTTP tables. The tables in this file are structured in
accordance to the LTPP table structure system. To facilitate the identification of all tables
and codes, a (CODE.mdb) file is also included. The traffic data tables from the LTPP
database for all sections in Idaho are included in the file (ID_Traffic.mdb). The user will
need to use the MS-Access Database software to open these database files.
In addition, all files for the data analysis are presented in series of Excel sheets and charts.
They are grouped and stored in the same folder “ID_LTPP Mini Database”.
7
Figure 2-1: LTPP Sites in Idaho
9034
N GPS-1 (9 Sites)
GPS-3 (2 Sites)
GPS-5 (1 Site)
GPS-6A (1 Site)
SPS (3x3 = 9Sites)
Idaho Sites (5)
1001
9032
1005
1009
1020
1007 5025
3017
6027
1021
SPS_B SPS_C
1010 SPS_A
3023
9
Table 2-1: Available LTPP Sites in Idaho and Their Locations
1001 8/1/1973 Kootenai 2 95 2150 47.77 116.79 217 692.9 16.081005 7/1/1975 10/1/1999 Adams 2 95 3232 44.63 116.44 399.1 627 44.861007 6/1/1972 8/1/1997 Twin Falls 2 30 3771 42.59 114.7 326 253.8 23.96
1009 10/1/1974 Cassia 1 84 3025 42.47 113.38 350.61 261.77 29.131010 10/1/1969 8/1/1997 Jefferson 1 15 4775 43.68 112.12 665.21 303.42 19.041020 9/1/1986 Jerome 2 93 4097 42.74 114.44 327.59 280.01 42.121021 10/1/1985 Jefferson 2 20 4849 43.65 111.93 622 341.55 17.249032 10/1/1987 Kootenai 2 95 2602 47.64 116.87 258.65 717.98 10.59
3017 9/1/1986 Power 1 86 4254 42.64 113.05 356.24 341.82 47.35
SPS3320: Slurry
330: Crack B Jefferson 2 20 4849 43.65 111.93 622 341.55 17.24
350: Chip C 8/1/1997 Jefferson 1 15 4775 43.68 112.12 665.21 303.42 19.04
327.59 280.01 42.12
De-Assign date
Route # Elev, (m)
Freez Index (C-Days)
5
A Jerome 2 93 4097 42.74 114.44
42.45 111.35 817.16 379.77Bear Lake 2 30 6056GPS6A 6027 9/1/1960 8/1/1997
112.21 538.28 399.28 21.92
47.59
GPS5 5025 9/1/1972 8/3/1995 Bannock 1 15 4979 42.38
43.84 116.76 278.29 295.29Payette 1 84 2503GPS3
3023 10/1/1983
116.5 315.53 806.69 9
Days above 32 C
GPS1
9034 10/1/1988 Bonner 2 95 2119 48.42
Class Lat, Deg Long, Deg
Precipit. (mm)
Experiment Site Const. Date
County
Table 2-2: Selected sites in Neighboring States that were used in the Analysis
Experiment State SiteRoute Number
Functional Class Elev. (ft) ecipitation (Freezing Index (C‐days)
Climatic Region
GPS‐1 WA 1002 12 Rural Arterial 1557 18.7 155.7 Dry FreezeGPS‐3 WA 3013 195 Rural Arterial 2356 17 292.1 Dry FreezeGPS‐5 OR 5008 84 Rural Interstate 2729 17.2 179.8 Dry Freeze
UT 7082 15 Rural Interstate 4527 16.7 411.45 Dry FreezeWA 6056 195 Rural Arterial 2545 19.9 175.3 Dry FreezeWA 7322 195 Rural Arterial 2545 21.1 224.5 Wet FreezeWY 6032 22 Rural Mjr. Collector 6156 17.5 894.3 Dry FreezeGPS‐6A
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3. REVIEW OF CURRENT DATA ANALYSIS REPORTS
3.1 INTRODUCTION
This chapter discusses the previous research done related to the objectives of this project
through a comprehensive literature review of previous roughness and distress studies and the
effectiveness of the flexible pavement maintenance. Appendix A contains a bibliography of the
related FHWA and NCHRP Reports indicating which were reviewed for use.
3.2 LTPP SMOOTHNESS AND DISTRESS STUDIES: A REVIEW
The original Long-Term Pavement Performance (LTPP) program was established by the
Strategic Highway Research Program (SHRP) in 1987 to study the long-term performance of the
in-service pavements. The objective of the LTPP program and its state of progress since its
inception has been the subject of many publications. The original SHRP-LTPP program included
two main experiments, the General Pavement Studies (GPS) and the Specific Pavement Studies
(SPS). At the conclusion of the SHRP in 1992, the LTPP program continued under the
management of the Federal Highway Administration (FHWA). The FHWA-LTPP program team
recognized the need to study the environmental impacts on pavement performance.
Consequently, the FHWA-LTPP team launched the Seasonal Monitoring Program (SMP) as an
integral part of the LTPP program. The primary objective of the SMP was to study the impacts of
temporal variations in pavement response and materials properties due to the separate and
combined effects of temperature, moisture and frost/thaw variations. The GPS sections generally
represent pavements that incorporate materials and structural designs used in standard
engineering practice in the United States. The GPS test sections had been in service for some
time when they were accepted into the LTPP program. Roughness data collection at these test
sections has been performed at regular intervals after the test sections were accepted into the
LTPP program. However, the initial International Roughness Index (IRI) of these test sections
are not known. The SPS experiments were designed to study the effect of specific design features
on pavement performance. Each SPS experimental test site consists of multiple test sections,
each of which is 152 m in length.
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3.3 ROUGHNESS STUDIES
Several research projects that used LTPP data to study roughness progression have been
performed during the past several years. Perera et al. (1998) had performed the first
comprehensive analysis of roughness progression at LTPP sections. He investigated the time-
sequence roughness data at GPS test sections to study trends in development of roughness, and
developed models to predict roughness. An evaluation of roughness data collected for the SPS-1,
-2, -5 and –6 experiments were also performed. Khazanovich et al. (1998) used LTPP data to
investigate common characteristics of good and poorly performing PCC pavements. They
grouped jointed plain concrete (JPC), jointed reinforced concrete (JRC) and continuously
reinforced concrete (CRC) pavements into three groups (poor, normal and good) based on time
vs IRI relationships, and examined factors contributing to differences in pavement performance.
Owusu-Antwi et al. (1998) and Titus-Glover et al. (1998,1999) used LTPP data to analyze the
performance of PCC pavements. They determined design features and construction practices that
enhance pavement performance, and developed models to predict roughness. Simpson et al.
(1994) performed a sensitivity analysis of IRI data at the GPS sections.
Profile data collected at GPS-3 and 4 sections were analyzed by Byrum (2000). This research
developed a curvature index to quantify slab shape from profile elevation data, and showed that
slab curvature was related to PCC pavement performance. An analysis of pavement performance
trends for test sections in SPS-5 and SPS-6 projects was also performed by Daleiden et al.
(1998). In this study, a comparison of performance trends of different test sections was made to
evaluate the effect of different rehabilitation treatments. The parameters studied were pavement
distress (e.g., fatigue cracking, longitudinal cracking, transverse cracking), roughness, rutting,
and deflection data. Von Qunitus et al. (2000) used LTPP data to study the relationship between
changes in pavement surfaces distress of flexible pavements to incremental changes in IRI.
3.3.1 Factors Affecting Pavement Smoothness
The data available in the LTPP Information Management System (IMS) was used by Perera and
Kohn (2001) to determine the effect of design and rehabilitation parameters, climatic conditions,
12
traffic levels, material properties, and extent and severity of distress that cause changes in
pavement smoothness. The IRI was used as the measure of pavement smoothness.
The pavement types in the GPS experiment that were studied in this research project were:
asphalt concrete (AC) on granular base, AC on stabilized base, jointed plain concrete, jointed
reinforced concrete, continuously reinforced concrete, AC overlays of AC pavements, and AC
overlays on concrete pavements. Roughness trends over time for each of these pavement types
were studied. Subgrade, climatic and pavement material properties that influence the roughness
progression on each of these pavement types were identified.
In their final report, Perera and Kohn (2001) concluded that the cause for the high rate of
increase of roughness that were observed on some of the sections prior to the end of their design
life can be attributed to several causes. If a pavement is subjected to its design traffic volume in a
time period that is less than its intended design life, the roughness of the section is expected to
increase rapidly. Also, if the pavement is not adequately designed based on the subgrade and
environmental conditions at the site, the roughness of the section can increase at a high rate. It
was noted that the pavements that are at higher levels of roughness generally were subjected to a
several factors that were identified to be causing high roughness levels. There were many
sections that were old, but have maintained their smoothness level over time. Many of these
sections appear to have carried low traffic volumes relative to the theoretical traffic volume that
can be carried by the pavement section. It was observed that pavements that were of similar age
show a parallel trend in roughness progression, indicating pavements that are built smoother
provide a smoother pavement over its design life.
3.3.2 Roughness Development of AC Pavements
Perera et al. (1998) found a strong relationship between pavement performance and
environmental factors. Each of their GPS sections had been profiled an average of four times.
When roughness progression for test sections in each GPS experiment was plotted for each of the
four environmental zones (i.e., wet-freeze, wet no-freeze, dry-freeze, and dry no-freeze), there
were distinct trends in roughness progression between the regions. The observed roughness
13
development trends in GPS-1 sections seem to indicate that pavement roughness remains
relatively constant over the initial life of the pavement and then after a certain point show a rapid
increase. The IRI plots show several sections that were over 15 years old, but had low IRI values.
An analysis of these sections indicated they have carried a relatively low cumulative traffic
volume when compared to the theoretical cumulative traffic volume the section was capable of
carrying. A preliminary analysis of the sections showing a high increase in roughness over the
monitored period indicated that these sections were close to or exceeded their design life based
on equivalent axle loads.
3.3.3 Roughness Development of PCC Pavements
A comprehensive analysis of IRI trends of GPS-3, GPS-4 and GPS-5 pavements was performed
by Perera et al. (1998). This analysis indicated distinct IRI trends for each of those experiments.
Perera et al. (1998) found that for JPC pavements (i.e., GPS-3) there were distinct differences in
IRI progression between doweled and non-doweled pavements. Generally, the non-doweled
pavements showed higher rates of increase in roughness when compared to doweled pavements.
For both doweled and non-dowelled pavements, higher IRI values were generally indicated for
pavements located in areas that received higher precipitation, had higher freezing indices, and
had a higher content of fines in the subgrade. In the non-freeze regions, pavements located in
areas that had a higher number of days above 32°C had lower IRI values for both doweled and
non-doweled pavements. Pavements that had higher modulus values for PCC had higher IRI
values. These observations indicate that mix design factors and the type of aggregate used may
influence the performance of the pavements from a roughness point of view.
Roughness trends in JPC (i.e., GPS-3) sections have been analyzed by Khazanovich et al. (1998)
through dividing the sections into three groups based on IRI vs. time performance. The three
groups were classified as poor, normal and good. The performance of a pavement section was
classified to be good if the IRI satisfied the following condition:
IRI < 0.631 + 0.0631 * Age
Where, IRI is in m/km, and age is the pavement age in years.
14
The performance of a pavement section was classified to be poor if the IRI satisfied the
following condition:
IRI > 1.263 + 0.0947 * Age
Where, IRI is in m/km, and age is the pavement age in years.
Pavement sections falling between the good and poor cut-off limits were considered to be
performing normally. Of the poor performing sections, approximately 71 percent were located in
wet-freeze region, 24 percent in dry-freeze region, and 6 percent in wet no-freeze region. None
of the poorly performing sections were located in dry no-freeze regions. Higher IRI values were
related to high freeze index values, higher freeze thaw cycles, and higher annual days below 0
°C. They also found that the presence of increased moisture over an extended period of time,
characterized by the average number of wet days per year, caused higher roughness. Pavements
in warmer climates generally had lower IRI values. They also found a strong relationship
between pavement performance and subgrade type. Approximately 67 percent of sections
constructed on fine-grained subgrade had a poor IRI performance, while only 33 percent of
sections on coarse-grained soils had a poor IRI performance. No trend between traffic and IRI
was found. Sections with stabilized bases had lower IRI compared to sections with granular
bases. In the poor performance group, 82 percent of the sections had granular bases while 18
percent of the sections had stabilized bases. Sections with asphalt-stabilized bases had
significantly lower IRI than all other bases. They used linear regression to estimate the initial as-
constructed roughness and to obtain a rate of increase of roughness. They found that poor
performing sections had the highest average rate of increase of roughness, while good
performing sections had the lowest rate. They also found that poor performing sections had
higher backcasted initial roughness when compared to normal and good sections.
Perera et al. (1998) found that for JRCP (i.e., GPS-4) pavements, higher IRI values were
associated with higher precipitation, higher moisture content in subgrade, thicker slabs, longer
joint spacing, lower water cement ratios, and higher modulus values for PCC. Khazanovich et al.
(1998) performed an analysis of JRCP sections using an approach similar to that used in the
analysis of GPS-3 sections. They determined JRCP constructed on coarse-grained soil performs
better than JRCP constructed on fine-grained subgrade. All JRCP rated as poor were constructed
15
on fine-grained subgrade while no JRCP rated as poor was constructed on coarse-grained soil.
They indicated where poor subgrade soil exists; the specification of a thick granular layer will be
beneficial. They did not find any specific trends between IRI and traffic, but observed JRCP in
good IRI performance category carried much higher ESALs than those in the poor or normal
group. Higher IRI values were associated with thicker slabs which indicated thicker slabs were
constructed rougher than thinner slabs.
Pavements in areas having a greater annual precipitation or a higher number of wet days had a
higher IRI. There were no significant differences in IRI between granular and stabilized bases.
They used a linear regression on the time-sequence IRI data to backcast the initial roughness
value and obtain a rate of increase of IRI. This analysis indicated that both the initial IRI and rate
of increase of IRI over time were greater for the JRCP rated as poor when compared to the
normal and good performing category. They found that the mean backcasted initial IRI of JRCP
rated as poor was 2.38 m/km while the sections that were rated as good had a mean backcasted
initial IRI of 1.10 m/km. The sections that were rated as poor had an IRI increase per year that
was twice as high for JRCP rated as good. They also found, on average, sections with higher k-
values had lower IRI values. Perera et al. (1998) analyzed roughness trends of CRCP pavements
and observed that CRCP pavements appear to maintain a relatively constant IRI over the
monitored period. The IRI behavior pattern was observed to be similar for new as well as old
pavements. They report that there were many sections that are over 15 years old, but are still
very smooth (IRI < 1.5 m/km). Lower IRI values were associated with higher percentage of
longitudinal steel and higher water cement ratios for PCC mix, while higher IRI values were
associated with higher values of PCC modulus. In non-freezing areas, higher IRI values were
noted for pavements in areas that had higher number of days above 32°C.
Khazanovich et al. (1998) analyzed roughness trends in CRCP pavements by dividing the LTPP
sections into three groups based on time vs IRI performance. The three groups were classified as
poor, normal and good. They found higher percentage of steel reinforcement resulted in
smoother pavements. They indicated that pavements constructed over coarse-grained subgrade
performed better than those constructed over fine-grained subgrade. Among all poorly
performing sections, 63 percent were located on fine-grained subgrade while 37 percent was
16
located on coarse-grained subgrade. They did not find any trends between IRI and traffic, but
found that sections that were in the good category had higher traffic volumes.
3.3.4 Roughness Characteristics of Overlaid Pavements
The roughness characteristics of SPS-5 projects that deal with the performance of selected
asphalt concrete rehabilitation treatment factors have been investigated by Perera et al. (1998).
The study found that regardless of the roughness before overlay of a section, the roughness after
overlay of the sections for a specific project would fall within a relatively narrow band. They
also analyzed IRI data from the GPS-6B and GPS-7B pavements for which IRI before and after
the overlay was available. The analysis indicated that a relatively thin overlay could reduce the
IRI of a pavement dramatically. For example, a 100 mm thick AC overlay reduced the IRI of a
flexible pavement from 3.15 to 0.63 m/km. Similarly, a 84 mm thick AC overlay reduced the IRI
of a PCC pavement from 2.68 to 0.87 m/km. Sufficient time-sequence IRI data were not
available for the GPS-6B and GPS-7B experiments to see the how the rate of IRI development is
affected by the IRI before the overlay.
3.3.5 Models to Predict Roughness Development
Perera et al. (1998) developed models to predict the development of roughness for GPS
experiments 1 through 4 using an optimization technique. These models predict the initial IRI of
the pavement with the use of subgrade properties and structural properties of the pavement, and
then predict a growth rate as a function of time, traffic, subgrade properties, and pavement
structure. Models to predict roughness that were developed using LTPP data for PCC pavements
were also presented by Titus-Golver (1998, 1999). Paterson (1987) used data from Brazil to
develop models to predict roughness based on traffic, structural parameters of pavement and
distress data. The incremental change in roughness was modeled through three groups of
components dealing with structural, surface distress, and environmental-age-condition factors.
Von Quintus et al. (2001) studied relationships between changes in pavement surface distress in
flexible pavements to incremental changes in IRI using LTPP data.
17
3.3.6 Transverse, Seasonal and Daily Variations of IRI
Several experiments were conducted using an inertial profiler for NCHRP project 10-47 by
Karamihas et al (1999) to investigate the effect of lateral variations of the profiled path on IRI. A
shift in the wheel path of 0.3 m typically caused variations of IRI ranging from 5 to 10 percent.
In this project, IRI values from LTPP seasonal sites were analyzed to study variations in IRI due
to seasonal effects. Also, data from PCC seasonal sites were used to study daily variations in IRI.
The project report also describes the seasonal variations in roughness that was observed at the
LTPP seasonal monitoring sites. When daily variations in IRI at the seasonal monitoring sites
were analyzed, it was noted for slabs that were curled downwards, that the pavement roughness
increased in the afternoon when compared to the morning. The roughness of slabs that are curled
upwards decreased in roughness from morning to afternoon. The magnitude of this change in
roughness observed during the day due to temperature effects was generally less than 0.1 m/km
for most sections.
3.3.7 Relationships Between IRI and Profile Index (PI)
3.3.7.1 The Pennsylvania Transportation Institute (PTI) Method The Pennsylvania Transportation Institute (PTI) conducted a full-scale field-testing program on
behalf of the Federal Highway Administration (FHWA) (Kulakowski and Wambold, 1989) in an
effort to develop calibration procedures for profilographs and evaluate equipment for measuring
the smoothness of new pavement surfaces. 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 3-1-a shows the relationship
between the inertial profiler IRI and the PI5-mm (PI0.2-in) determined manually from the
California-type profilograph. As can be seen, the resulting linear regression equation had a
coefficient of determination (R2) of 0.57. Figure 3-1-b shows the relationship between the
inertial profiler IRI and the computer-generated PI5-mm (PI0.2-in) from the California-type
profilograph. Although the resulting linear regression equation had a similar coefficient of
determination (R2 = 0.58), its slope was considerably flatter. For any given IRI, the data show a
18
wide range of PI5-mm (PI0.2-in). Although both of these relationships were based on
measurements from both concrete and asphalt pavement sections, neither one is considerably
different from regressions based solely on data from the concrete sections.
Figure 3-1: Relationship between the inertial profiler IRI and the PI5-mm (PI0.2-in)
a) Determined manually, b) Computer-generated from the California-type profilograph
19
3.3.7.2 Arizona DOT Initial Smoothness Study In 1992, the Arizona Department of Transportation (AZDOT) initiated a study to determine the
feasibility of including their K.J. Law 690 DNC Profilometer (optical-based inertial profiler) as
one of the principal smoothness measuring devices for measuring initial pavement smoothness
on PCC pavements (Kombe and Kalevela, 1993). At the time, the AZDOT used a Cox
California-type profilograph to test newly constructed PCC pavements for compliance with
construction smoothness standards.
To examine the correlative strength of the Profilometer (IRI) and profilograph (PI) outputs, a
group of twelve 0.16-km (0.1-mi) pavement sections around the Phoenix area were selected for
testing. The smoothness levels of the sections spanned a range that is typical of newly built
concrete pavement—PI5-mm (PI0.2-in) between 0 and 0.24 m/km (15 inches/mile). A total of
three smoothness measurements were made with the Profilometer over each wheelpath of each
selected section, whereas a total of five measurements were made by the profilograph over each
wheelpath of each section. The mean values of each set of three or five measurements were then
used to correlate the IRI and PI5-mm (PI0.2-in) values. Simple linear regression analyses
performed between the left wheelpath, right wheelpath, and both wheelpath sets of values
indicated generally good correlation between the two indexes. The R2 for the both wheelpath
regression line was very high (0.93).
3.3.7.3 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.
20
Results of linear regression analysis showed a strong correlation (R2 = 0.92) between the IRI and
PI5-mm (PI0.2-in) values. The resulting linear regression equation had a higher intercept value
than those obtained in the PTI and AZDOT studies, while the slope of the equation was more in
line with the slopes generated in the PTI study.
3.3.7.4 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 3-2 shows the relationships developed between the profilograph PI5-mm (PI0.2-in) and
the IRI values respectively derived from the laser and ultrasonic sensors. As can be seen, both
correlations were fairly strong (R2 values of 0.88 and 0.67), and the linear regression equations
were somewhat similar in terms of slope. As is often the case, however, the ultrasonic-based
smoothness measurements were consistently higher than the laser-based measurements, due to
the added sensitivity to items such as surface texture, cracking, and temperature. This resulted in
a higher y-intercept for the ultrasonic-based system.
21
Figure 3-2: Relationships between the profilograph PI5-mm (PI0.2-in) and the IRI values.
3.3.7.5 Texas Transportation Institute Smoothness Testing Equipment Comparison Study
As part of a multi-staged effort to transition from a profilograph-based smoothness specification
to a profile-based specification, the Texas Transportation Institute (TTI) was commissioned by
the Texas DOT (TXDOT) in 1996 to evaluate the relationship between IRI and profilograph PI
(Fernando, 2000). The study entailed obtaining longitudinal surface profiles (generated by one of
the Department’s high-speed inertial profiler) from 48 newly AC resurfaced pavement sections
throughout Texas, generating computer-simulated profilograph traces from those profiles using a
field-verified kinematic simulation model, and computing PI5-mm (PI0.2-in) and PI0.0 values
using the Pro-Scan computer software. A total of three simulated runs per wheelpath per section
were performed, from which an average PI value for each section was computed. The resulting
section PI values were then compared with the corresponding section IRI values, which had been
computed by the inertial profiling system at the time the longitudinal surface profiles were
22
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 3-3 are the relationships between the IRI and the simulated PI response
parameters. As can be seen, a much stronger trend was found to exist between IRI and PI0.0 than
between IRI and PI5-mm (PI0.2-in). Again, this is not unexpected since the application of a
blanking band has the natural effect of masking certain components of roughness. In comparison
with the other IRI–PI5-mm (IRI–PI0.2-in) correlations previously presented, the one developed
in this study is quite typical. The linear regression equation includes a slightly higher slope but a
comparable y-intercept value.
Figure 3-3: Relationships between the IRI and the simulated PI response
3.3.7.6 Kansas DOT Lightweight Profilometer Performance Study The major objective of this 1999/2000 study was to compare as-constructed smoothness
measurements of concrete pavements taken by the Kansas DOT’s (KDOT) manual
Californiatype profilograph, four lightweight inertial profilers (Ames Lightweight Inertial
Surface Analyzer [LISA], K.J. Law T6400, ICC Lightweight, and Surface Systems Inc. [SSI]
Lightweight), and two full-sized inertial profilers (KDOT South Dakota-type profiler, K.J. Law
T6600) (Hossain et al., 2000). The simulated PI0.0 values produced by the various lightweight
systems were statistically compared with the California-type profilograph PI0.0 readings to
23
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 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.
3.3.7.7 Illinois DOT Bridge Smoothness Specification Development Study As part of an effort to develop a preliminary bridge smoothness specification for the Illinois
DOT (ILDOT), the University of Illinois coordinated a series of bridge smoothness tests in 1999
using the K.J. Law T6400 lightweight inertial profiler (Rufino et al., 2001). A total of 20 bridges
in the Springfield, Illinois area were chosen and tested, with each bridge measured for IRI and
PI5-mm (PI0.2-in). At least one run per wheelpath of the driving lane was made, and each run
extended from the front approach pavement across the bridge deck to the rear approach
pavement.
A correlation analysis of the IRI and simulated PI5-mm (PI0.2-in) values produced by the
lightweight profiler was performed in the study, which resulted in the graph and linear
24
relationship given in Figure 3-4. Unlike other relationships presented earlier in this chapter, this
relationship covers a larger spectrum of PI values— PI5-mm (PI0.2-in) values largely in the
range of 0.4 to 1.0 m/km (25 to 63 inches/mile)—due to the fact that bridges are often much
rougher than pavements.
Figure 3-4: Correlation analysis of the IRI and simulated PI5-mm (PI0.2-in) values produced by the lightweight profiler.
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 are summarized in Table 3-1 by Kelly et al.
(2002).
Kelly et al. (2002) performed a much broader and more controlled evaluation using over 43,000
LTPP smoothness data points. The data showed generally similar PI–IRI trends as the past study
trends. The data points consisted of IRI and simulated PI values computed from the same
longitudinal profiles measured multiple times for 1,793 LTPP pavement test sections. Detailed
statistical analyses of IRI and simulated PI data indicated a reasonable correlation between IRI
and PI (PI5-mm, PI2.5-mm, and PI0.0) and between PI0.0 and PI (PI5-mm and PI2.5-mm).
However, it was determined that pavement type (i.e., AC, JPC, AC/PCC) and climatic conditions
(i.e., dry-freeze, wet-nonfreeze) are significant factors in the relationship between IRI and PI.
25
The effects of these variables were taken into consideration in the development of PI-to-IRI and
PI-to-PI conversion models. A total of 15 PI-to-IRI models and 18 PI-to-PI models covering all
three PI blanking band sizes (5, 2.5, and 0 mm [0.2, 0.1, and 0 inches]) and all four climatic
zones (dry-freeze, dry-nonfreeze, wet-freeze, and wet-nonfreeze) were developed for Ac
surfaced pavements.
Table 3-1: 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) , Kelly et al. (2002).
26
3.4 FLEXIBLE PAVEMENT MAINTENANCE EFFECTIVENESS
3.4.1 Distress Variability
Distress was viewed as the most critical aspect of the performance analysis of the preventive
maintenance treatments. If carrying or distributing load is the primary function of a pavement,
the secondary function is protecting the underlying layers from the infiltration of water and
erosion. Cracking is the inevitable phenomenon by which this secondary function is undermined.
It is the function of a maintenance treatment to offset the detrimental effects of cracking by
sealing the crack itself, as well as, the pavement surface. This prevents or decreases the
infiltration of water and incompressibles into the cracks and subsequent loss of supporting
material out of the crack. Maintenance treatments also reduce the rate of future cracking by
slowing the pavement aging process. Untended cracks are a major contributor to pavement
deterioration and consume significant amounts of a pavement's performance life.
The distress data evaluated in this portion of study by Morian et al., (1998), were obtained from
the Regional Information Management Systems (RIMS) of the four LTPP regions. This data has
been collected on General Pavement Studies (GPS) sections since 1988 and in fact, the GPS data
were reviewed as a basis for selecting sites for the SPS-3 experiment. It must be noted that there
are several significant sources of differences in the distress data that explain some of the data
variability. Among these are:
• Rater variability. • PASCO versus manual methods. • Weather and time of day effects. • Seasonal effects.
Although distress criteria are clearly defined, subjective evaluation of distress data results in rater
variability. The distress data used in this analysis is particularly subject to this because two
different methods of distress data collection were used: manual and PASCO. The manual
procedures were still under development at the beginning of this project and were not finalized
until 1993. Consequently, the majority of initial distress data was gathered by the automated
procedure.
27
Weather and time of day influence rater variability. Data collection activities varied from
morning to evening on clear and overcast days. These factors influence the rater’s ability to
perceive different types of cracks. Seasonal effects can influence the extent, severity, and number
of cracks that appear in pavement. Depending on the climate, some types of cracks heal
themselves during the heat of summer. Conversely, cracks may increase in width and number
during the winter. No control over which season the distress evaluations were made was
possible, so subsequent ratings at one site may have varied from winter and summer. As a result,
it is possible the data could reflect distress actually present in the field, yet show significantly
different amounts of cracking from one data collection round to another.
3.4.2 Description of LTPP Experiment SPS-3
The SPS-3 experiment was designed to assess the performance of different flexible pavement
maintenance treatments, relative to the performance of untreated control sections. The
experiment design was developed by the Texas Transportation Institute, under SHRP Highway
Operations contracts.
The core SPS-3 experiment consists of a control section and four maintenance treatments, listed
in Table 3-2. Agency supplemental test sections are also present at several SPS-3 sites. These are
additional test sections for study of maintenance treatments of interest to the participating
highway agency.
Table 3-2: SPS-3 Core Experimental Sections.
Test section number Treatment
310 Thin overlay
320 Slurry seal
330 Crack seal
340 Control
350 Chip seal
The thin overlays were nominally 1.5 inches thick. These overlays were placed by the state and
provincial highway agencies using their own asphalt concrete mixes and their own crews. Four
28
contractors placed the slurry seals and chip seals, one in each of the four LTPP regions. The
material specifications were the same for all four regions, but a different source was used for
each region. The material used for crack sealing was the same for all sites in all regions, but the
installation procedures varied. Four different installation crews, one in each region, applied the
crack sealant.
Thus, for the crack seals, the installation crews varied by region. For the slurry seals and chip
seals, both the materials and installation crews varied by region. For the thin overlays, both the
materials and installation crews varied by state or province. SPS-3 experiments were placed at 81
sites in the United States and Canada, in 1990 and 1991. Every SPS-3 site is located adjacent to a
GPS-1 or GPS-2 test section, and is linked to this GPS site in the LTPP database. Thirty of the
81 SPS-3 sites have no control (340) test section; at these sites, the linked GPS site serves as the
control (Hall et al., 2002).
3.4.3 SPS-3 Performance Findings from Previous Studies
4.4.3.1 Damage Modeling Approach Proposed in Original Experiment Design The approach to SPS-3 performance modeling proposed by the developers of the SPS-3
experiment design was development of one or more damage models by Smith et al (1993). Such
models express some aspect of pavement performance (e.g., development of a given type of
distress or other performance measure) in terms of a damage index between 0 and 1.
An S-shaped curve has upper and lower horizontal asymptotes, and is well suited for measures of
performance that can be expressed in this manner (e.g., percent of wheel path area cracked,
portion of allowable serviceability loss that has occurred). The general form of such a model is
the following:
g = exp [ - (ρ / W ) β] (Eqn. 1)
where
g = the damage index
W = accumulated traffic or age
ρ = parameter for the expected traffic or time to failure
29
β = parameter for the shape of the performance trend
This model form was used to develop the original AASHO flexible and rigid pavement
performance models (HRB, 1962) which are still embedded in the design equations in the 1993
AASHTO Guide. In the context of the AASHTO models, the damage index g is the ratio of the
actual serviceability loss (initial serviceability minus actual serviceability) to maximum
allowable serviceability loss (initial serviceability minus failure serviceability, 1.5). In the
AASHTO models, both ρ and β are functions of the applied load (axle type and magnitude) and
the pavement design.
The report on the SPS-3 experiment design proposed the development of a basic damage model
for the performance of the control sections in the SPS-3 experiment as a function of design,
materials, soils, climate, and traffic rate variables. The relative effectiveness of different
maintenance treatments on improving performance could hypothetically then be expressed as
adjustments to the parameters, which define the shape of the S-shaped curve in the basic
performance model, (Smith et al., 1993).
Variations on the basic model form could reflect the following potential effects of a maintenance
treatment:
• Delaying initiation of a distress, • Achieving an immediate improvement in pavement condition by reducing the quantity of
a distress without significantly affecting the rate of occurrence of the distress, and/or • Changing the rate of occurrence of a distress.
Smith et al., 1993 identified structural adequacy as a factor in the SPS-3 experiment design, and
defined it as the ratio of in-place Structural Number to required Structural Number. This factor
does not, however, appear to enter into the originally proposed approach to modeling SPS-3
maintenance effectiveness.
Smith et al., 1993 describes some efforts to apply this analysis approach to early performance
data from the SPS-3 experiment. These efforts were hampered by data availability problems and
the short times in which the treatments had been in service. The researchers estimated that it
would be five to ten years from the time of treatment application before the effects of the
maintenance treatments on pavement performance could be assessed.
30
3.4.3.1 Five-Year Evaluation of SPS-3 Performance by Expert Task Groups Morian et al (1997) reported that four Expert Task Groups (ETGs), one in each LTPP region,
visited and evaluated a total of 57 SPS-3 sites in the summer and fall of 1995
The ETG members used a 0-10 scale (e.g., 0-2 = “very poor,” 8-10 = “very good”) to give
consensus ratings to the overall pavement condition independent of treatment, the overall
condition of the treatments, the overall effectiveness of the treatments, and the appropriateness of
the treatments.
According to Morian et al (1997), the SPS-3 maintenance treatments were judged by the ETGs to
have exhibited somewhat better performance than the control sections in the first five years of
service. This was judged to be more true of the thin overlay and chip seal treatments than the
slurry seal and crack seal treatments.
Zaniewski and Mamlouk (1999) attributed the following conclusions to the 1995 report on the
Expert Task Groups’ site evaluations of SPS-3:
• Sections with preventive maintenance treatments generally outperformed control sections.
• Treatments applied to pavements in good condition have shown good results. • Traffic level and pavement structural adequacy did not appear to affect performance.
3.4.3.2 Regression Modeling of SPS-3 Performance Morian et al (1998) described efforts made to apply regression analyses to SPS-3 performance
data. The data analyzed included distress, deflection, profile, rut depth, and friction data.
Attempts were made to use multiple regressions to develop prediction models for cracking,
rutting, ride quality, friction, and an index called Pavement Rating Score (PRS). They defined
structural adequacy as “the actual structural number of the test section divided by structural
number requirements to carry the section traffic volume. Whether“actual structural number” is
that at the time of construction of the pavement or at the time of application of the maintenance
treatment, and in either case, how it is to be determined, is not clear. How the required structural
number should be determined, i.e., for what design traffic volume and for what subgrade
modulus, drainage, and reliability inputs, is also not clear.
31
Morian et al (1998) concluded that structural adequacy was not found to have a significant effect
on performance of SPS-3 treatments. They also reported that only the thin overlay treatment
achieved a significant immediate reduction in rutting. Analysis of the change in rut depths after
five years of service indicated that crack seal sections and thin overlay sections rutted at about
the same rate as control sections, slurry seal sections at a slightly slower rate, and chip seal
sections at a slightly faster rate. At certain sites in Arizona, chips seals and slurry seals appeared
to have accelerated rutting. This effect was attributed to stripping in the asphalt concrete layer,
due to an increase in moisture content in the pavement structure.
Morian et al (1998) reported also that thin overlays achieved significant initial reductions in IRI,
chip seal and slurry seals achieved slight initial reductions, and crack sealing did not initially
reduce IRI. Analysis of the change in IRI after five years of service indicated that all of the
treatments, including crack sealing, resulted in better smoothness than in the control sections.
However, the effect of crack sealing on long-term IRI trends was judged to be difficult to
accurately assess after five years of service, given that new cracks did not get sealed, and some
sections designated as crack seal treatment sections did not in fact have any cracks.
Deflections measured before treatment, after treatment, and after five years of service were
normalized to a fixed load level for analysis purposes, but apparently not adjusted to account for
temperature variation. As a result, no conclusions could be drawn about the effects of any of the
treatments on either post treatment deflections or deflections after five years of service.
3.4.3.3 Survival Modeling of SPS-3 Performance Eltahan et al (1999) and Daleiden and Eltahan (1999) conducted a survival analysis of SPS-3
sites in the Southern LTPP region. The objectives of the analysis were to obtain estimates of:
• The life expectancy of each treatment (i.e., the median, or fiftieth percentile, survival time),
• The effect of timing of treatment application on life expectancy (i.e., whether the treatment was applied when the pavement was in good, fair, or poor condition), and
32
• The benefit of the treatment, in terms of added years of life expectancy due to the treatment, compared to the life expectancy without treatment (i.e., the life expectancy of the control section).
The survival analyses were conducted using the Kaplan-Meier method, which is a nonparametric
survival analysis technique. That is, it generates the actual failure probability distribution without
attempting to fit the data to any assumed theoretical distribution. Failure probabilities were
calculated as a function of age only, not accumulated traffic. Failure was defined as reaching
poor condition, defined in terms of severities and quantities of cracking, patching, and bleeding.
Eltahan et al (1999) reported that that overall, after six years of service, sections that received
maintenance when in poor condition had a probability of failure of 83 percent, whereas those that
received treatment when in fair or good condition had probabilities of failure of 38 or 37 percent,
respectively. The overall median survival times for thin overlay, slurry seal, and crack seal were
7, 5.5, and 5.1 years, respectively. A median survival time for chip seal could not be determined
because fewer than 50 percent of these sections had failed at the time of the analysis.
Nonetheless, chip seals were concluded to have outperformed thin overlay, slurry seal, and crack
seal treatments with respect to controlling the reappearance of distress.
3.4.3.4 Crack Sealing Field Study In addition to the various SPS-3 performance modelling efforts described before, the findings of
a related SHRP field study deserve mention. Smith, K. L. and Romine (1999) documented an
asphalt pavement crack sealing study conducted under SHRP Project H-106 and the Long-Term
Monitoring (LTM) Pavement Maintenance Materials Test Sites project. The study addressed the
installation and performance monitoring of 31 different crack treatments (combinations of
sealant materials, reservoir configurations, and installation methods such as conventional air
blasting versus hot air blasting) at five sites. The findings from this study are relevant to the
crack sealing treatment used in the SPS-3 experiment; because some of the crack sealant
reservoir configurations studied resemble those used in the SPS-3 crack sealing sections. In the
North Atlantic and North Central regions, a 38-mm-wide by 9.5-mm-deep reservoir was used in
the SPS-3 crack sealing sections.
33
These reservoir dimensions are similar to those of the standard and shallow recessed band-aid
treatments (configurations B and C) evaluated in the H-106/LTM study. The 25-mm by 25-mm
reservoir size used in the Western region SPS-3 sites is similar to the deep and standard
reservoir-and-recess treatments (configurations E and F) evaluated in the H-106/LTM study.
Notable differences in crack treatment performance were noted among the sites surveyed. These
differences were attributed to factors such as climate, traffic, pavement type, crack type, and
crack spacing, all of which influence the magnitudes of crack movements. When used with using
SHRP-specified rubberized asphalt sealants, the standard recessed band-aid configuration (B)
exhibited the longest service life, followed very closely by the shallow recessed band-aid
configuration (C). The simple band-aid configuration (D) exhibited only about half the service
life of these two other treatments.
3.4.4 Effects of Flexible Pavement Maintenance Treatment on Roughness
In an analysis of a long-term effect of a maintenance treatment on IRI by Hall et al. (2002), the
results indicated that of the four maintenance treatments in the core SPS-3 experiment, only the
thin overlays had long-term IRI values significantly different than the corresponding control
sections.
They also tested the significant effects of other factors, i.e., time, traffic, climate, pavement
strength, and pretreatment IRI. The steps in this analysis are the following:
1. For each site, the change in IRI in the control section, from the first post-treatment
measurement to the most recent measurement, is calculated.
2. For each treated section at the same site, the change in IRI in the treated section at the
same site, from the first post-treatment measurement to the most recent measurement, is
calculated.
3. The difference between the change in IRI in the control section and the change in IRI in
each of the treated sections is calculated for each site.
4. For each treatment type, the slope of the difference calculated in step 3 with respect to
each of the factors of interest is analyzed. This may be done with an F test (or
equivalently, with a t test).
34
Although testing for significance of factor effects applies a statistical test to the linear regression
of performance differences with respect to each of the factors, it does not imply a presumption
that those relationships are better described by linear rather than nonlinear regression. In
detection of significant factor effects, the question of interest is not whether a linear versus a
nonlinear relationship exists, but whether any relationship exists.
The analysis of long-term treatment effects described here represents the first two steps in
building models for the effects of maintenance treatments on pavement performance:
• Determining which treatment types significantly affect long-term performance, and • Determining which factors (traffic, climate, pretreatment condition, etc.), if any,
significantly influence how much effect the treatment types have on long-term performance.
The result showed that on average thin overlays had a significant effect in reducing long-term
roughness, as reported earlier, but that no factor effects were found to be significant, with the
exception of a slight correlation with precipitation? These are not contradictory findings. From
them it may be inferred that the effectiveness of thin overlays in reducing the rate of increase in
roughness, relative to the rate in the control sections, was (a) significant, and (b) consistently so
across the ranges of the factors studied. The exception to this is the precipitation factor: at sites
with more precipitation, the rate of increase in roughness in the control sections exceeded the
rate of increase in the thin overlays slightly more than at sites with less precipitation.
Similarly, what does it mean that one significant factor effect was detected for slurry seals and
one for crack seals, even though, as reported earlier, on average neither of these treatments had a
significant effect in reducing long-term roughness? Again, these are not contradictory
statements. From them, it may be inferred that the effectiveness of these two treatment types in
reducing the rate of increase in roughness, relative to the rate in the control sections, was (a)
negligible, and (b) consistently so across the ranges of the factors studied, with the exception of a
slight correlation with precipitation for slurry seals, and pretreatment IRI for crack seals.
35
3.4.5 Effects of Flexible Pavement Maintenance Treatment on Rutting
The long-term effects of maintenance treatments on rutting are assessed by Hall et al. (2002) in
the same manner as described previously for IRI. SPS-3 sites that were excluded from the
analysis of long-term effects on rutting were those at which all or most of the sections were
rehabilitated or taken out of service shortly after treatment application. However, a site was not
judged as unsuitable for use if one or two of the four treatment sections did not have suitable data
available.
About 50 percent of the test sections in the thin overlay and slurry seal treatment groups were
judged to have data suitable for use in analysis of the long-term effects of maintenance on
rutting. About 40 percent of the test sections in the crack seal and chip seal treatment groups had
suitable data. The data used in the long-term rutting analysis cover a range of time from 2.0 to
8.1 years, with an average of 4.5 years.
The long-term effect of maintenance treatment type on rutting was analyzed using multiple
paired difference tests. The long-term rutting in each treated section was compared to the long-
term rutting in the corresponding control section, and the mean differences were tested to
determine whether or not they are significantly different than zero.
3.4.6 Effects of Flexible Pavement Maintenance Treatment on Fatigue Cracking
Hall et al. (2002) assessed the long-term effects of maintenance on fatigue cracking by
comparing the percent of the test section area cracked, as recorded in the most recent distress
survey, with the percent of the corresponding control section cracked, as recorded at the same
time. The rather remarkable result is that fatigue cracking is fairly consistent among almost all of
the thin overlay sections considered in this analysis. The trend line is very close to horizontal,
and the average area cracked is 13 percent.
The effectiveness of the slurry seal treatment, while not as great as that of the thin overlay
treatment, is still evident. At most of the sites, fatigue cracking in the control section exceeds the
fatigue cracking in the slurry seal section. There is a more upward trend in the data, however,
then there was in the thin overlay data. This suggests that the tendency for fatigue cracking to
36
increase with time is somewhat stronger in the slurry seal sections than in the thin overlay
sections.
The slope of the trend line suggests that the effectiveness of the chip seals at retarding the
reflection of fatigue cracking is between that of the thin overlay sections and that of the slurry
seal sections. These data cover a range of time from 1.6 to 8.4 years, with an average of 5.9
years. However, a relatively small proportion of SPS-3 sites – just 25 percent – have data
suitable for a long-term analysis of cracking. Thus, more detailed analysis of the influence of
factor effects seems unwarranted; as any results obtained could not be counted on to reflect most
of the SPS-3 experiment.
37
4. DATA MINING AND ANALYSIS – IDAHO DATA
4.1 INTRODUCTION
This chapter presents analysis of the pavement performance data retrieved from the LTPP
database for sections in the state of Idaho, and for the selected sections from neighboring states.
The overall performance of various pavement types with regard to roughness, rutting, and certain
types of cracking is discussed, as well as the effectiveness of maintenance techniques used to
remedy these pavement problems. All analyses and conclusions are focused on the state of Idaho
and the close surrounding region.
4.2 SELECTED SITES
In chapter 2, a brief discussion was made about the Idaho sites in the LTPP program along with
those in the proximity of Idaho boarders. For the convenience of the analysis in this chapter the
selected sites and the pavement properties in the selected sites are listed in Table 4-1 through
Table 4-5
Table 4-1: Pavement Information for GPS-1 Sites
Pavement Layer Thickness (in) for GPS‐1 Sites Pavement Layers 1001 1005‐1 1005‐2 1007‐1 1007‐2 1009‐1 1009‐2 1010 1020‐1 1020‐2 1021 9032‐1 9032‐2 9034Seal Coat 0.3 0.2 0.6 0.2 0.6 0.2 0.8 0.2 0.2 0.2 0.3 0.2 0.6 0.3 Original Surface (AC) 3.4 3.6 3.6 3.4 3.4 4.8 4.6 5 3.6 3.6 5.6 2.4 2.4 2.9 AC Below Surface (Binder Course) 5.6 5.6 5.7 3.4 3.4 6 Granular Base 9.2 11.6 11.6 19.4 19.4 9.2 9.2 5.4 12.3 12.3 5.3 23.2 23.2 18.8Engineering Fabric Interlayer 0.1 0.1 Granular Subbase 8.2 8.2 Subgrade 48 48 48 51 51 ? ? ? 93 93 30 ? ? ?
Note: Missing data is indicated by “?” entries
38
Table 4-2 Climatic Information for GPS and SPS Sites in Idaho
Table 4-3: Pavement Information for GPS-3, 5, 6A and SPS-3 Sites
Pavement Layer Thickness (in) for GPS‐3, 5, 6A and SPS‐3 Sites
GPS‐3 GPS‐5 GPS‐6A SPS‐3 Pavement Layers 3017 3023 5025‐1 5025‐2 6027‐1 6027‐2 A B C Seal Coat 0.2 0.4 0.3 0.3 0.4 Overlay (AC) ? 1.8 1.8 AC Below Surface (Binder Course) ? 5 Interlayer (AC) ? Original Surface (AC) 3.6 3.6 3.7 5.1 4.9 Original Surface (PC) 10.3 9 8.3 8.3 Granular Base 4.4 11.4 11.4 12.3 5.3 5.4 Treated Base 5.4 4 4 Granular Subbase 11.6 14.3 6.6 6.6 7.8 7.8 8.2 Subgrade ? ? 111 111 60 60 ? ? ?
Note: Missing data is indicated by “?” entries
Experiment Site County
Route Number
Number of Lanes Elevation (ft)
Precipitation (in)
Freezing Index (C‐days)
Climatic Region
1001 Kootenai 95 4 2150 27.3 217 Wet Freeze 1005 Adams 95 2 3232 24.7 399.1 Wet Freeze 1007 Twin Falls 30 2 3771 10.0 326 Dry Freeze 1009 Cassia 84 4 3025 10.3 350.61 Dry Freeze 1010 Jefferson 15 4 4775 11.9 665.21 Dry Freeze 1020 Jerome 93 2 4097 11.0 327.59 Dry Freeze 1021 Jefferson 20 4 4849 13.4 622 Dry Freeze 9032 Kootenai 95 2 2602 28.3 258.65 Wet Freeze
GPS‐1
9034 Bonner 95 2 2119 31.8 315.53 Wet Freeze 3017 Power 86 4 4254 13.4 356.24 Dry Freeze GPS‐3 3023 Payette 84 4 2503 11.6 278.29 Dry Freeze
GPS‐5 5025 Bannock 15 4 4979 15.7 538.28 Dry Freeze GPS‐6A 6027 Bear Lake 30 2 6056 14.9 817.16 Dry Freeze
A Jerome 93 2 4097 11.0 327.59 Dry Freeze B Jefferson 20 4 4849 13.4 622 Dry Freeze
SPS‐3
C Jefferson 15 4 4775 11.9 665.21 Dry Freeze
39
Table 4-4: Specific Location and Climate Information for Non-Idaho Sites
Experiment State Site Route Number
Functional Class
Number of Lanes
Elevation (ft)
Precipitation (in)
Freezing Index (C‐days)
Climatic Region
GPS‐1 WA 1002 12
Rural Arterial 2 1557 18.7 155.7 Dry Freeze
GPS‐3 WA 3013 195
Rural Arterial 4 2356 17.0 292.1 Dry Freeze
GPS‐5 OR 5008 84
Rural Interstate 4 2729 17.2 179.8 Dry Freeze
UT 7082 15
Rural Interstate 4 4527 16.7 411.45 Dry Freeze
WA 6056 195
Rural Arterial 2 2545 19.9 175.3 Dry Freeze
WA 7322 195
Rural Arterial 2 2545 21.1 224.5 Wet Freeze
GPS‐6A
WY 6032 22
Rural Mjr. Collector 2 6156 17.5 894.3 Dry Freeze
Table 4-5: Pavement Information for Non-Idaho Sites
Pavement Layer Thicknesses (in) for Non‐Idaho Sites Pavement Layers OR‐5008 UT‐7082 WA‐1002 WA‐3013 WA‐6056 WA‐7322 WY‐6032
Seal Coat Overlay (AC) 2.5 2.3 2.3 Friction Course 0.3 1.1
Original Surface (AC) 8.1 9.8 4.3 8.2 3.8 4.9 1.9 Original Surface (PC)
AC Below Surface (Binder Course) 2.7 9.8 Interlayer (AC) Granular Base 8 2.2 11.3 9.6 Treated Base 4.4 4.2
Granular Subbase 6 22 36.9 Subgrade ? ? 66 ? ? ? 36
Note: Missing Data is indicated by ?
4.3 METHODOLOGY
Once familiar with the LTPP database, analysis began with the thirteen GPS sites. Data for all
available sites was downloaded for alligator cracking, longitudinal cracking, transverse
cracking and roughness. The data was then extracted and organized by site. For all cracking
data, a sum of the number of cracks per section was used, ignoring the distinction between low,
40
medium or high severity cracks. This was done in an attempt to simplify analysis and also to
eliminate the inconsistency introduced when different data collectors determined the severity
of a specific crack.
For roughness data, an average of the left-wheel and right-wheel path IRI measurements was
used. Each distress parameter was then graphed versus time for each site.
The GPS sites were then grouped according to their corresponding GPS experiment. The
maximum, minimum and average increase rates for each type of distress in a given experiment
group were calculated. These values were analyzed, and thus conclusions based solely on
pavement type were made. Limited analysis was also done on the basis of climate and
temperature by ignoring experiment distinctions and looking at the geographic areas and
functional classes of each roadway.
Similar data for the available SPS sites was also downloaded and organized according to site
and distress type, and eventually grouped by SPS experiment for analysis purposes. Increase
rates were calculated for each site and each distress type both before and after surface
treatment. The changes in increase rates within each SPS experiment were compared to
determine which, if any, surface treatments had a significant impact on improving poor
pavement conditions caused by the various distresses.
4.4 ANALYSIS
The results are organized by experiment type, and include both local and national trends, when
available, for the various distress types. While every attempt was made to complete a thorough
analysis, a lack of local data and national information made it impossible to make consistent
comparisons throughout the report. Very few of the Idaho sites had available data for any of the
cracking types, and all sites providing data were asphalt pavements on granular pavements. In
addition, most of the sites containing cracking data had only one or two data points, making it
inadequate for analysis purposes. Washington, Oregon, Utah, and Wyoming sites also provided
no useful cracking data. Therefore, longitudinal, transverse and fatigue cracking trends could not
be analyzed for GPS sites in Idaho or the surrounding areas. Additionally, national rutting trends
41
were unavailable for GPS-3 and GPS-5 experiments. Consequently, several experiments could
not be compared on a local versus national level for several distresses.
4.4.1 GPS-1: Asphalt Pavements On Granular Bases
The purpose of the GPS-1 experiments is to study the behavior of asphalt pavements on granular
bases with respect to roughness, rutting and fatigue cracking. As described by the LTPP manual,
sections in this experiment include a dense-graded hot mix asphalt concrete (HMAC) surface
layer, with or without other HMAC layers, placed over an untreated granular base. One or more
subbase layers may also be present but are not required. "Full depth" AC pavements are also
included in this study. All GPS-1 distress trends can be found in Appendix B.
4.4.1.1 Analysis of Roughness Data Pavement roughness is quantified using the IRI. Information on gathering roughness data and
computing IRI values can be found in the Data Collection Guide for Long-Term Pavement
Performance Studies (1990).
Roughness Trends in Idaho Of the four experiments in Idaho, asphalt pavements on granular bases showed the most
significant increases in roughness over time, followed by continuous concrete pavements, jointed
concrete pavements, and existing asphalt overlays on asphalt pavements, respectively. The
average increase in roughness with time in the asphalt pavement sites was 0.077 m/km per year,
with increases as high as 0.20 m/km per year in sites 1001 and 1005. The high increases in
roughness in these areas could be attributed to a combination of climate and traffic; both are on
the major arterial US HWY 95 and have a high annual precipitation in comparison to most other
sites (see Error! Reference source not found.), however they also have some of the lower
freezing indices, which is counterintuitive. Figure 4-1 shows the roughness trends for all GPS-1
sites in Idaho.
42
GPS-1 IRI Versus Time
0
0.5
1
1.5
2
2.5
3
Aug-87 May-90 Jan-93 Oct-95 Jul-98 Apr-01 Jan-04 Oct-06
Date
Ave
rage
Rou
ghne
ss (m
/km
)
Site 1001 Site 1005 Site 1007 Site 1009 Site 1010 Site 1020 Site 1021 Site 9032 Site 9034
Figure 4-1: GPS-1 Roughness Trends in Idaho
National Roughness Trends According to FHWA publication FHWA-RD-97-147 (Perera et al, 1998), flexible pavement data
gathered from the LTPP program reveals several trends. In general, pavement roughness
remains relatively constant for the initial years of the pavement life, and after a certain point,
shows a rapid increase in roughness. The average increases in roughness, as gleaned from
figures within the report, are approximately 0.1 m/km per year in dry-freeze regions and 0.05
m/km per year in wet-freeze regions. It is important to note, however, that while the increase
rate for dry-freeze environments is higher than that of wet-freeze environments, the actual
roughness values are higher in the wet-freeze regions. The national trends also show that areas
with high freezing indices or a high number of freeze/thaw cycles had higher roughness values
than those with lower freezing indices.
FHWA publication FHWA-RD-99-193 (Rauhut et al, 1999) presents roughness trends for AC
pavements under both interstate and non-interstate categories. Nearly all sites analyzed under
the non-interstate category showed IRI values less than 2.5 m/km, which is consistent with
roughness values found in Idaho sites. Based on the performance boundaries determined by the
report authors, the majority of sites analyzed feel within the “good” performance range, with IRI
values less than approximately 1.6 m/km. Using the same boundaries, five Idaho GPS-1 sites
43
would fall into the “good” performance category, and all others would be classified as having
“normal” performance.
4.4.1.2 Analysis of Rutting Data Rutting Trends in Idaho Of the four GPS experiments, asphalt pavement on granular bases provided the most useful data
in terms of rutting analysis, as seven of the nine sites could be used. The average increase in rut
depth with time was 0.31 mm per year, with values as high as 0.59 mm per year in section 1001
and as low as 0.00 mm per year in section 1005. The high value for section 1001 can be
explained using the same reasoning as described in the roughness analysis, however the low
value in section 1005 cannot be explained based on this preliminary analysis; climate and traffic
data are similar for both sections 1001 and 1005, and the structural design of the two pavements
is nearly identical as well (see Table 4-1). Figure 4-2 shows the rutting trends for all GPS-1
sites in Idaho.
GPS-1 Rut Depth Versus Time
0
2
4
6
8
10
12
14
Aug-87 May-90 Jan-93 Oct-95 Jul-98 Apr-01 Jan-04 Oct-06
Date
Max
imum
Whe
el P
ath
Rut
Dep
th (m
m)
Site 1001 Site 1005 Site 1007 Site 1009 Site 1010 Site 1020 Site 1021 Site 9032 Site 9034
Figure 4-2: GPS-1 Rutting Trends in Idaho
44
National Rutting Trends FHWA publication FHWA-RD-99-193 (Rauhut et al, 1999) presents rutting trends for AC
pavements under both interstate and non-interstate categories. Based on the performance
boundaries presented in the report, nearly all non-interstate sites analyzed fall into the “good” or
“normal” range, with rut depths less than approximately 15mm, depending on pavement age. All
sites in Idaho easily fall into this range, with two sites performing “good” and the remaining
seven having a “normal” performance.
The same publication also analyzed the rutting rate of AC pavements, with approximately 62%
of GPS-1 pavements having a nominal rutting rate (less than 1mm per year). This rate is lower
than the average rutting rate of 0.31 mm per year that was found in Idaho sites. As stated in the
FHWA report, asphalt concrete pavements that are built in wetter, colder climates tend to have a
higher percentage of pavements with poor rutting performance. This correlation provides an
explanation as to the higher rutting rates seen in Idaho versus the national averages.
4.4.1.3 Analysis of Various Cracking Data Cracking Trends in Idaho As stated previously, a lack of usable data within Idaho sites, as well as those in surrounding
states, made analysis of local fatigue, longitudinal and transverse cracking impossible.
National Cracking Trends Longitudinal Cracking National trends for longitudinal cracking in GPS-1 experiments could not be located. Transverse Cracking As discussed in FHWA publication FHWA-RD-99-193 (Rauhut et al, 1999), 64% of sites in
Dry-Freeze climates performed “good” (crack spacing greater than 20 m) while 100% of sites in
Wet-Freeze climates performed “good”. However, no distinction was made between GPS-1 and
GPS-6 pavements in the presentation of these results. Since all sites in Idaho are in one of these
45
two climatic regions, it can be concluded that the majority of asphalt pavements in Idaho are
performing well with respect to transverse cracking.
Fatigue Cracking As discussed in FHWA publication FHWA-RD-99-193 (Rauhut et al, 1999), 94% of sites in
Dry-Freeze climates performed “good” (crack spacing greater than 20 m) while 97% of sites in
Wet-Freeze climates performed “good. However, no distinction was made between GPS-1 and
GPS-6 pavements in the presentation of these results. Since all sites in Idaho are in one of these
two climatic regions, it can be concluded that the majority of asphalt pavements in Idaho are
performing well with respect to fatigue cracking.
4.4.2 GPS-3: Jointed Concrete Pavements
GPS-3 pavement experiments included jointed, unreinforced Portland cement concrete (PCC)
slabs placed over most types of base layers. One or more subbase layers also may have been
present but were not required. The joints may have had either no load transfer devices or smooth
dowel bars. Sampling design factors for this study are moisture, temperature, subgrade type,
traffic rate, dowels, PCC thickness and base type. All GPS-3 distress trends can be found in
Appendix C.
4.4.2.1 Analysis of Roughness Data Pavement roughness is quantified using the International Roughness Index (IRI). Information on
gathering roughness data and computing IRI values can be found in the Data Collection Guide
for Long-Term Pavement Performance Studies (1990).
Roughness Trends in Idaho Jointed concrete pavements showed nearly constant or slight increases in roughness values in
Idaho. Site 3017 showed an increase of approximately 0.045 m/km per year. Figure 4-3 shows
the roughness trends for GPS-3 sites in Idaho.
46
GPS-3 IRI Versus Time
0
0.5
1
1.5
2
2.5
Aug-87 May-90 Jan-93 Oct-95 Jul-98 Apr-01 Jan-04 Oct-06
Date
Ave
rage
Rou
ghne
ss (m
/km
)
Site 3017 Site 3023
Figure 4-3: GPS-3 Roughness Trends in Idaho
National Roughness Trends FHWA publication FHWA-RD-98-148 (What Makes….., 2000) presents key findings in the
analysis of roughness trends in PCC pavements. Jointed plain concrete pavements (JPCP) had
higher roughness values in areas with high precipitation and high freezing indices, however
roughness values showed little or no increase over time in wet-freeze regions. In jointed
reinforced concrete pavements (JRCP), roughness values increased with precipitation, subgrade
moisture content, slab thickness, joint spacing and PCC modulus vales. The overall roughness
trend for JRCP pavements appears to increase exponentially. The average increase in roughness
in JRCPs in wet freeze areas was 0.05 m/km per year.
4.4.2.2 Analysis of Rutting Data Rutting Trends in Idaho Jointed concrete pavement experiments in Idaho provided inadequate data for analysis, and only
one site in Washington (53-3013) was found to have applicable values. Thus, the average rate of
increase in rut depth with time for jointed concrete pavements was 0.50 mm per year. Figure 3-1
shows rutting trends for GPS-3 sites in Idaho and Washington.
47
GPS-3 Rut Depth Versus Time
0
2
4
6
8
10
12
Aug-87 May-90 Jan-93 Oct-95 Jul-98 Apr-01 Jan-04 Oct-06
Date
Max
imum
Whe
el P
ath
Rut
Dep
th, m
m
Site 3017 Site 3023 WA Site 3013
Figure 4-4: GPS-3 Rutting Trends in Idaho and Washington
National Rutting Trends As described previously, rutting trends were unavailable for GPS-3 sites, and therefore
comparisons between local and national trends could not be made.
4.4.3 GPS-5: Continuous Concrete Pavements
The GPS-5 experiment studies continuously reinforced PCC slabs placed over most types of base
layers. One or more subbase layers may exist but are not required. A seal coat is also
permissible above a granular base layer. All GPS-5 distress trends can be found in Appendix D.
4.4.3.1 Analysis of Roughness Data Pavement roughness is quantified using the IRI and information on gathering roughness data and
computing IRI values can be found in the Data Collection Guide for Long-Term Pavement
Performance Studies (1990) .
48
Roughness Trends in Idaho The single continuous concrete pavement site in Idaho showed nearly constant or slight increases
in roughness values with time. Both before and after the 1996 rehabilitation, roughness values
remained nearly constant with only a slight increase of 0.05 m/km per year prior to maintenance.
Figure 3-1 shows the roughness trend for the single GPS-5 site in Idaho.
GPS-5 IRI Versus Time
0
0.5
1
1.5
2
2.5
3
Aug-87 May-90 Jan-93 Oct-95 Jul-98 Apr-01 Jan-04 Oct-06
Date
Ave
rage
Rou
ghne
ss (m
/km
)
Site 5025
Figure 4-5: GPS-5 Roughness Trends in Idaho
National Roughness Trends FHWA publication FHWA-RD-98-148 (What Makes…., 2000) presents key findings in the
analysis of roughness trends in PCC pavements. Continuous reinforced concrete pavements
(CRCP) maintained relatively constant roughness values over time. In wet-freeze locations,
roughness values showed little to no increase over time. In no-freeze areas, higher roughness
values were associated with a higher number of days above 32 degrees Celsius. In all climatic
regions, higher roughness values in CRCPs were associated with higher PCC elastic modulus
values.
49
4.4.3.2 Analysis of Rutting Data Rutting Trends in Idaho Being that there exists only one continuous concrete pavement experiment site in Idaho, two
additional sites were used in Eastern Oregon. However, the data obtained from these two sites
was deemed inadequate for this analysis, thus the single Idaho site was used. This site showed
nearly constant values for rut depth over time, leading to an average increase rate of zero, and
thus having the best performance in terms of rutting. Figure 4-6shows the rutting trends for the
GPS-5 sites in Idaho and Oregon.
GPS-5 Rut Depth Versus Time
0
2
4
6
8
10
12
Aug-87 May-90 Jan-93 Oct-95 Jul-98 Apr-01 Jan-04 Oct-06
Date
Max
imum
Whe
el P
ath
Rut
Dep
th, m
m
Site 5025 OR Site 5006 OR Site 5008
Figure 4-6: GPS-5 Rutting Trends in Idaho and Oregon
National Rutting Trends As described previously, rutting trends were unavailable for GPS-5 sites, and therefore
comparisons between local and national trends could not be made.
50
4.4.4 GPS-6A: Existing Asphalt Overlays on Asphalt Pavements
The GPS-6 experiments include pavement sections, which were a part of the original LTPP
experimental design for rehabilitated pavements, as well as those that have been added in
response to changes in practice. Pavements included for GPS-6A, Existing AC Overlay of AC,
have a dense-graded HMAC surface layer with or without other HMAC layers placed over a
previously existing AC pavement. The total thickness of HMAC used in the overlay is at least 25
mm (1.0 inch). All GPS-6A distress trends can be found in Appendix E.
4.4.4.1 Analysis of Roughness Data Pavement roughness is quantified using IRI and information on gathering roughness data and
computing IRI values can be found in the Data Collection Guide for Long-Term Pavement
Performance Studies (1990).
Roughness Trends in Idaho The sole GPS-6 site in Idaho showed nearly constant roughness values for all data points, with a
maximum IRI value of 1.62 m/km and a maximum roughness increase rate of 0.20 m/km per
year. Figure 4-7 shows the roughness trend for the single GPS-6A site in Idaho.
National Roughness Trends As presented in FHWA publication FHWA-RD-00-165 (Performance Trends…., 2000), nearly
one quarter of the GPS-6 test sections showed roughness values greater than the nominal level of
roughness selected for this study (less than 1.6 m/km). No correlations between climatic data
and roughness values, or roughness values over time were presented. However, it is noted that
the amount of traffic on an overlay significantly affects the rate of increase of roughness, while
the initial condition of the pavement (prior to rehabilitation) has little to do with the overlay
roughness.
FHWA publication FHWA-RD-99-193 (Rauhut et al, 1999) presents national trends for GPS-6A
sites with respect to roughness. All but two data points fall within the “normal” or “good”
performance range, which, as determined by the report authors, is approximately less than 3.18
51
m/km, depending on pavement age. The sole Idaho GPS-6A site falls within the “good” range of
this study’s boundary selection.
GPS-6 IRI Versus Time
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
Dec-88 May-90 Sep-91 Jan-93 Jun-94 Oct-95 Mar-97
Date
Ave
rage
Rou
ghne
ss (m
/km
)
Site 6027
Figure 4-7: GPS-6A Roughness Trends in Idaho
4.4.4.2 Analysis of Rutting Data Rutting Trends in Idaho The sole existing asphalt overlay on asphalt pavement site in Idaho also provided data that was
not applicable; however one site in Wyoming (56-6032) and two sites in Washington (53-6056,
53-7322) were found to have adequate data. The average rate of increase in rut depth was 0.42
mm per year for these experiments. Figure 4-8 shows the rutting trends for GPS-6A sites in
Idaho, Washington and Wyoming.
52
GPS-6A Rut Depth Versus Time
0
2
4
6
8
10
12
Aug-87 May-90 Jan-93 Oct-95 Jul-98 Apr-01 Jan-04
Date
Max
imum
Whe
el P
ath
Rut
Dep
th, m
m
Site 6027 WA Site 6056 WA Site 7322 WY Site 6029 WY Site 6032
Figure 4-8: GPS-6A Rutting Trends in Idaho, Washington and Wyoming
National Rutting Trends As presented in FHWA publication FHWA-RD-00-165 (Performance Trends …., 2000), one
third of the GPS-6 test sections had rut depths greater than 7 mm. Again, no correlations
between climatic data and rut depth, or rut depth over time were presented. The analysis shows
that thick overlays do no resist rutting any more than thin overlays. It is noted that material
properties and construction techniques are likely to be the most important factors in the severity
of rutting.
FHWA publication FHWA-RD-99-193 (Rauhut et al, 1999) also presents national trends for
GPS-6A sites with respect to rutting. The majority of analyzed data points fall into the “normal”
or “good” performance range, which, as determined by the report authors, is approximately less
than 10 mm, depending on pavement age. The sole Idaho GPS-6A Idaho site, as well as those
sites in surrounding states, also falls within this “normal” or “good” range. Also presented in
this report was the rutting rate of GPS-6A pavements. 49% of GPS-6A pavements showed a
nominal rutting rate (less than 1 mm per year) while 13% showed a moderate rutting rate
53
(between 1 and 2 mm per year). Based on these rutting rate distinctions, the Idaho and
surrounding area GPS-6A sites easily qualify as having nominal rutting rates, as their average
rate of increase in rut depth was 0.42 mm per year.
4.4.4.3 Analysis of Various Cracking Data Cracking Trends in Idaho As stated previously, a lack of usable data within Idaho sites, as well as those in surrounding
states, made analysis of fatigue, longitudinal and transverse cracking impossible.
National Cracking Trends Longitudinal Cracking As presented in FHWA publication FHWA-RD-00-165 (Performance Trends…., 2000),
approximately twenty percent of the GPS-6 test sections contained more than 50 meters of
longitudinal cracking, both in and out of the wheel path. Again, no correlations between climatic
data and longitudinal cracking, or longitudinal cracking over time were presented. Neither the
condition of the pavement prior to rehabilitation nor the age of the overlay has a significant
impact on its performance with respect to longitudinal cracking.
Transverse Cracking As presented in FHWA publication FHWA-RD-00-165 (Performance Trends…, 2000), thirty-
five percent of the GPS-6 test sections have more than eleven transverse cracks. Again, no
correlations between climatic data and transverse cracking, or transverse cracking over time were
presented. However, it is noted that while transverse cracks are believed to be caused by low
temperatures, only moderate to low amounts of transverse cracks were observed in Canadian test
sections, where low temperatures are common. Unlike longitudinal cracking, the original
pavement condition prior to overlay placement does affect the amount of cracking observed in
the overlay. The age of the overlay affected its performance with respect to transverse cracking
in thin overlays (less than 60 mm) but not in thicker overlays. Also, as the thickness of an
overlay increases, so does its resistance to transverse cracking.
54
FHWA publication FHWA-RD-99-193 (Rauhut et al, 1999) presents national transverse
cracking trends for all AC pavements, but does not distinguish between GPS-1 and GPS-6
pavements. See previous sections for a summary of these trends.
Fatigue Cracking As presented in FHWA publication FHWA-RD-00-165 (Performance Trends…., 2000), fifteen
percent of the GPS-6 test sections have more than 11 m2 of fatigue cracking. Again, no
correlations between climatic data and fatigue cracking, or fatigue cracking over time were
presented. However, a correlation between longitudinal cracking in the wheel path and fatigue
rutting was made. It was found that with continued traffic loading, longitudinal cracking in the
wheel path will evolve into fatigue cracking.
FHWA publication FHWA-RD-99-193 (Rauhut et al, 1999) presents national fatigue cracking
trends for all AC pavements, but does not distinguish between GPS-1 and GPS-6 pavements.
See previous sections for a summary of these trends.
4.4.5 SPS-3: Pavement Treatment Performance
SPS-3 compares the effectiveness and mechanisms by which the selected maintenance
treatments preserve and extend pavement service life, safety and ride quality. The overall goal
was not to compare the performance of one treatment to another, but to compare the change in
performance of the treated section to the untreated section. The study factors for flexible
pavements include: climatic zone, subgrade type (fine or coarse), traffic loading (greater or less
than 85,000 ESALs/year), initial condition (good, fair, or poor), and structural adequacy (high or
low). The maintenance treatments applied were slurry seal, chip seal, crack seal and thin overlay.
All SPS distress trends can be found in Appendix F.
Three SPS-3 experiments were conducted in Idaho (SPS3-A, B and C). The results of the
analysis of various treatment performances for each distress type are discussed below. National
performance trends were gleaned from the FHWA publication FHWA-RD-96-208 (Morian et al,
1997).
55
4.4.5.1 Thin Overlay Treatment Treatment Performance in Idaho Roughness The thin overlay treatment performed the best, with regards to roughness, in sections SPS3-A
and B. SPS3-C also showed a marked improvement in roughness after the application of a thin
overlay.
Rutting The application of a thin overlay seemed to have little or no effect on resisting rutting in the three
Idaho SPS-3 experiment sites.
Longitudinal Cracking The only test sections using a thin overlay treatment recorded zero longitudinal cracks prior to
treatment. Therefore, no analysis could be conducted on its performance in treating this distress.
Transverse Cracking The single SPS site providing transverse cracking data for thin overlay treatments showed no
improvement in pavement condition after treatment.
Fatigue Cracking The only test sections using a thin overlay treatment recorded zero fatigue cracking prior to
treatment. Therefore, no analysis could be conducted on its performance in treating this distress.
Treatment Performance Nationally In all regions, the thin overlay treatments performed the best after five years.
4.4.5.2 Slurry Seal Treatment Treatment Performance in Idaho Roughness In section SPS3-C, the slurry seal treatment performed best in improving the roughness of the
pavement. A slight improvement in roughness was noted in SPS3-A and B due to the
application of the slurry seal coat.
56
Rutting The application of a slurry seal coat performed the best at resisting rutting of all treatments in
section SPS3-B. No significant improvement in rutting from this treatment was noted at the
other test sections
Longitudinal Cracking Only one test section using the slurry seal treatment also recorded longitudinal cracking data.
From this data, it was concluded that the slurry seal coat provided an initial improvement by
reducing the number of cracks; however, within a few years, the same number of longitudinal
cracks as was observed prior to treatment reappeared.
Transverse Cracking The single SPS site providing transverse cracking data for slurry seal treatments showed no
improvement in pavement condition after treatment.
Fatigue Cracking The only test sections using a slurry seal treatment recorded zero fatigue cracking prior to
treatment. Therefore, no analysis could be conducted on its performance in treating this distress.
Treatment Performance Nationally The slurry seal treatments were also best in the Southern Region, but performed very poorly in
the North Central Region.
4.4.5.3 Crack Seal Treatment Treatment Performance in Idaho Roughness The application of a crack seal coat had no effect on roughness in any of the three Idaho test
sections.
Rutting The application of a crack seal coat showed a slight improvement in rutting in section SPS3-B.
No significant improvement in rutting from this treatment was noted at the other test sections.
Longitudinal Cracking No test sections using a crack seal treatment provided longitudinal cracking data. Therefore, no
analysis could be made on its performance in treating this distress.
57
Transverse Cracking The single SPS site providing transverse cracking data for crack seal treatments showed no
improvement in pavement condition after treatment.
Fatigue Cracking The only test sections using a crack seal treatment recorded zero fatigue cracking prior to
treatment. Therefore, no analysis could be conducted on its performance in treating this distress.
Treatment Performance Nationally Crack seals performed very well in the North Atlantic and North Central Regions, but were
unsuccessful in the Western and Southern Regions
4.4.5.4 Chip Seal Treatment Treatment Performance in Idaho Roughness The application of a chip seal coat had no effect on roughness in any of the three Idaho test
sections.
Rutting The application of a chip seal coat showed no significant improvement in rutting in any of the
three Idaho test sections.
Longitudinal Cracking The only test sections using a chip seal treatment recorded zero longitudinal cracks prior to
treatment. Therefore, no analysis could be conducted on its performance in treating this distress.
Transverse Cracking Two sections, SPS3-B and C, provided transverse cracking data for chip seal treated pavements.
Both sections showed no improvement in transverse cracks due to this treatment.
Fatigue Cracking No test sections using a chip seal treatment provided fatigue cracking data. Therefore, no
analysis could be made on its performance in treating this distress.
58
Treatment Performance Nationally Chip seals performed fairly well, although they performed best in the Southern Region.
4.5 CONCLUSIONS FORM IDAHO SITES
4.5.1 GPS Sites
Since cracking trends were not considered for GPS sites in Idaho, conclusions as to the most
effective pavement type is based solely on its performance with regards to roughness and rutting.
Continuous concrete pavements performed best in both areas, while jointed concrete pavements,
asphalt pavements on granular bases and existing asphalt overlays on asphalt pavements had
mediocre performances.
4.5.2 SPS Sites
With regards to cracking and rutting, the surface treatments tested were not effective at
improving pavement conditions. To improve pavement roughness, a thin overlay is the best
treatment option, followed by the placement of a slurry seal coat. Chip and crack seal
treatments again have no impact on pavement roughness.
59
5. SEASONAL VARIATION OF SUBGRADE RESILIENT MODULUS – NATIONAL LTPP DATA
5.1 INTRODUCTION
As mentioned earlier, there was only one seasonal site in Idaho, and therefore, data from other
LTPP seasonal sites (form allover the national sites) were used to investigate the variability of
the subgarde resilient modulus with seasonal variation. This investigation was published in a
paper at the TRB annual meting by Salem and Bayomy (2003). The goal of the research was to
develop regression models that can enable design engineers to assess the seasonal changes in the
resilient modulus, and to develop an algorithm for calculating a seasonal adjustment factor that
allows estimating the subgrade modulus at any season from a known reference value at a given
season.
5.2 BACKGORUND ON THE LTPP SMP STUDY
The FHWA-LTPP team launched the Seasonal Monitoring Program (SMP) as an integral part of
the LTPP program. The primary objective of the SMP was to study the impacts of temporal
variations in pavement response and materials properties due to the separate and combined
effects of temperature, moisture and frost/thaw variations. The SMP experiment focused on
collecting data that capture the seasonal variations of the pavement material properties along
with the associated variations in pavement performance. The factorial design of the SMP
experiment included 32 different study factors. Table 5-1 summarizes the original experiment
design of the LTPP-SMP. The original design included 32 design cells, with three sites to be
selected for each flexible pavements cell (cells 1-16) and one site for each rigid pavement
cell(cells 17-32). However, and due to practical implementation of this huge study program, not
all cells were filled with the required number of sites. The data collected by the FHWA-LTPP
program for the SMP study included, in addition to the basic LTPP data designated for the
General Pavement Studies (GPS), data that relate to the seasonal variations of the material
properties and the structural capacity of the exiting pavements. The data types that were
collected under the SMP study are listed in Table 5-1. Most of the LTPP data were released to
60
the public in CD formats via the DataPave software. The latest DataPave software released is
version 3.0, which includes the data release in January 2002.
The approach adopted in this study was to select LTPP-SMP sites that represent various soil
categories and use the backcalculated modulus and gravimetric moisture content data in the
LTPP database to develop regression models for the modulus-moisture relationships for various
soils.
Table 5-1: Experimental Design and Data Elements for the LTPP Seasonal Monitoring Program (Rada et al, 1994)
a) LTPP-SMP Experimental Design
No Freeze Zone Freeze Zone Pavement Type Subgrade
Soil Type Dry Wet Dry Wet
Fine 1 2 3 4 Flexible, Thin AC Surface, <127 mm Coarse 5 6 7 8
Fine 9 10 11 12 Flexible, Thick AC Surface, >127 mm Coarse 13 14 15 16
Fine 17 18 19 20 Rigid –Jointed Plain Concrete, JPC Coarse 21 22 23 24
Fine 25 26 27 28 Rigid Jointed Reinforced Concrete, JRC Coarse 29 30 31 32
b) SMP Data Elements
Element Type of Data Collected
Structural Capacity Deflection data using FWD
Environmental Related Data
Ambient Temperature and rainfall Pavement surface and air temperature Surface layer temperature profile Moisture – depth profile Depth of frost /thaw Depth of ground water table Joint opening and joint faulting (rigid pavements)
Elevation and Profile Data Surface elevation (rod and level) Longitudinal profile (profiler/dipstick)
Distress Data Distresses (Photographic /annual)
61
5.3 MODULUS-MOISTURE RELATIONSHIP FOR SUBGRADE SOILS
The relationship between the modulus of resilience of the soil and its changes with moisture has
been studied for many decades. Most of the published information is based on laboratory or
small-scale field experiments. A brief summary of previous work discussing the effect of
seasonal variations on the soil resilient modulus is presented in the following subsections.
5.3.1 Moisture Effects on Soil Resilient Modulus
Many researchers have investigated the influence of water content on the resilient modulus of
fine-grained soils. Seed et al, (1962) studied the influence of "natural" water content on the
resilient modulus of undisturbed samples of the silty clay subgrade soil used in the AASHO
Road Test. The positions of the test points showed that for this soil a decrease in water content
of only three percent below the optimum resulted in a doubling of the resilient modulus. For
instance, the data showed a modulus increase from about 34 MPa to about 69 MPa upon
decrease in moisture of 3% (Seed et al, 1962).
Tests conducted on silty clay subgrade soil at the San Diego County Experimental Base Project
by Jones and Witczak (1977) showed that as its compaction water content was increased from
about 11 percent to about 20 percent the resilient modulus varied from almost 275 MPa to as low
as about 52 MPa.
Carmichael and Stuart (1985) presented correlations relating the resilient modulus to
fine-grained soil composition parameters. Using a database representing over 250 soils (fine and
coarse) and 3,300 modulus test data points, they developed the following relationship:
Mr = 37.431 - 0.4566 PI - 0.6179 w - 0. 1424F + 0.1791CS - 0.3248 σd + 36.422CH + 17.097
MH (2)
62
Where Mr is the resilient modulus in ksi, PI is plasticity index in percent, w is the water content
in percent, F is percent passing sieve No. 200, CS is the confining stress in psi and σd is deviator
stress in psi. The CH term is a material factor which is equal to one for soils classified as CH and
is equal to zero for soils classified as ML, MH, or CL. MH is a material factor equal to one for
soils classified as MH and equal to zero for soils classified as ML, CL, or CH.
The moisture sensitivity of coarse-grained materials depends on the amount and nature of its fine
fraction. Clean gravels and sands classified as GW, GP, SW, and SP are not likely to exhibit
moisture sensitivity due to the absence of a sufficient number of the small pores necessary to
create significant suction-induced effective stresses even at low water contents (Hicks and
Monismith, 1971). Studies of coarse materials containing larger amounts of fines have shown
that increasing degrees of saturation above 80 to 85 percent can have a pronounced effect on
resilient modulus. Rada and Witczak (1981) concluded that changes in water content of
compacted aggregates and coarse soils could cause modulus decreases of up to 207 MPa.
Several researchers have developed regression relationships between the resilient modulus of
granular materials and water content. The general regression relationship for granular materials
of Carmichael and Stewart (1985), stated previously as Equation 2, contains a water content term
that results in a 0.62 Ksi (4.3 MPa) decrease in resilient modulus for each one percent increase in
water content. Lary and Mahoney (1984) found regression relationships for resilient moduli of
specific northwest aggregate base materials and predominantly coarse subgrade soils. The
regression equations for the materials showed that if the initial modulus is on the order of 140
MPa, a one percent increase in moisture content typically results in a resilient modulus decrease
from about 4 to 11 MPa. A reasonable estimate for the influence of water content on reference
resilient modulus of coarse soils would be about 3.4 MPa decrease for each one percent moisture
content increase for uniform or well-graded coarse materials containing little or non-plastic fines
(GW, GP, SW, SP). That value would increase to about 3.8 MPa per one percent moisture
content increase for sands and gravels containing substantial amounts of plastic fines (GM, GC,
SM, SC).
63
For the development of AASHTO2002, Witczak et al (2000) developed the model shown below
by Eqn. 3 for evaluating the change in modulus of resilience due to change in moisture content.
Log {Mr / Mr-opt} = a + (b-a) / (1+ exp(c + d (S-Sopt))) (3)
Where;
Mr = Resilient modulus at any degree of saturation, S
Mr-opt = Resilient modulus at optimum moisture content
S = Degree of saturation in decimal.
Sopt = S at optimum
a, b, c, d = Model parameters
5.3.2 Temperature Effects on Subgrade Soil Resilient Modulus
Low temperatures, below freezing, may cause significant variation of the soil moduli values. The
penetration of freezing temperatures into moist soils may cause the moisture in the soil voids to
freeze. The soil-moisture system during the freezing condition will show much higher modulus
value. However, during the thaw period, the material loses the apparent increase in its modulus.
This effect is more profound in soft soils, where the material softens further in the thaw season
and it loses its strength significantly below its normal value. Study by Hardcastle (1992) showed
that freezing of soil moisture could transform a soft subgrade into a rigid material. However,
thawing of the same material can produce a softening effect such that for some time after
thawing, the material would have a resilient modulus that is only a fraction of its pre-freezing
value. The effects of an annual cycle of freezing and thawing on the deflections of pavements
having coarse and fine-grained subgrade soils in Illinois and Minnesota were studied by Scrivner
et al. (1969). The study showed that, for all of the pavements, freezing results in sharp reductions
in surface deflections while thawing produces immediate deflection increase. It showed also that
the pavement deflection changes could occur due to freezing of the structural layers alone, while
the largest thaw-induced deflection increases take place when there is deep frost penetration into
the fine-grained subgrade soils. Deflection increases due to deep frost penetration and thawing of
the coarse-grained subgrade soil are smaller than those for fine-grained soils.
64
5.3.3 Subgrade Moisture Prediction Using the Integrated Climatic Model (ICM)
Recent studies are showing that important climatic factors such as temperature, rainfall, wind
speed and solar radiation can be modeled accurately enough for design purposes by using a
combination of deterministic and stochastic analytical methods. These techniques provide the
input into climatic-materials-structural-infiltration-drainage frost-penetration frost-heave and
thaw weakening models that result in meaningful simulations of the behavior of pavement
materials and of subgrade conditions or characteristics over several years of operation. The
Integrated Climatic Model developed under contract to Federal Highway Administration by
Lytton et al. (1989) has been designed to perform these tasks. Larson and Dempsey (1997-2000)
upgraded the model for the ICM version 2.1. It could be applied to either asphalt or Portland
cement concrete pavements. The model is composed of four major components. They are the
Precipitation (PRECIP) Model, the Infiltration and Drainage (ID) Model, the Climatic-Material-
Structural Model (CMS) Model and the CRREL (The U.S. Army Cold Regions Research and
Engineering Laboratory) Model for Frost Heave-Thaw Settlement. For the development of
AASHTO2002, the NCHRP 1-37(a) research team modified the ICM and developed the
enhanced ICM. The new version of the model is called EICM 2.62 (Witzak et al, 2000).
Richter and Witczak (2001) have discussed the application of data collected at 10 LTPP SMP
sites to evaluate the volumetric moisture prediction capabilities of the ICM. The moisture
prediction capabilities of the Integrated Climatic Model (ICM) were evaluated by applying the
model to predict the subsurface moisture contents for the test sections, and then comparing the
results to the data collected at those sites. Several versions of the ICM model were considered in
this work. Six of the sites were modeled with Version 2.1 of the ICM. Poor agreement between
the model output and the monitored moisture data was observed because several of the key
material parameters required by the model are not among the data collected for the test sections
used in the evaluation. Based on their findings, Richter and Witczak (2001) concluded that
Version 2.62 of the ICM could sometimes provide reasonable estimates of the variation in the in-
situ moisture content of unbound pavement materials. The findings for one of the sites suggested
that the model might not work well for sites in arid climates; however, they recommended more
extensive evaluation to draw definitive conclusions in this regard.
65
5.3.4 Seasonal Variation and Seasonal Adjustment Factors
In a study on the LTPP data by Ali and Parker (1996), they found out that the backcalculated
resilient moduli of both subgrade and AC surface could be correlated to the month of the year in
a sinusoidal function with reasonable accuracy.
Several research projects were conducted at the University of Idaho by Hardcastle (1992), Al-
Kandari (1994), Bayomy et al (1977), studying the effect of seasonal variations on pavement
performance). These research projects provided initial values of subgrade soil resilient modulus
for various climatic regions and soil types across the State of Idaho. Based on these studies,
Bayomy et al (1996) developed seasonal adjustment factors (SAF) for subgrade soils in Idaho
and incorporated them in the Idaho overlay design system, WINFLEX 2000 (Bayomy and Abo-
Hashema, 2001).
The Washington State Department of Transportation (WSDOT) uses a mechanistic-empirical
system developed at the University of Washington and implemented in the computer program
EVERPAVE 5.0 (Sivaneswaran et al, 1999). This program uses the SAF as key inputs by users
and it does not compute the SAF.
The Minnesota Department of Transportation (Mn/DOT) uses a mechanistic-empirical (M-E)
flexible pavement thickness design that is implemented in the computer program ROADENT 4.0
by Timm et al (2001). The ROADENT program does not include the SAF to adjust the resilient
modulus from one season to another; therefore the user has to calculate and enter the resilient
modulus values for each season.
The above studies demonstrate that there is need to establish realistic prediction models that
allow the prediction of the subgrade soil modulus at various seasons. The prediction can be in the
form of a direct relationship between the modulus and the moisture at a selected season, or in the
form of a seasonal adjustment factor (SAF) that shifts the modulus value from a known reference
modulus to that of the season in consideration.
66
5.4 LTPP-SMP DATA REQUISITION AND PREPARATION
The first step in the analysis was to isolate all sites in the freeze zones (wet and dry) from the
non-freeze zones since the frost susceptibility of a soil would certainly influence its modulus
change, especially in the transition from the freeze period to the thaw period. It is also
recognized that the frost susceptibility issue is another main factor that may influence soil
behavior in the freeze and thaw period. It was decided that this chapter would concentrate on
moisture variation effects in “non-freeze” zones.
Next, extensive data mining was performed to gather and consolidate available data in all sites in
the no-freeze zones (wet or dry), which have sufficient data that allows the development of the
desired prediction models. The extensive analysis revealed seven LTPP sites that were used in
the analysis. Table 5-2 presents the selected sites, and the subgrade soil properties at these sites.
It is important to note that even though the LTPP site number 24-1634 is located in Maryland,
which is classified geographically as Freeze zone, the climatic data of this site indicated no frost
conditions but the authors included the data obtained from this site in their analysis because it
was the only site that had fine silt subgrade soil.
The downloaded data for each site included the backcalculated elastic moduli for subgrade soil
and asphalt concrete (AC) surfaces, the AC layer temperature, and both volumetric and
gravimetric moisture content of subgrade soil at different time intervals. The Backcalculated
subgrade resilient (elastic) modulus was obtained from the LTPP database table
(MON_DEF_FLX_BAKCAL_SECT). The gravimetric moisture content was obtained from the
table SMP_TDR_AUTO_MOISTURE. These tables are available in the DataPave software. For
the purpose of this study, only the analyses of the change in subgrade soil moduli with seasonal
moisture variation were considered. The moisture content of the subgrade is provided in the
LTPP database as moisture profile along the subgrade depth. The average moisture content along
the depth was considered the corresponding moisture for the backcalculated resilient modulus at
a given location.
67
Table 5-2: Selected LTPP Sites and Subgrade Soil Characterizations
1 2 3 4 5 6 7 LTPP Sites
48-4143 13-1005 48-1122 24-1634 48-1077 35-1112 28-1016
Location Texas (TX)
Georgia (GA)
Texas (TX)
Maryland (MD)
Texas (TX)
New Mexico (NM)
Mississippi (MS)
Surface Type Rigid Flexible Flexible Flexible Flexible Flexible FlexibleMinimum Monthly Avg. Air Temp, Co
9.7 8.7 9.7 1.7 3.6 5.8 5
Soil Type as Identified by the LTPP
Lean Inorganic Clay
Fine Clayey Sand
Coarse Clayey Sand
Fine Silt Fine Sandy Silt
Coarse, poorly graded sand
Coarse, Silty sand
AASHTO Soil Classification A-7-6 A-6 A-2-6 A-4 A-4 A-3 A-2-4
% Passing # 4 - - 99 99 94 100 92 % Passing # 10 - - 97 98 93 99 91 % Passing # 40 - - 75 98 87 94 85 % Passing # 200 90 38.4 6.5 97.9 51.8 2.7 25.7
D60, mm - - 0.3 - 0.1 0.18 0.23 Liquid Limit, % 41 27 26 - - - 18
Plasticity Index, % 23 12 12 NP NP NP 3
Max. Dry Density, gm/cm3
1.730 2.05 1.858 1.746 1.906 1.698 1.906
Optimum Moisture, % 15.0 10.0 8.0 12.0 10.0 12.0 13.0
5.5 DATA ANALYSIS
The selected sites were placed in two groups, sites for plastic soils (LTPP sites 48-4143, 13-
1005, and 48-1122), and non-plastic subgrade soil (LTPP sites 24-1634, 48-1077, 35-1112 and
28-1016). In the following discussions, the sites will be referred to by their serial numbers (1
through 7), shown in Table 3-1. The data were analyzed to investigate the changes over time
68
(time series analysis), and to develop models for modulus prediction for both types of soil
groups. In addition, a generalized model for seasonal adjustment factor was developed.
5.5.1 Moisture and Modulus Variation with Time
Time series plots for the relationship between both gravimetric moisture content and subgrade
backcalculated modulus for the different sites considered in this study are presented in 26.
The data indicate that both moisture content and backcalculated elastic modulus have almost a
sinusoidal function with time. The data also indicate that the backcalculated elastic modulus
could be related to moisture content in an inverse function. It increases when the moisture
decreases, and vice versa. This correlates with the data obtained by Ali and Parker (1996). The
same behavior is observed at all sites except for site 28-1016, where the modulus showed an
increasing function with increasing moisture content. Careful analysis of the data showed that the
subgrade soils at that site had recorded field moisture contents that were below the lab optimum
moisture content. Since the soil is granular (coarse silty sand) and has very low plasticity (PI=3),
it is most likely that the field condition was on the dry side of the optimum, which may lead to an
increase in the modulus with the increase in moisture content until near the optimum. The results
for sites 24-1634 and 13-1005 in Figure 5-1 indicates that the maximum modulus values and
minimum moisture values are measured through the summer season (July and August), while the
minimum modulus values and maximum moisture values are measured through the winter
(January and February).
5.5.2 Model Development for Plastic Soils
Multiple regression analysis techniques were applied to relate the backcalculated elastic modulus
to subgrade moisture content and other soil properties such as Atterberge limits and percentage
passing sieve # 200. Data from the first three LTPP sites (48-4143,13-1005 and 48-1122) were
used in this analysis. The subgrade soils at these three sites are: clay, fine sandy clay and coarse
sandy clay, respectively.
Based on study by Carmichael and Stuart (1985), a model that includes the fine content and the
plasticity index as representative of the soil properties was suggested. After intensive statistical
analysis, the best form of the relationship between the soil resilient modulus and other related
soil parameters was chosen in the form:
69
Figure 5-1: Variation of Modulus and Moisture with Time for Various Soil Types at the Selected
LTPP Sites.
70
E1 = Co + C1 * X1 + C2*X2 + C3 * F + C4* PI (4)
Where;
E1 = Log (E)
E = Backcalculated elastic modulus, MPa
X1 = log (moisture content, %) X2 = 1/ (moisture content, %)
F = Percentage passing sieve # 200, %
PI = Plasticity index, %
Co, C1, C2, C3 and C4 are regression coefficients.
The Statistical Analysis System (SAS Software, 2001) computer program was used to perform
the multiple regression analysis with the model proposed by Equation 4 above. The program’s
output of the regression analysis is shown in Table 5-3. The ANOVA results indicate that the
logarithm of the backcalculated modulus (E1) could be related only to the logarithm of moisture
content (X1), with a function having a coefficient of determination (R2) value of 0.6981.
However, when adding other soil properties like PI and F to the model, a better model having R-
square value of 0.9891 could be achieved. Hence, a regression model in the form of Equation 4
was fitted. The results of the regression analysis for the model are also shown in Table 5-3. The
results of the statistical test that evaluates the significance of each regression coefficient indicate
that the estimated model parameters are significant (p-value is less than 0.05). The final model
for this group of soils, based on 183 data points, is represented by Equation 5 below:
Log (E) = 8.82 – 0.673 * X1 – 2.44 * X2 + 0.0084 * F – 0.11* PI (5)
Error! Reference source not found.Figure 5-2 shows the model application on the data
collected from sites 48-4143,13-1005 and 48-1122, respectively. The three plots in the figure
indicate that the model fits the data very well and that the modulus decreases with increasing soil
moisture even if the field moisture content is less than the optimum moisture content, as in sites
13-1005 and 48-1122, respectively. This would be acceptable since the subgrade soils at both
sites are cohesive soils (sandy clay). Hence, when the moisture content decreases, the soil
becomes harder and its modulus increases, and vice versa. It should be noted that this model
71
could be applied only for plastic soils, as there is a term in the model for PI. For non-plastic soils,
this model will be modified to account for soil properties other than PI, as is discussed below.
Table 5-3: SAS Output for Regression Analyses for Modulus-Moisture Relationship for Plastic Soils The REG Procedure Model: MODEL1 Dependent Variable: E1 R-Square Selection Method Number in Root Model R-Square C(p) BIC MSE Variables in Model 1 0.9767 176.6577 -844.4975 0.07112 PI 1 0.7840 2926.776 -491.2373 0.21651 F 1 0.6981 4151.479 -437.8291 0.25593 x1 1 0.5068 6880.904 -359.4122 0.32712 x2 ------------------------------------------------------------------------------- 2 0.9795 137.8704 -863.9304 0.06683 F PI 2 0.9768 177.2805 -844.1832 0.07120 x2 PI 2 0.9767 178.3979 -843.6575 0.07132 x1 PI 2 0.9022 1241.671 -617.3199 0.14614 x1 x2 ------------------------------------------------------------------------------- 3 0.9884 13.2697 -950.0654 0.05045 x1 F PI 3 0.9871 32.4110 -933.3682 0.05329 x2 F PI 3 0.9781 159.8558 -852.5841 0.06930 x1 x2 PI 3 0.9161 1045.302 -641.4009 0.13579 x1 x2 F ------------------------------------------------------------------------------- 4 0.9891 5.0000 -957.7523 0.04902 x1 x2 F PI
The REG Procedure Model: MODEL1 Dependent Variable: E1 Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr > F Model 4 33.90986 8.47747 3528.46 <.0001 Error 155 0.37240 0.00240 Corrected Total 159 34.28227 Root MSE 0.04902 R-Square 0.9891 Dependent Mean 5.70630 Adj R-Sq 0.9889 Coeff Var 0.85899 Parameter Estimates Parameter Standard Variance Variable DF Estimate Error t Value Pr > |t| Inflation Intercept 1 8.81933 0.31794 27.74 <.0001 0 x1 1 -0.67276 0.12405 -5.42 <.0001 301.27894 x2 1 -2.43912 0.76112 -3.20 0.0016 135.24219 F 1 0.00838 0.00066926 12.52 <.0001 32.88774 PI 1 -0.11065 0.00343 -32.28 <.0001 16.50651
Summary of Regression Coefficients (Model presented by Equation 4):
C0 = 8.81933 C1 = -0.67276 C2 = -2.43912 C3 = 0.00838 C4 = -0.11065
72
Clayey Soil, Site 48-4143100
105
110
115
120
125
130
135
140
20 20.5 21 21.5 22 22.5 23 23.5
Mois ture Content, %
Mod
ulus
, MP
Collected DataModel
Fine Sandy Clay, Site 13-1005
350
370
390
410
430
450
470
7.0 8.0 9.0 10.0 11.0 12.0 13.0 14.0Mois ture Content, %
Mod
ulus
, MP
a
Collected DataModel
290
310
330
350
370
390
410
430
450
3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0
Mois ture Content, %
Mod
ulus
, MP
a
Collec ted DataModel
Coarse Sandy Clay , Site 48-1122
Figure 5-2: Model Development for Non-Plastic Soils
73
As was described previously, the model shown in Equation 5 could not be applied directly for
non- plastic soils (sandy and/or silty soils), since there is a term in the model for PI. However, a
general model in the form of equation 3 can be used after replacing the PI variable with the soil
parameter D60, which is the soil size for 60% passing. This was selected based on the study by
Witczak et. al (2000).
Similar to the above analysis on plastic soils, data from sites (24-1634, 48-1077 and 35-1112)
with non-plastic materials were used to develop a model in the form:
E1 = Co + C1 * X1 + C2*X2 + C3 * F + C4* D60 (6)
Where variables E1, X1, X2 and are defined as in equation 3, and D60 is the soil grain size for 60% passing.
SAS results revealed that the C1 coefficient was insignificant. Then the model was modified to
exclude the term X1 (Log (Moisture content), and the regression was conducted on the model
with three independent parameters only, X2, F and D60. The results of the multi-regression
analysis are presented in Table 5-4. Figure 5-3 shows the predicted outcome versus the data
observations at these three sites, which once again verifies the high degree of correlation as
represented by the developed regression model.
The final model that represents the modulus-moisture relationship for non-plastic soils, based on
135 data points, can thus be written as:
Log (E) = 13.01194 – 0.18922 * X2 –0.07845* F – 38.03227 * D60 (7)
5.5.3 Estimating Seasonal Adjustment Factors
The previous analysis allows for prediction of the absolute value of the soil modulus at given
moisture contents for the investigated soil types. There is a concern that the developed
relationships may be site specific due to the fact that few sites were identified in the LTPP
database. However, the trends of the relationships are likely to be applicable for the soil groups
74
investigated, which may limit the applicability of the developed equations to the soil types
investigated.
Table 5-4: SAS Output for Regression Analyses for Modulus-Moisture Relationship for Non-Plastic Soils The REG Procedure Model: MODEL1 Dependent Variable: E1 R-Square Selection Method Number in Root Model R-Square C(p) BIC MSE Variables in Model 1 0.6846 1224.554 -378.0942 0.19987 F 1 0.6280 1464.690 -358.8774 0.21707 x2 1 0.5969 1596.451 -349.5369 0.22595 D60 ------------------------------------------------------------------------------- 2 0.9677 26.1621 -638.1637 0.06428 F D60 2 0.7371 1004.122 -399.0313 0.18329 x2 F 2 0.7003 1160.145 -383.8334 0.19570 x2 D60 ------------------------------------------------------------------------------- 3 0.9734 4.0000 -657.6296 0.05861 x2 F D60
The REG Procedure Model: MODEL1 Dependent Variable: E1 Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr > F Model 3 14.17838 4.72613 1376.03 <.0001 Error 113 0.38811 0.00343 Corrected Total 116 14.56649 Root MSE 0.05861 R-Square 0.9734 Dependent Mean 5.49464 Adj R-Sq 0.9726 Coeff Var 1.06660 Parameter Estimates Parameter Standard Variance Variable DF Estimate Error t Value Pr > |t| Inflation Intercept 1 13.01194 0.23642 55.04 <.0001 0 x2 1 -0.18922 0.03849 -4.92 <.0001 3.76081 F 1 -0.07845 0.00231 -34.03 <.0001 178.52955 D60 1 -38.03227 1.20141 -31.66 <.0001 155.80905
Summary of Regression Coefficients (Model presented by Equation 6):
C0 = 13.01194 C1 = Zero C2 = -.18922 C3 = -.07845 C4 = -38.03227
75
Silty Soil, Site 28-1634 140
160
180
200
220
240
260
14.0 14.5 15.0 15.5 16.0 16.5 17.0 17.5 18.0 18.5
Moisture Content, %
Mod
ulus
, MPa
Collected DataModel
Fine Sandy Silt, Site 48-1077120
130
140
150
160
170
180
190
12.0 13.0 14.0 15.0 16.0 17.0 18.0 19.0Moisture Content, %
Mod
ulus
, MPa
Collected DataModel
150
200
250
300
350
400
450
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0Moisture Content, %
Mod
ulus
, MPa
Collected DataModel
Sandy Soil, Site 35-1112
Figure 5-3: Modulus-Moisture Relationships for Non-Plastic Soils.
76
In order to predict the change in modulus with moisture on a relative basis, an effort was made to
develop a shift factor that allows transferring the modulus from one season to another. For this
purpose, modulus and moisture data were sorted and analyzed to relate the modulus ratio to the
moisture ratio instead of using the absolute values of the modulus and moisture. The modulus
ratio was defined to be the modulus at a given season to that of a known reference season, and
similarly the moisture ratio is the ratio of the moisture content at the considered season to that of
the same reference season.
Based on several statistical trials of various models, an equation was developed in the form:
SAF = k1 (Wr ) k2 (8)
Where;
SAF = Seasonal Adjustment Factor for a season, which is equal to (ESeason/ Eref)
ESeason = Modulus at a given season Eref = Modulus at the reference season
Wr = Moisture ratio, which is equal to (WSeason/ W ref)
WSeason = Water content at a given season Wref = Water content at the reference season
k1 and k2 = Model parameters in which, k1 depends on reference point, and k2 represents the sensitivity of modulus changes with moisture.
Data for the sites (1 through 5) are listed in Table 5-1, and were used to fit a regression model in
the form of Equation 8. The results of the regression analysis are shown in Table 5-5. Plots of the
data for all soils are shown in Figure 5-1.
Table 5-5: Parameters k1 and k2 for the SAF Model (Equation 7)
Site Soil Type k1 Exponent, k2 R2
1 48-4143 Clay 0.99 -1.07 0.48
2 13-1005 Fine Clayey Sand 0.99 -0.29 0.57
3 48-1122 Coarse Clayey Sand 1.04 -0.35 0.53
4 24-1634 Fine Silt 1.01 -1.32 0.72
5 48-1077 Fine Silty Sand 1.02 -0.35 0.50
77
The variables in the model shown by Equation 8 are dimensionless. Once the user determines the
reference modulus and moisture content, Equation 8 can be used to determine the modulus at any
season by multiplying the reference modulus value by the SAF value of that season. It is to be
noted that the parameter k1 depends on the selected reference point, and the parameter k2
depends on the soil type. In this analysis, the authors used the lowest moisture content as the
reference point, which is generally associated with the highest modulus. Therefore, almost all
SAF values, as shown in Figure 5-4, were below 1, and k1 values were almost equal to 1 for all
soils. Practically, the reference modulus and moisture values are the ones determined at the
construction stage, which would be the values determined at the optimum moisture content. As
such, it is recommended that the user determine two modulus-moisture points in order to
determine the k1 and k2 parameters.
y = 0.99x-1.07
R2 = 0.48
y = 0.99x-0.29
R2 = 0.57
y = 1.04x-0.35
R2 = 0.53
y = 1.01x-1.32
R2 = 0.72
y = 1.02x-0.35
R2 = 0.57
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9
Moisture Ratio
Mod
ulus
SA
F
SC, 13-1005 SC, 48-1122 Silt, 24-1634
SM,48-1077 Clay, 48-4143 Power (Clay, 48-4143)
Power (SC, 13-1005) Power (SC, 48-1122) Power (Silt, 24-1634)
Power (SM,48-1077)
Figure 5-4: Variation of the Seasonal Adjustment Factor with the Moisture Ratio for Different Soil Types.
78
5.6 CONCLUSIONS OF SMP DATA ANALYSIS FOR SUBGRADE SOILS
- Based on the analysis presented, the findings are summarized below:
- Variation of modulus and moisture with time followed an inverse function, where the
modulus decreased with moisture increase. This result was valid for all soils where the
field moisture contents observed were above the optimum. This relationship may change
if the field moisture is below optimum. In this case, an increase in soil moisture may
cause an increase in the modulus value as well.
- A relationship between subgrade modulus (E) and the gravimetric moisture content was
determined for different soil types. A general model relating subgrade modulus to soil
moisture and other soil properties was developed and applied for different soil types.
- A model for calculating the modulus seasonal adjustment factor (SAF) of subgrade soil
was developed. The modulus SAF adjusts the subgrade modulus from one reference
season (usually summer) to another. This allows the determination of subgrade resilient
modulus at any season by multiplying the reference value by the SAF for that season. The
reference value is the modulus value determined by testing during any selected season
(for instance, the summer). The SAF determined here is dependent on the variation in
moisture content from one season to another.
79
6. SEASONAL VARIATION OF THE ASPHALT CONCRETE MODULUS – NATIONAL LTPP DATA
6.1 INTRODUCTION
Similar to the study on subgrade resilient modulus, the seasonal variation of the asphalt concrete
(AC) modulus with the change in pavement temperature was investigated by data from the
national LTPP database. The goal of the research was to develop regression models that can
enable design engineers to assess the seasonal changes in the AC resilient modulus, and to
develop an algorithm for calculating a seasonal adjustment factor that allows for estimating the
AC modulus at any season from a known reference value at a given season. The results of this
investigation have been published in a TRB paper by Salem and Bayomy (2004).
The approach adopted in this study was to select LTPP-SMP sites that represent various climatic
regions from both freezing and nonfreezing zones. Using the backcalculated modulus and
pavement temperature data in the LTPP database, regression models that capture the modulus-
temperature relationship could be developed for the different climatic zones.
6.2 MODULUS-TEMPERATURE RELATIONSHIP FOR AC LAYER
The relationship between the AC modulus and its changes with temperature has been studied for
many decades. Most of the published information was based on laboratory experiments, while
little was done based on field data. A brief summary of previous work discussing the effect of
seasonal variations on the AC modulus is presented in the following subsections.
6.2.1 Seasonal Variations in the AC Layer Elastic Modulus
The elastic modulus of the asphalt concrete (AC) layer is highly affected by pavement
temperature. Newton’s law shown in Equation 9 explains the exact mechanism:
τ = μ (δε / δτ) (9)
where
80
τ = Shearing resistance between the microscopic layers
μ = Viscosity (a function of temperature)
δε / δτ = Rate of shear strain.
As temperature changes, the viscosity of the binder material changes (the higher the temperature,
the lower the viscosity), thus changing the shear resistance of the material. The elastic modulus
of linear elastic material (E) is related to the shear modulus (G) and Poisson's ratio (ν) via the
following equation:
E = 2(1 + ν) G (10)
This mechanism explains why the elastic modulus of asphalt concrete decreases as temperature
increases. However, since pavement temperature is related to ambient air temperature, and the
latter often follows a sinusoidal pattern throughout the year, Ali and Parker (1996) expected that
the elastic modulus of the AC layer follows the temperature cycle. This theory was supported by
observations made on the seasonal sites included in the analysis (e.g., Sites 48SA and 48SF
located at a nonfreezing zone in Texas).
Von Quintus and Simpson (2002) illustrated examples of the monthly variation of computed
elastic moduli for selected LTPP test sections in which the modulus of the asphalt concrete
layers increased for winter months and decreased for summer months.
6.2.2 Relating Temperature Variation to AC Modulus
Rada et. al (1991) developed a comprehensive equation using initial LTPP data back in 1991.
Their equation related the asphalt layer modulus to several mix parameters and to the pavement
temperature.
Based on the data collected at LTPP site (48-1077) located at Texas, Ali and Lopez (1966) found
that the AC elastic modulus could be well correlated (R2= 0.72) to the AC layer temperature with
this model:
E = e 9.372 - 0.0361 T (11)
where
E = AC elastic modulus in MPa.
81
T = Pavement temperature in oC at depth 25 cm from the surface.
They found that the correlations between temperatures at various depths are very high. This
suggests that in constructing a model to predict the value of the AC modulus, only one measure
of temperature should be included in the model. There is no need to include more than one
temperature measure since there exists a large degree of redundancy between temperature
measures. The authors found that the coefficient of determination (R2) reduced to 0.63 and 0.66
when using pavement temperatures at depths of 69 mm and 112 mm from the AC layer surface,
respectively. They found also that when using the asphalt surface temperature the coefficient of
determination was 0.63.
Von Quintus and Simpson (2002) showed illustrated examples of computed elastic moduli for
asphalt concrete surface layer as a function of mid-depth temperature based on LTPP data. Their
results showed that the modulus of the asphalt concrete layer increased with decreasing
temperatures as typically expected, but some reversed results were also observed. They attributed
the inconsistency due to observed stripping in the AC layers and due to the extreme variations in
the underlying support layers.
The Minnesota Department of Transportation (MnDot;
http//mrr.dot.state.mn.us/research/mnpave/mnpave.asp, 2003) addressed the effect of seasonal
variations on Minnesota pavement through dividing the year into fives seasons; early spring, late
spring, summer, fall and winter. They incorporated the seasonal average daily HMA temperature
in their flexible pavement design software (MnPave Beta Version 5.1). In the MnPave software,
the HMA temperature is being predicted from air temperature using the Asphalt Institute model
(1982).
6.2.3 Pavement Temperature Prediction Models
Many statistical models were developed to predict the AC layer temperature from air
temperature. Some of these models are old like the Asphalt Institute (AI) model (1982), and
some of them are recent and require many input parameters like those called BELLS models
(Stubstad et al, 1994; Stubstad et al, 1998; Lukanen et al, 2000). Abo-Hashima and Bayomy
82
(2002) developed a more recent model called IPAT. They compared their model (IPAT) to
BELLS3 model and Asphalt Institute (AI) model. The statistical analysis indicated that the
correlation coefficients for IPAT, BELLS, and AI models were 0.971, 0.985, and 0.96,
respectively. Models for predicting high and low pavement temperatures, based on air
temperature, were also developed and incorporated in the LTPPBIND, a SUPERPAVE binder
selection software (Mohseni and Symons, 1998; Pavement Systems LLC, 1999). For the
development of the 2002 guide, Larson and Dempsey (1997 - 2003) are working on upgrading
the Enhanced Integrated Climatic Model (EICM) for moisture and temperature predictions to be
incorporated in the design guide. The temperature is being predicted through interpolation from
the nearest weather stations. The EICM can predict the temperature profile at various depths
from the surface. However, the most recent beta version of the EICM (Version 3.0) does not
show the capability of modulus prediction.
6.3 LTPP DATA ACQUISITION AND PREPARATION
The analysis here is based on Eleven different LTPP sites that were selected from the national
database. Five from the nonfreezing zones and six from the freezing zones. The sites are
described in Table 6-1.
Table 6-1: Selected LTPP Sites and Their AC Layer Properties.
Climatic
Zone
LTPP Site
State AC Layer
Thickness (mm)
Bulk Specific Gr. of AC Mix
(BSG)
Air Voids in AC Mix
(%)
AC Binder Grade
Binder Specific Gravity
Binder Content
(%) 13-1005 GA 195.6 2.341 4.4 AC-30 1.034 4.68 28-1016 MS 200 2.359 2.67 AC-30 1.03 4.45 48-1077 TX 129.5 2.373 3.05 AC-10 0.985 4.5 48-1122 TX 86.4 2.321 3.20 AC-10 0.99 4.61
Non - Freeze
35-1112 NM 160 2.464 4.4 AC-30 1.015 5.05
9-1803 CT 183 2.444 5.35 AC-20 1.01 4.3 23-1026 ME 163 2.352 3.85 AC-10 1.015 5.1 25-1002 MA 198 2.427 6.80 AC-20 1.026 5.5 33-1001 NH 213 2.386 5.80 AC-20 1.03 4.7 16-1010 ID 272 2.294 5.30 AC-10 1.026 5.2
Freeze
27-6251 MN 188 2.353 5.80 N/A N/A 4.5
83
The AC layer modulus was downloaded for each site at different time intervals. The AC layer
temperature at different depths, the pavement surface temperature and air temperature were also
downloaded from the DataPave software (2002). An intensive effort was made to select
pavement temperature values that were recorded at nearly the same time at which the FWD (the
Falling Weight Deflectometer) test was conducted. The average daily air temperature was also
downloaded for the same day on which the test was conducted as well as the day before. Other
supporting data describing the properties of the AC layer for the sites were downloaded and are
shown in
Table 6-1. These data include: AC layer thickness, bulk specific gravity (BSG) of the asphalt
mix, asphalt binder grade, asphalt binder specific gravity and asphalt binder content.
The AC modulus and mid-depth pavement temperature were downloaded from the DataPave
table (MON_DEFL_FLX_BAKCAL_SECT). Supporting data for modulus and mid-depth
pavement temperatures were downloaded from table (MON_DEFL_FLX_BAKCAL_POINT)
for outside lanes at the locations nearest to the installed temperature sensors. The asphalt
pavement temperatures at different depths (25 mm from the surface, mid-depth and 25 mm from
bottom of AC layer thickness) were downloaded from the table
(MON_DEFL_TEMP_VALUES), where the data were recorded every 30 minutes. An effort
was made to select the temperature readings at approximately the same time of the FWD test.
The exact depths of the thermistor probes were downloaded from table
(MON_DEFL_TEMP_DEPTHS). The pavement surface temperature and air temperature
recorded during FWD testing were downloaded from table (MON_DEFL_LOC_INFO). The
average daily air temperature was downloaded from table (SMP_ATEMP_RAIN_DAY). The
asphalt binder viscosity, penetration and specific gravity were downloaded from table
(INV_PMA_ASPHALT). The bulk specific gravity was downloaded from table TST_AC02, the
maximum specific gravity was downloaded from table (TST_AC03), and finally the content of
the asphalt binder was downloaded from table (TST_AC04). The different properties of the AC
layer for each of the sites are shown in Table 6-1
84
6.3.1 DATA ANALYSIS
This analysis is based on the data that were downloaded for eleven different LTPP sites, shown
in Table 6-1. The selected sites were placed in two groups, sites from nonfreezing zones (LTPP
sites 13-1005, 28-1016, 35-1112, 48-1077and 48-1122), and sites from freezing zones (LTPP
sites 9-1803, 23-1026, 25-1002, 33-1001,16-1010, 27-6251). The data were analyzed to
investigate the changes over time (time series analysis), and to develop models for modulus
prediction for both types of climatic zones. In addition, a generalized model for estimating the
seasonal adjustment factor was developed. The following discussions describe these efforts.
6.3.2 Temperature and Modulus Variation with Time
The backcalculated modulus and mid-depth pavement temperature were analyzed versus time for
three different sites from nonfreezing zones. Figure 6-1 shows the asphalt concrete (AC)
modulus – temperature relationship for these sites. The figure illustrates that both modulus and
temperature follow a sinusoidal function with time. This finding agrees with the conclusion
drawn by Ali and Parker (1996). The figure also illustrates that when the pavement temperature
increases the modulus decreases and vice versa.
6.3.3 AC Layer Temperature at Various Depths Versus Modulus
To determine the value that best represented the overall pavement temperature, a preliminary
analysis was conducted on three different sites. For each site, pavement temperatures at three
different depths from the AC layer surface were considered, along with the asphalt surface
temperature and the air temperature. The three sites included in this analysis are 13-1005, 28-
1016 and 35-1112. Statistical analysis using SAS software (2001) as carried out to relate the
natural logarithm of the backcalculated AC modulus to the different temperatures.
The statistical results of the three sites, based on 149 data points, indicated that the mid-depth
pavement temperature, T2, achieved the highest coefficient of determination (R2= 0.93) and the
least root mean squared errors (root MSE=0.1614). The statistical analysis also showed that the
pavement temperatures at the lower depth (25 mm from the bottom) and shallow depth (25 mm
from the surface) achieved lower R2 values (0.91 and 0.88 respectively), while the pavement
85
surface temperature achieved the lowest coefficient of determination (R2 =0.785), even lower
than air temperature (R2 =0.86).
Site48-1077
0
10
20
30
40
50
Oct-93 Jan-94 Apr-94 Jul-94 Oct-94 Jan-95 Apr-95 Jul-95
Month
Tem
partu
re, C
1.0E+03
4.0E+03
7.0E+03
1.0E+04
1.3E+04
1.6E+04
Mod
ulus
, MP
a
Tem pModulus
Site48-1122
0
10
20
30
40
50
Nov-93 Jan-94 Apr-94 Jul-94 Oct-94 Jan-95 Apr-95 Jul-95
Month
Tem
partu
re, C
1.0E+03
3.0E+03
5.0E+03
7.0E+03
9.0E+03M
odul
us, M
Pa
Tem pModulus
Site 35-1112
0
10
20
30
40
50
Feb-94 May-94 Aug-94 Nov-94 Feb-95 May-95 Aug-95
Month
Tem
partu
re, C
2.0E+03
6.0E+03
1.0E+04
1.4E+04
1.8E+04
Mod
ulus
, MP
a
TempModulusP l (T )
Figure 6-1: Variation of Modulus and Temperature with Time for Three Different LTPP Sites.
86
Based on this finding, the authors decided to use the mid-depth pavement temperature in the
modulus temperature analysis. This assessment disagrees with the results of Ali and Lopez
(1996), as they used the temperature at 25 mm depth (T1). The main reason for this disagreement
maybe because they based their analysis on data from only one site. The authors believe that the
mid-depth pavement (T2) temperature is the best temperature to represent the AC pavement,
rather than T1 or T3, because it represents the AC average temperature value through the layer
depth. However, the authors agree with Ali and Lopez (1996) in that there is no need to include
more than one temperature measure since there exists a large degree of redundancy between
temperature measures. Furthermore, a possible high correlation between various measures of
temperature would render results unreliable if used in the same estimation process, thanks to the
multicollinearity problem. Figure 6-1 shows the relationship between AC modulus and pavement
temperature at various depths for the three sites. The figures indicate that, while the three
pavement temperatures look the same at lower temperature values, using the temperature at the
shallow depth of 25 mm (T1) overestimates the modulus at higher temperature values. However,
the mid-depth is considered the average value and is the best to represent pavement temperature.
6.3.4 AC Modulus Versus Mid-Depth Temperature
6.3.4.1 Data from Nonfreezing Zones The modulus-temperature relationship was plotted for five different sites in nonfreezing zones.
The results from testing the five sites, shown in Figure 6-3, indicate that the AC modulus could
be related to pavement temperature with an exponential function in the form:
E = Ko e K2 Tac (12)
where E = AC elastic modulus
Tac = Asphalt Concrete pavement temperature
Taking the natural logarithm (log) of Equation 12 yields:
log(E)= K1 + K2* Tac (13)
87
where K1 = log (Ko)
Site 13-1005
0.0E+00
3.0E+03
6.0E+03
9.0E+03
1.2E+04
1.5E+04
1.8E+04
2.1E+04
2.4E+04
0 5 10 15 20 25 30 35 40 45 50 55 60
Tempr, C
Mod
ulus
, MP
a
T3T2T1Expon. (T1)Expon. (T2)Expon. (T3)
Site 28-1016
0.0E+00
3.0E+03
6.0E+03
9.0E+03
1.2E+04
1.5E+04
1.8E+04
2.1E+04
2.4E+04
0 5 10 15 20 25 30 35 40 45 50 55 60
Tempr, C
Mod
ulus
, MP
a
T3T2T1Expon. (T1)Expon. (T2)Expon. (T3)
Site 35-1112
0.0E+00
3.0E+03
6.0E+03
9.0E+03
1.2E+04
1.5E+04
1.8E+04
2.1E+04
2.4E+04
0 5 10 15 20 25 30 35 40 45 50 55 60
Pavement Temperature, C
Mod
ulus
, MP
a
T3T2T1Expon. (T1)Expon. (T2)Expon. (T3)
Figure 6-2: Modulus Versus Pavement Temperature at Various Depths.
88
0.0E+00
4.0E+03
8.0E+03
1.2E+04
1.6E+04
2.0E+04
2.4E+04
5 10 15 20 25 30 35 40 45 50
Temperature
Mod
ulus
, MPa
13-1005
28-101648-1077
48-1122
35-1112Expon. (28-1016)
Expon. (35-1112)
Expon. (48-1122)
Expon. (48-1077)Expon. (13-1005)
Figure 6-3: Modulus - Temperature Relationship for Five Sites from Nonfreezing Zones.
The values of the model coefficients Ko, K1 and K2 for the different sites are shown in Table 6-2.
The table indicates that this model has a good coefficient of determination, where R2 ranges from
0.85 to 0.98. The model exponent (K2) ranges from -0.051 to -0.058, while the intercept (K1)
ranges from 9.86 to 10.42. The model fitted to different nonfreezing sites is shown in Figure 6-3.
The figure indicates that the curves for all sites are almost parallel; they have nearly the same
slope but different intercepts. The difference in intercepts could be related to the difference in
AC layer properties such as binder viscosity, binder content, mix specific gravity, aggregate type
and /or degree of compaction during construction.
89
Table 6-2: Estimated Constants of The Exponential Function for The different Sites.
Climatic Zone Site Ko K1 = Ln (ko) K2 R2
13-1005 26740 10.19 -0.053 0.96
28-1016 28471 10.26 -0.051 0.98
48-1077 20090 9.91 -0.052 0.96
48-1122 19163 9.86 -0.053 0.85
35-1112 33525 10.42 -0.058 0.95
Nonfreezing
Average 25598 10.13 -0.053 0.83
9-1803 14852 9.61 -0.038 0.95
23-1026 17337 9.76 -0.059 0.95
25-1002 10322 9.24 -0.051 0.96
33-1001 13104 9.48 -0.037 0.95
16-1010 14888 9.61 -0.047 0.67
27-6251 13960 9.54 -0.042 0.91
Freezing
Average 14077 9.54 -0.048 0.77
Comparing the results of Figure 6-2 to the AC layer properties shown in
Table 6-1, the data show that the site having the higher intercept (site 35-1112) also has the
higher binder grade (AC-30). On the other hand, the site having the lower intercept (site 48-
1122) also has the lower binder grade (AC-10). Therefore, the intercept increases with increasing
binder grade. The effect of binder grade and the other AC layer properties, shown in
Table 6-1, will be discussed later in detail, through statistical analysis using the SAS program.
6.3.4.2 Data from Freezing Zones It is important to note that “freezing zones” are those classified by LTPP. The term refers to
regions where the temperature may fall below zero degrees Celsius. The temperature data
reported in the sites in these zones (refer to Table 6-2) showed temperature ranges well above the
zero degrees (refer to Figure 6-3). The apparent reason is the fact that it is practically impossible
90
to test the pavements at these low temperatures. Therefore, the authors considered the use of
Equation 13 to compare the data of the six different sites from freezing zones. The values of the
model coefficients Ko, K1 and K2 for the six sites are also presented on the lower part of Table
6-2. The table indicates that the model has also good coefficient of determination, where R2
ranges from 0.67 to 0.96. The model exponent (K2) ranges from -0.037 to -0.059 while the
intercept (K1) ranges from 9.24 to 9.76. The model compared to the data of different freezing
sites is shown in Figure 6-4. The figure indicates that the curves for different sites are not as
parallel as the sites of nonfreezing zones. The main reason for this difference maybe related to
the freezing effect of the AC pavement. When the pavement temperature reaches freezing,
higher modulus values are achieved. The modulus variation with temperature below the freezing
point is not the same as its variation above the freezing point. It may behave in a different way
and at a different rate. Since the minimum temperature that was recorded at these sites is about –
3.5 °C, there are not enough data available to show this modulus variation with temperature
when the temperature falls below the freezing point simply because the data are not available.
Thus, it is important to re-iterate that the freezing effect on the modulus is not quantified in this
study, simply because the data are not available or very scarce in the LTPP database.
0.0E+00
4.0E+03
8.0E+03
1.2E+04
1.6E+04
2.0E+04
2.4E+04
-5 0 5 10 15 20 25 30 35
Pavement Temperature, C
Mod
ulus
, MPa
16-10109-180323-102625-100233-100127-6251Expon. (9-1803)Expon. (25-1002)Expon. (27-6251)Expon. (33-1001)Expon. (16-1010)Expon. (23-1026)
Figure 6-4: Modulus – Temperature Relationship for Six Sites from Freezing Zones
91
6.3.5 AC Layer Modulus Prediction Models
Although the previous section showed that the AC modulus has a strong correlation with AC
pavement temperature, the temperature alone could not be used to accurately predict the
modulus. The AC layer properties surely affect the value of the elastic modulus. This section is
devoted to the discussion of the prediction of the AC modulus from the mid-depth pavement
temperature and various layer properties.
6.3.5.1 Nonfreezing Sites As described previously, the AC layer modulus could be related to the asphalt pavement
temperature with an exponential function. It was also mentioned above that the different sites of
nonfreezing zones followed almost the same exponential function but with different intercepts.
The difference in intercepts could be related to the difference in AC layer properties such as
layer thickness, mix specific gravity, mix air voids, asphalt binder content and binder grade.
Therefore, an attempt was made, using the statistical package SAS software, to predict the AC
layer modulus from the mid-depth pavement temperature and the AC layer properties shown in
Table 6-2. The statistical analysis revealed the general model given by Equation 14.
Log (E) = Co + C1 * Tac + C2 * H + C3 * BSG + C4 * AV + C5 * GRD (14)
where
E = AC elastic modulus, MPa
Log (E) = Natural logarithm of E
Tac = AC mid-depth temperature, oC
H = AC layer thickness, mm
BSG = Bulk specific gravity of AC mix
AV = % of air voids in the mix
GRD = Code representing the binder grade; equals to 1 for AC-10, 2 for AC-20 and 3 for AC-30.
Co, C1, C2, C3 , C4 & C5 = Model coefficients equal 7.215, -0.053, 0.001, 1.095 , -0.0495 and 0.146, respectively.
92
After substituting the estimated values of model coefficients, the model takes the form shown in
Equation 15:
Log (E) = 7.215 - 0.053 Tac + 0.001 H + 1.095 BSG - 0.049 AV + 0.146 GRD
(15)
The model given by Equation 15 is based on 386 data points from 5 different sites (LTPP sites
13-1005, 28-1016, 35-1112, 48-1077and 48-1122). The coefficient of determination (R2) for this
model is 0.956 and the value of root MSE is 0.123. The positive sign of the coefficients C2 , C3
and C5 indicates that the modulus increases with increasing both the AC layer thickness, the bulk
specific gravity of AC mix and the binder grade. The negative sign of coefficients C1 and C4
indicates that the modulus decreases with increasing both the pavement temperature and the air
voids in the asphalt mix. The statistical analysis also revealed that adding the binder percentage,
binder penetration and binder specific gravity to the model is not significant, so these are not
included in the model. Figure 6-5-A shows the model when compared to data from 5 nonfreezing
sites. The figure shows that the data points in all sites are almost symmetrical around the equity
line (45° line), which indicates that the model fits the data very well.
6.3.5.2 Freezing Sites Five LTPP sites (9-1803, 23-1026, 25-1002,33-1001, 16-1010) are considered in this analysis;
the sixth site (27-6251) is excluded because there is no information available for the properties of
the asphalt binder used in it, as it appears in Table 6-1. The same regression procedures used
before in the nonfreezing sites are followed in these sites. The regression results indicated that all
the variables included in the model are significant. The predicted model took the general form of
the nonfreezing sites, given by Equation 15, but with different coefficients. The coefficients Co,
C1, C2, C3, C4 and C5 were found to be 5.398, -0.047, 0.007, 1.753, -0.420 and 0.469
respectively. The model was based on 406 data points from 5 different sites with R2 value of
0.897, which is less than that of the nonfreezing zone model, and root MSE of 0.171. The model
could be represented by Equation 16.
Log (E) = 5.398 - 0.047 Tac + 0.007 H + 1.753 BSG – 0.420 AV + 0.469 GRD
(16)
93
B) Model for Freezing Zones
0
5000
10000
15000
20000
25000
30000
35000
0 5000 10000 15000 20000 25000 30000 35000
Measured Modulus, MPa
Pre
dict
ed M
odul
us, M
Pa
Full ModelReducedEquityLinear (Equity)
A) Model for Nonfreezing Zones
0
4000
8000
12000
16000
20000
0 4000 8000 12000 16000 20000Measured Modulus, MPa
Pre
dict
ed M
odul
us, M
Pa
Full ModelEquityLinear (Equity)
Figure 6-5: Comparing The Models to Data from Different Zones.
94
As it appears in the previous equation, the model coefficients Co, C1, C2, C3, C4 and C5 have
also the same signs like the nonfreezing zone model, given by Equation 15, with slight difference
in their numeric values. This agreement between the two models could be considered validation
for both of them. The lower R2 values for the Equation 16, compared to Equation 15, could be
related to the freezing and thawing effect that may cause aging and pavement distress in some of
the sites in the freezing zone. This pavement surface distress could make the AC layer behave
non-homogenously compared to the nonfreezing sites. The data presented in Figure 6-3 and
Figure 6-4 explain this behavior. While the curves are almost parallel for all the nonfreezing sites
(Figure 6-3), they are not for the freezing sites (Figure 6-4) due to the dissimilarity in the
pavement surface condition.
The model was applied to compare data from five different sites of freezing zones; the results are
shown in Figure 6-5-B. The figure indicates that the data are well centered around the equity line
except a few data points (13 out of 406) having higher modulus values, which were reported
during the freezing season. The figure indicates that some of the modulus values reported during
freezing season are much higher than usual, where the modulus values exceeded 30, 000 MPa,
while the highest modulus value reported in the nonfreezing sites is 20,000 MPa. These higher
values maybe related to the freezing effect, which occurs for a limited time period, or to an error
in the backcalculation process. Therefore, ignoring these values will not affect the model
accuracy. The model underestimates the modulus during the freezing season, which is considered
safe and more conservative because of the high variability in measuring the modulus during
freezing season.
6.3.6 Estimating the Seasonal Adjustment Factor
The previous analysis allows for prediction of the absolute value of AC modulus at given
temperature for the included sites. Although many variables are included in the models given by
Equations 15 and 16 that could accurately estimate the elastic AC modulus from pavement
temperature and other layer properties, there maybe some concern that certain other variables
may affect the modulus values. These other variables, which could not be included in the model,
may include the construction method, degree of compaction of AC layer, pavement surface
condition and pavement age. Therefore, another effort was made to make the model applicable
for any site. Instead of using the absolute modulus values that may be site specific, a relative
95
value called shift factor (SF) was used. The SF is defined as the AC modulus for a certain site at
any season divided by the AC modulus during a reference season, summer. The asphalt
pavement temperature was also replaced by the temperature ratio (Tr). This is the ratio of the
temperature at the season for which one needs to calculate the AC modulus SF divided by the
temperature of the selected reference season, summer. SAS software was used to predict the AC
modulus shift factor based on pavement temperature and the previously stated layer properties.
The SAS regression results for non-freezing sites indicated that the modulus shift factor could be
determined only from the temperature ratio (Tr) with R2 value of 0.90. The statistical analysis
also showed that adding the other AC layer properties such as viscosity, thickness, or MSG did
not contribute statistically significantly to the model. Therefore the model takes the form shown
in Equation 17.
SF = C1 eC2 Tr (17)
where
SF =AC modulus at any season divided by AC modulus during summer
= (E season / E summer)
Tr = Temperature ratio = Pavement temperature at any season divided by the summer temperature.
C1 and C2 are model coefficients for nonfreezing zones. Thus C1= 10.44 and C2 = -2.18.
For freezing zones the coefficients C1 and C2 were found to be 4.64 and -4.47, respectively. The
R2 value was found to be 0.69, which is smaller than that of the nonfreezing zones but may be
considered acceptable due to the fact that the data used here are actual field data, which have
been collected under the vast variability in environmental conditions. The model was compared
to data from both nonfreezing and freezing sites; the results are shown in Figure 6-6. The figure
shows higher variability of the data from freezing sites while much less variability with
nonfreezing sites. The two curves of nonfreezing and freezing sites, shown in Figure 6-6, could
be used as upper and lower limits for estimating the seasonal adjustment factor. The figure
indicates that if the temperature ratio reduces from 1.0 (during summer) to 0.1 (during winter),
the modulus value would increase to more than 8 times of its summer value for nonfreezing sites
and about 4 times its summer value for freezing sites.
96
y = 4.64e-1.47x
R2 = 0.69
y = 10.44e-2.18x
R2 = 0.90
1
2
3
4
5
6
7
8
9
-0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1
Temperature Ratio (T/ Ts)
Mod
ulus
SF
(E/E
s)FreezNonFreezExpon. (Freez)Expon. (NonFreez)
Figure 6-6: Estimated AC Layer Modulus Shift Factor for Both Nonfreezing and Freezing Zones.
The model shown in Equation 17 is simple, dimensionless and does not need many input
parameters. Once the user determines the reference modulus and temperature, Equation 17 can
be used to determine the modulus at any season by multiplying the reference modulus value by
the SAF value of that season. In this analysis, the authors used the summer temperature as the
reference point, which is the construction season and is generally associated with the lowest AC
modulus.
6.4 CONCLUSIONS OF THE SMP DATA ANALYSIS FOR ASPAHLT MODULUS
Based on the analysis presented, the following conclusions are drawn:
‐ The variation of AC modulus and pavement temperature with time followed an inverse
function, where the modulus decreases with temperature increase. This result was valid for
all sites from freezing and nonfreezing zones.
‐ The mid-depth pavement temperature was found to be the best temperature to represent AC
layer rather than the temperature at 25 mm depth and/or the pavement surface temperature.
97
‐ A relationship between AC modulus and pavement temperature was determined for different
sites in both freezing and nonfreezing zones. Models relating AC modulus to mid-depth
pavement temperature and other AC layer properties were developed and applied for both
freezing and nonfreezing zones.
‐ A model for calculating the modulus seasonal adjustment factor (SAF) of the AC layer was
developed. The SAF adjusts the AC layer modulus from one reference season to another. The
study also showed that the AC modulus could increase in winter to more than 8 times its
summer value if the temperature ratio reduced from 1.0 to 0.1
98
7. APPLICABILITY OF THE IDAHO LTPP DATA FOR THE IMPLEMENTATION OF MEPDG
7.1 INTRODUCTION
This chapter addresses the efforts that were spent in this project to use the Idaho LTPP data with
the new Mechanistic-Empirical Pavement Design Guide (MEPDG) software that was released by
the NCHRP as project 1-37A. It is not intended to look into the development of an
implementation plan of the MEPDG, especially that at the time of the initiation of this project,
the release of the software was very remote. Thus, a background on the design guide and the
efforts by FHWA and lead states to implement the new guide will be briefly discussed. The
efforts of using the beta version software and the development of input forms will be presented,
and finally, the extent of the data availability in Idaho LTPP database will be addressed.
7.2 BACKGORUND ON THE MEPDG
The new MEPDG addresses many issues that were considered shortcomings in the 1993
AASHTO design guide. With a fundamental drawback where the guide was developed based on
empirical data collected form the AASHO Road Test. The new guide combines the pavement
theories with real performance data that have been collected from many real pavement
conditions under actual prevailing traffic and environment including the LTPP experiments and
many other pavement experiments such as the MnRoad.
Although the MEPDG does have immediate benefits, such as allowing for the design of thinner
pavement sections that still provide the required support, the major benefits of the new guide are
long term. The benefits of the MEPDG that will become more apparent over time include; more
appropriate designs, better performance predictions, improved materials research and the ability
to determine the factors that cause pavement failure.
In order to prepare for implementing the new design guide it will be crucial to improve the traffic
and climatic data collected in the state. The MEPDG used “traffic spectra” to classify the
loading conditions that are being designed for. The classification is based on axle type and the
99
distribution of axle weights. Traffic is also evaluated based on daily, weekly, and seasonal
volumes. To accurately represent the traffic in this manner it will be necessary to increase the
amount of traffic data collected.
As for the environmental considerations in the program, it is necessary to have as much
information as available, so as to provide a complete range of the possible climatic conditions in
the area. Ideally at least 20 years of continuous climatic data would be used for the design guide.
The sooner an agency begins compiling data for the various regions within in the state the sooner
this information can be available for use in pavement design. Idaho has a wide range of climatic
zones so it will be necessary to collect data in as many different areas of the state as possible to
insure all locations are sufficiently represented in the data. A possible starting point would be to
gather data for the climatic regions already used in the ITD pavement design guide and then
expand the data from there.
The MEPDG, as released, is calibrated on a nationwide level, which is supposed to accurately
represent the most common conditions throughout the country. Although the guide can be used
effectively under this calibration, increased reliability and accuracy of design can be achieved by
further calibrating the guide to a statewide or even project specific level.
7.3 MEPDG INPUTS AND AVAILABILITY IN THE LTPP DATABASE FOR IDAHO
The software version 0.9 was used to understand the various design inputs that are required by
the guide. An extensive table (almost 14 page long) was developed to enable the user identify
these inputs for a design section. The table is presented in Appendix G. However, many of these
inputs can be the default values in the design software and this would cut the time and effort for
searching for data. An example of data input that was developed using the site 16-1001 is shown
in Table 7-1
Two major obstacles were encountered in the effort to implement the guide during this project.
One was the impracticality of the use of the software where some runs took almost two hours
and the computer hanged up. Based on communication with the MEPDG research team, they
100
indicated that is why it is beta version and they were working on fixing many of such
programming issues. Second obstacle was the fact that Idaho LTPP sites did not have any
multiple sections that could be used for the calibration of the models in the software? In addition,
most of the performance data were limited and discrete as have been presented earlier in chapter
4 of this report.
7.4 RECOMMENDATION FOR IMPLEMENTATION AT THE STATE LEVEL
The MEPDG represents a drastic change in the way pavements are designed and evaluated and
its implementation will require a significant amount of resources to be successful. Also, unless
agencies step up and take the challenge of trying to be on the leading edge of pavement design
technology the new guide will take a long time to realize the potential of the MEPDG. There are
now nineteen state agencies including Washington, Montana and Utah that have already
developed implementation plans and are making steps towards putting the guide into use. This
provides a great opportunity for Idaho not only to build on their local experience but to partner
with them in its effort to implement the new guide.
Along with all of the software considerations that must be taken into account before
implementation, training will also be required for ITD staff. Although it is possible to sit down
at the computer with the software and learn the basics without any formal instruction, it would
not be possible to achieve a high level of proficiency with the technology. The FHWA supports
a number of training activities for state personnel, including workshops and technology enhanced
training. The FHWA training sessions are free and would be an excellent way for staff to become
familiar with the available methods and technology provided in the MEPDG. An additional
benefit of these training sessions would be to facilitate the sharing of information between state
agencies, which could aid in other areas of implementation.
Since immediate implementation of the MEPDG is not possible it is recommended that the state
begin looking at developing a general implementation plan, with a general implementation
scheduled possibly for three years in the future and full implementation to follow two years later.
101
Table 7-1 Example of Inputs for the MEPDG Using Data from Idaho Site 16-1001
Description Value Unit / Format
Project Information:
General Information:
Design Life 20 – 100 years
Base/Subgrade construction 8/1973 month / year
Pavement Construction 8/1973 month / year
Traffic open 8/1973 month / year
Type of Design New Flexible Asphalt Pavement
Site/Location Identification:
Location Kootenai County, Rt 95
Project ID
Section ID 16-1001
Date 5/8/2003
Station/milepost format MP
Station/milepost begins 432
Station/milepost ends 435.86
Traffic Direction N
Analysis Parameters:
Initial IRI 86.8 in/mi
Terminal IRI in/mi (limit / reliability)
AC Surface Down Cracking / long. Cracking 256.3 ft/mi (limit / reliability).
102
AC Bottom Up Cracking / Alligator Cracking 0.5 % % (limit / reliability).
AC Thermal Fracture ft/mi (limit / reliability).
Chemically Stabilized layer / Fatigue Fracture % (limit / reliability).
Permanent Deformation – Total Pavement in (limit / reliability).
Permanent Deformation – AC only in (limit / reliability).
Traffic:
AADTT 14589
Percent of Heavy Vehicles (class 4 or higher) 12.5
Number of Lanes in Design Direction 2
Percent of Trucks in Design Direction 12.5
Percent of Trucks in Design Lane 6.3
Operational Speed 60 mph
Growth
Mean Wheel Location from Lane Marking in
Traffic Wander Standard Deviation in
Design Lane Width ft
Average Axle Width ft
Dual Tire Spacing in
Single Tire Pressure psi
Dual Tire Pressure psi
Tandem Axle Spacing in
Tridem Axle Spacing in
103
Quad Axle Spacing in
Climate:
Latitude 47 deg 46 min degree.minutes
Longitude 116 deg 47 min degree.minutes
Elevation 2150 ft
Annual or Seasonal Depth of Ground Water form Surface
ft
Structure:
Drainage:
Surface Shortwave Absorptivity
Infiltration
Drainage Path Length ft
Pavement Cross Slope %
Layers:
HMA:
Level of Importance 1 (High) to 3 (Low)
HMA Layer Thickness 3.6 in
HMA Mix Properties:
Agg. Cumulative % Retained 3/4 in Sieve 1 %
Agg. Cumulative % Retained 3/8 in Sieve 18 %
Agg. Cumulative % Retained #4 Sieve 44 %
Agg. % Passing #200 Sieve 7 %
Asphalt Binder Grade
104
Asphalt Reference Temperature °F
Effective Binder Content %
Air Voids %
Total Unit Weight pcf
Poisson’s ratio
Asphalt Thermal Conductivity BTU/hr.ft.F°
Asphalt Heat Capacity BTU/lb.F°
Base Type Crushed stone/gravel AASHTO Classification
Base Thickness 9.2 in
Base Elastic Modulus
Base Poisson’s ratio
Base Coefficient of Lateral Pressure, Ko
Subbase Type N/A AASHTO Classification
Subbase Thickness N/A
Subbase Elastic Modulus N/A
Subbase Poisson’s ratio N/A
Subbase Coefficient of Lateral Pressure, Ko
N/A
Subgrade Type A-1-b AASHTO Classification
Subgrade Thickness 48 in
Subgrade Elastic Modulus
Subgrade Poisson’s ratio
Subgrade Coefficient of Lateral Pressure, Ko
105
Thermal Cracking:
Tensile strength at 14 °F
Creep test duration
Creep Compliance (-4, 14, 32 °F) at Different Loading Time
Mixture VMA %
Aggregate coefficient of thermal contraction 1 (High) to 3 (Low)
Distress Potential:
Block Cracking % of Total Design Lane (H/M/L/User Define)
% and Standard Deviation
Sealed Longitudinal Crack Outside Wheel Path (H/M/L/User Define)
ft/mile
106
8. CONCLUSIONS The analysis of LTPP data in this project was focused on the LTPP sites in Idaho and few other
sites in the proximity of Idaho borders. Most of the performance data was time series showing
variation along the pavement life. However, there is no structural evaluation except for one site
at the Idaho Falls (16-1010) which is considered in the SMP program. The data in this site was
not sufficient to develop models that relate the seasonal variability of the pavement properties.
Therefore, several sites from the national database were selected to enable the development of
models that relate the change in layer moduli values with the variation of moisture in Subgrade
or the temperature of the asphalt pavements. The conclusions of these analyses were presented
in the respective chapters. They are summarized here in the context of the project objectives and
scope.
Idaho LTPP Mini Database
LTPP sites in Idaho have been identified and presented in Chapter 2. All data for Idaho sites is
accumulated in one database file which includes all data tables. The file is in MS-Access
Database (MDB) format. It is provided in the CD attached to this report. Traffic data for all
Idaho sites was gathered in a separate MDB file that is also included in the CD. The database
also includes data on few sites from the neighboring states and series of Excel sheets that have
all the developed performance trends.
Review of LTPP data analysis Reports
Published reports by FHWA and NCHRP have been reviewed and summarized in Chapter 3 of
this report. A bibliography list including abstracts of these reports is provided in Appendix A.
Performance Trends at Idaho LTPP Sites
The analysis of performance data in Idaho LTPP sites (presented in Chapter 4) addressed
roughness as represented by IRI values and distresses, which included rutting and cracking.
However, cracking data were very limited with the exception of the SMP site 16-1010 at Idaho
Falls. Therefore, there was no basis for comparison and the analysis of cracking data was
dropped. Hence, the performance was based solely on two indicators, Roughness and Rutting. It
107
was also noted that the LTPP database included rutting data for concrete pavements, which is not
common and not expected. The rutting data reported was based on the analysis of the transversal
profile of the pavement cross section to see whether there is variability on the cross profile.
Therefore, it shouldn’t be interpreted as rut depth trend as commonly done for asphalt
pavements.
For GPS Sites: the various types of pavements exited showed wide variability. Continuous
concrete pavements performed best followed by jointed concrete pavements, especially with
respect to roughness. Asphalt pavements on granular bases and existing asphalt overlays on
asphalt pavements had mediocre performances. No specific trends were captured based on the
data available at these sites in the LTPP database.
For SPS Sites, performance trends showed that the surface treatments tested were not effective at
improving pavement conditions. To improve pavement roughness, data showed that a thin
overlay is the best treatment option, followed by the placement of a slurry seal coat. Chip and
crack seal treatments again have no impact on pavement roughness.
Variability of the Subgrade Resilient Modulus with Moisture:
Based on the analysis presented using data from national sites, results showed that the variation
of modulus with moisture over the time followed an inverse function, where the modulus
decreased with moisture increase. This result was valid for all soils where the field moisture
contents observed were above the optimum. This could change if the field moisture is below
optimum. In such case, an increase in soil moisture may cause an increase in the modulus value
as well. The data was used to develop a modulus-moisture relationship, and an equation for a
seasonal shift factor was developed. These are presented in Chapter 5.
Variability of the Asphalt Modulus with temperature:
Moduli data from national sites were used to develop modulus-temperature relationship that
takes into consideration the effect of the asphalt mix properties such as the asphalt viscosity, air
voids, and asphalt content. A model was developed and presented in Chapter 6. The model
considers the climatic region in the country (e.g. dry free vs. dry no-freeze or wet freeze vs. wet
no-freeze). The model was used to develop an algorithm to calculate a seasonal adjustment factor
that can be used in adjuting moduli data for the purpose of pavement design and evaluation.
108
Applicability of the LTPP data in Idaho Sites for the M-E Design Guide
It was extremely difficult to reach concrete conclusions in this task of the project for two main
reasons. First, the M-E design guide software, until the completion of this project, was a beta
version that did not work smoothly and we had many glitches in every run. Many data had to be
assumed to allow the program to run, and hence would not be applicable to any real condition at
any of the sites. Second, the LTPP database did not include sufficient data in the Idaho sites that
can enable the use of M-E software, with one exception for one site at Idaho Falls where moduli
values and climatic related data were available in the database. The data in this site could be used
to calibrate the roughness model in the M-E Design Guide software.
It is our view that this task is complicated enough to require a whole new project on its own,
especially that the first version of the design guide software has been just released even though it
is still not yet approved by the AASHTO subcommittee. The fact that there are currently huge
efforts at the national level with highly funded projects via the NCHRP and pooled fund state
programs that are looking into this goal will provide Idaho with an opportunity to benefit from
these national efforts and to use their results to plan a specific study that focuses on Idaho
conditions.
109
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Al-Kandari, F. Mechanistic Based Overlay Design Procedure for Idaho Flexible Pavements.
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Pavements. Proceedings of the 8th International Conference on Asphalt Pavements,
Seattle, Washington, August 1977.
Bayomy, F., F. Al-Kandari, and R. Smith. Mechanistic-Based Overlay Design System for Idaho.
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D.C., 1996, pp. 10-19.
Bayomy, F. and M. Abo-Hashema. WINFLEX 2000: Mechanistic-Emperical Overlay Design
System for Flexible Pavements - Program Documentation. Final report of UI project
110
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Technology, NIATT, University of Idaho, Report 01-13, June 2001.
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Seasonal Variation of Subgrade Resilient Modulus on Overlay Mechanistic Design”,
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Treatments of Flexible Pavements,” Transportation Research Record 1680.
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Hall, K., Correa, C and Simpson, A (2002).” LTPP Data Analysis: Effectiveness of Maintenance
and Rehabilitation Options” NCHRP Web Document 47 (Project 20-50[3/4]):
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111
Hardcastle, J.H. Subgrade Resilient Modulus for Idaho Pavements. Final Report of ITD Research
Project RP 110-D, Agreement No. 89-47, Department of Civil Engineering, University of
Idaho, 1992, 252 pp.
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Materials. Highway Research Record 345, Highway Research Board, National Research
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May 16-18, 1962, St. Louis, Missouri, Special Report 73.
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Jiang, Y.J. and Darter, M.I. (2000). Structural Factors for Jointed Plain Concrete Pavements:
SPS-2 - Initial Evaluation and Analysis, Final Report.
Jones, M.P. and M.W. Witczak. Subgrade Modulus on the San Diego Test Road. Transportation
Research Record 641, Transportation Research Board, National Research Council,
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Karamihas, S.M., Gillespie, T.D., Perera, R.W., and Kohn, S.D.,(1999). Guidelines for
Longitudinal Pavement Profile Measurements, NCHRP Report 434, Transportation
Research Board.
Khazanovich et al, (1998). Common Characteristics of Good and Poorly Performing PCC
Pavements, FHWA-RD-97-13.
Larson, G. and B. J. Dempsey. Enhanced Integrated Climatic Model Version 2.0. Report No.
DTFA MN/DOT 72114, University of Illinois at Urbana-Champaign, 1977.
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Larson, G. and Dempsey, B. J. (1997-2003). “Enhanced Integrated Climatic Model Version 3.0,”
University of Illinois at Urbana-Champaign and Newmark Civil Engineering Laboratory.
Report No. DTFA MN/DOT 72114.
Lary, J.A. and J.P. Mahoney. Seasonal Effects on the Strength of Pavement Structures.
Transportation Research Record No. 954, Transportation Research Board, National
Research Council, Washington, D.C., 1984, pp. 88-94.
Lukanen, E. O., R. Stubstad & Robert C. Briggs , “Temperature Predictions and Adjustment
Factors for Asphalt Pavements,” Report FHWA-RD-98-085, Federal Highway
Administration, 2000.
Lytton, R. L., D. E., Pufahl, C. H., Michalak, H. S. Liang and B. J. Dempsey. An Integrated
Model Of Climatic Effects On Pavements. Final Report, Federal Highway Administration
Report No. FHWA-RD-90-033. Washington, D. C., 1989.
Mohseni, A. and M. Symons , "Improved AC Pavement Temperature Models from LTPP
Seasonal Data," Prepared for Presentation at 77th Annual TRB Conference, Washington
D.C., 1998.
Morian, D. A., Gibson, S. D., and Epps, J. A., (1998). Maintaining Flexible Pavements – The
Long- Term Pavement Performance Experiment SPS-3 5-Year Data Analysis, Report No.
FHWARD- 97-102.
Morian, D.A., Epps, J.A., and Gibson, S.D., “Pavement Treatment Effectiveness, 1995 SPS-3
and SPS-4 Site Evaluations.” Report No. FHWA-RD-96-208. Federal Highway
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Owusu-Antwi, E.B., Titus-Glover, L, and Darter, M.I.(1998). Design and Construction of PCC
Pavements, Vol. I: Summary of Design Features and Construction Practices That
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Paterson, W.D.O.(1987). Road Deterioration and Maintenance Effects, Models for Planning and
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Perera, R. W. and Kohn, S. D. (2001).” LTPP Data Analysis: Factors Affecting Pavement
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Performing AC Pavements.” Report No FHWA-RD-99-193. Federal Highway
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Richter, C.A. and M.W. Witczak. Application of LTPP Seasonal Monitoring Data to Evaluate
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116
10. APPENDICES All Appendices are provided on the attached CD to this report. List is provided below:
Appendix A: Bibliography: Related FHWA and NCHRP Reports
Appendix B: Performance Trends for GPS-1 Sites
Appendix C: Performance Trends for GPS-3 Sites
Appendix D: Performance Trends for GPS-5 Sites
Appendix E: Performance Trends for GPS-6A Sites
Appendix F: Performance Trends for SPS-3 Sites
Appendix G: Table for Design Inputs for the MEPDG
File Folder: {ID_LTPP Mini Database}
The folder includes MS-Access Database (MDB) files for the developed mini database, and
Excel sheets for the performance trends.