Investigation of AASHTOWare Pavement ME Design/DARWin-ME Performance Prediction Models for Iowa Pavement Analysis and Design, 2015

The Mechanistic-Empirical Pavement Design Guide (MEPDG) was developed under National Cooperative Highway Research Program (NCHRP) Project 1-37A as a novel mechanistic-empirical procedure for the analysis and design of pavements. The MEPDG was subsequently supported by AASHTO’s DARWin-ME and most recently marketed as AASHTOWare Pavement ME Design software as of February 2013. Although the core design process and computational engine have remained the same over the years, some enhancements to the pavement performance prediction models have been implemented along with other documented changes as the MEPDG transitioned to AASHTOWare Pavement ME Design software. Preliminary studies were carried out to determine possible differences between AASHTOWare Pavement ME Design, MEPDG (version 1.1), and DARWin-ME (version 1.1) performance predictions for new jointed plain concrete pavement (JPCP), new hot mix asphalt (HMA), and HMA over JPCP systems. Differences were indeed observed between the pavement performance predictions produced by these different software versions. Further investigation was needed to verify these differences and to evaluate whether identified local calibration factors from the latest MEPDG (version 1.1) were acceptable for use with the latest version (version 2.1.24) of AASHTOWare Pavement ME Design at the time this research was conducted. Therefore, the primary objective of this research was to examine AASHTOWare Pavement ME Design performance predictions using previously identified MEPDG calibration factors (through InTrans Project 11-401) and, if needed, refine the local calibration coefficients of AASHTOWare Pavement ME Design pavement performance predictions for Iowa pavement systems using linear and nonlinear optimization procedures. A total of 130 representative sections across Iowa consisting of JPCP, new HMA, and HMA over JPCP sections were used. The local calibration results of AASHTOWare Pavement ME Design are presented and compared with national and locally calibrated MEPDG models

Investigation of AASHTOWare Pavement ME Design/DARWin-MEPerformance Prediction Models for Iowa Pavement Analysis and Design

The Mechanistic-Empirical Pavement Design Guide (MEPDG) was developed under National Cooperative Highway Research Program (NCHRP) Project 1-37A as a novel mechanistic-empirical procedure for the analysis and design of pavements. The MEPDG was subsequently supported by AASHTO’s DARWin-ME and most recently marketed as AASHTOWare Pavement ME Design software as of February 2013. Although the core design process and computational engine have remained the same over the years, some enhancements to the pavement performance prediction models have been implemented along with other documented changes as the MEPDG transitioned to AASHTOWare Pavement ME Design software. Preliminary studies were carried out to determine possible differences between AASHTOWare Pavement ME Design, MEPDG (version 1.1), and DARWin-ME (version 1.1) performance predictions for new jointed plain concrete pavement (JPCP), new hot mix asphalt (HMA), and HMA over JPCP systems. Differences were indeed observed between the pavement performance predictions produced by these different software versions. Further investigation was needed to verify these differences and to evaluate whether identified local calibration factors from the latest MEPDG (version 1.1) were acceptable for use with the latest version (version 2.1.24) of AASHTOWare Pavement ME Design at the time this research was conducted. Therefore, the primary objective of this research was to examine AASHTOWare Pavement ME Design performance predictions using previously identified MEPDG calibration factors (through InTrans Project 11-401) and, if needed, refine the local calibration coefficients of AASHTOWare Pavement ME Design pavement performance predictions for Iowa pavement systems using linear and nonlinear optimization procedures. A total of 130 representative sections across Iowa consisting of JPCP, new HMA, and HMA over JPCP sections were used. The local calibration results of AASHTOWare Pavement ME Design are presented and compared with national and locally calibrated MEPDG models

Investigation of AASHTOWare Pavement ME Design/Darwin-ME™ Performance Prediction Models for Iowa Pavement Analysis and Design

The Mechanistic Empirical Pavement Design Guide (MEPDG) was developed under the National Cooperative Highway Research Program (NCHRP) Project 1-37A as a novel mechanical-empirical procedure for analysis and design of pavements. The MEPDG was subsequently renamed the DarWin-ME in April 2011 and, most recently, marketed as the AASHTOWare Pavement ME Design as of February 2013. Although the core design process and computational engine have remained the same over the years, some enhancements to the pavement performance prediction models were implemented along with other documented changes as the MEPDG transitioned to the AASHTOWare Pavement ME Design. Preliminary studies were carried out to determine possible differences between AASHTOWare Pavement ME Design, MEPDG (version 1.1) and DARWin-ME (version 1.1) performance predictions for new Jointed Plain Concrete Pavement (JPCP), new Hot-Mix Asphalt (HMA), and HMA over JPCP pavement systems. Differences were indeed observed between the pavement performance predictions produced by these software versions. Further investigation was needed to verify these differences and to evaluate whether identified local calibration factors from the latest MEPDG (version 1.1) were acceptable for use with the latest version (version 2.1.24) of AASTHOWare Pavement ME Design at the time this research was conducted. The primary objective of this research was to examine AASHTOWare Pavement ME Design performance predictions using previously-identified MEPDG calibration factors (through Iowa DOT Project TR 401) and, if needed, refine local calibration coefficients of AASHTOWare Pavement ME Design pavement performance predictions for Iowa pavement systems using linear and nonlinear optimization procedures. A total of 130 representative sections across Iowa consisting of JPCP, new HMA and HMA over JPCP sections are used. The local calibration results of Pavement ME Design are presented and compared with national and MEPDG locally calibrated models

Investigation of AASHTOWare Pavement ME Design/DARWinME Performance Prediction Models for Iowa Pavement Analysis and Design

The Mechanistic-Empirical Pavement Design Guide (MEPDG) was developed under National Cooperative Highway Research Program (NCHRP) Project 1-37A as a novel mechanistic-empirical procedure for the analysis and design of pavements. The MEPDG, published in 2008, was subsequently supported by AASHTO’s DARWin-ME pavement design software (starting in April 2011) and AASHTOWare Pavement ME Design was the next generation of pavement design software (as of February 2013)

THERMAL FATIGUE DAMAGE OF ASPHALT PAVEMENT

Fatigue damage can be defined by a decrease in stiffness of Asphalt Concrete (AC) under repeated traffic loading. For each cycle of traffic loading, tensile strain develops at the bottom of AC layer of an asphalt pavement. Some localized damages occur in the material at minute-scale due to this developed tensile strain. These damages cause decrease in stiffness (E) of AC. Damage caused by a single vehicle is small. However the accumulated damage is not small if a large number of vehicles are considered over the design life of an asphalt pavement. After certain level of damage accumulation, bottom-up fatigue cracking initiates and forms alligator cracking at the surface. Like traffic loading, repeated day-night temperature cycle causes damages in AC. Damage due to a single day-night temperature fluctuation may be small. However the accumulated damage due to a large number of day-night temperature cycles may not be small. In this study, fatigue damage due to traffic loading is termed as traffic-induced fatigue damage, and fatigue damage due to temperature is termed as temperature-induced fatigue damage. The recently developed AASHTOWare Pavement Mechanistic-Empirical (ME) Design Guide predicts the fatigue performance of AC based on repeated traffic-induced tensile strain at the bottom of AC layer. Cyclic thermal strain due to day-night temperature fluctuation is not considered due to the fact that there is no closed-form solution or model available for calculating thermal fatigue damage. This study, for the first time, develops a closed-form equation for calculating the temperature-induced fatigue damage of AC. To develop the model, beam fatigue testing was conducted using different AC mixtures in the laboratory. The mechanical beam fatigue test data was correlated with the actual cyclic temperature loading test data. The model was then validated using an unknown test data. To that end, the developed model was calibrated for field conditions using the Falling Weight Deflectometer (FWD) test data. The developed model is used to evaluate fatigue damages of 34 Long-Term Pavement Performance (LTPP) test sections. Fatigue damage predicted by the traditional AASHTOWare Pavement ME Design approach, which considers only traffic-induced fatigue damage, is compared to the fatigue damage by the developed model which considers both traffic- and temperature-induced fatigue. Results show that the error may decrease by up to 29% through the incorporation of temperature-induced fatigue damage in the AASHTOWare Pavement ME Design approach. This means the reliability of alligator cracking prediction can be improved through the use of the developed thermal fatigue model. It is therefore suggested to include the temperature-induced fatigue damage model, which is developed in this study, in the AASHTOWare Pavement ME Design Software

Optimal Pavement Design and Rehabilitation Planning Using a Mechanistic-Empirical Approach

This paper presents the development of a pavement design and rehabilitation optimization decision-making framework based on Mechanistic-Empirical (ME) roughness transfer models. The AASHTOWare Pavement ME Design (the software of Pavement ME Design) is used to estimate pavement deterioration based on the combined effects of permanent deformation, fatigue, and thermal cracking. The optimization problem is first formulated into a mixed-integer nonlinear programming model to address the predominant trade-off between agency and user costs. To deal with the complexity associated with the pavement roughness transfer functions in the software and to use the roughness values as input to the optimization framework, a dynamic programming subroutine is developed for determining the optimal rehabilitation timing and asphalt concrete design thickness. An application of the proposed model is demonstrated in a case study. Managerial insights from a series of sensitivity analyses on different unit user cost values and model comparisons are presented

Material Characterization and Determination of MEPDG Input Parameters for Indiana Superpave 5 Asphalt Mixtures

Superpave 5 (SP 5) has the ability to slow asphalt binder aging in asphalt pavements, which is why the SP 5 mix with optimum asphalt binder content to yield 5% air voids has recently been used in Indiana roads. INDOT also uses the AASHTOWare Pavement ME design software in pavement design, and the current asphalt aging prediction model in Pavement ME was developed based on the conventional Superpave asphalt mixture (design air voids 4%) design method. For the successful use of the SP 5 mixture design method with Pavement ME, the input level and input parameters play a significant role. The objective of this study was to determine pavement performance using the three different input levels (Level 1, 2, and 3) and to recommend the necessary Pavement ME input parameters for SP 5 mixtures for accurate pavement performance prediction. The results show that Levels 2 and 3 are underpredicting or overpredicting the pavements’ distresses. Therefore, to capture the benefit of SP 5 pavement design, the Level 1 inputs (lab test results) were recommended for the Pavement ME. The findings of this research will provide guidance on using accurate input parameters for the Pavement ME design for SP 5 mixtures, resulting in more accurate asphalt pavement performance predictions during the pavement design process. It is anticipated that this will result in longer asphalt pavement service lives, which is a cost-effective benefit for INDOT

Calibration of Pavement ME Design and Mechanistic-Empirical Pavement Design Guide Performance Prediction Models for Iowa Pavement Systems

The AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) pavement performance models and the associated AASHTOWare® Pavement ME Design software are nationally calibrated using design inputs and distress data largely from the national Long-Term Pavement Performance (LTPP). Further calibration and validation studies are necessary for local highway agencies’ implementation by taking into account local materials, traffic information, and environmental conditions. This study aims to improve the accuracy of MEPDG/Pavement ME Design pavement performance predictions for Iowa pavement systems through local calibration of MEPDG prediction models. A total of 70 sites from Iowa representing both jointed plain concrete pavements (JPCPs) and Hot Mix Asphalt (HMA) pavements were selected. The accuracy of the nationally calibrated MEPDG prediction models for Iowa conditions was evaluated. The local calibration factors of MEPDG performance prediction models were identified using both linear and nonlinear optimization approaches. Local calibration of the MEPDG performance prediction models seems to have improved the accuracy of JPCP performance predictions and HMA rutting predictions. A comparison of MEPDG predictions with those from Pavement ME Design was also performed to assess if the local calibration coefficients determined from MEPDG version 1.1 software are acceptable with the use of Pavement ME Design version 1.1 software, which has not been addressed before. Few differences are observed between Pavement ME Design and MEPDG predictions with nationally and locally calibrated models for: (1) faulting and transverse cracking predictions for JPCP, and (2) rutting, alligator cracking and smoothness predictions for HMA. With the use of locally calibrated JPCP smoothness (IRI) prediction model for Iowa conditions, the prediction differences between Pavement ME Design and MEPDG are reduced. Finally, recommendations are presented on the use of identified local calibration coefficients with MEPDG/Pavement ME Design for Iowa pavement systems

Evaluation of the Impact of Soil Water Characteristic Curves (SWCC) on Nevada Pavement ME Design

The Mechanistic Empirical Pavement Design Guide (MEPDG) was developed by the American Association of State Highway and Transportation Officials (AASHTO), in cooperation with the Federal Highway Administration (FHWA), as part of the National Cooperative Highway Research Program (NCHRP), project 1-37A (1). The purpose of this work was to provide a state-of-practice tool that can be used to design new and rehabilitated pavement, and it relies on mechanistic-empirical principles. The mechanistic-empirical design method differs from previous methods by taking into consideration traffic conditions, climatic data, and material properties. Resilient modulus of the unbound layers plays a large role in pavement performance, and this parameter changes with seasonal variation. Currently, the AASHTOWare® Pavement ME software internally calculates this seasonal variation using climatic data and estimated unbound material parameters, including soil water characteristic curves (SWCC) and saturated hydraulic conductivity. This study seeks to evaluate the impact of SWCC and saturated hydraulic conductivity on Nevada Pavement ME Design. An extensive laboratory evaluation was conducted on 24Nevada unbound materials, which included testing for gradation, Atterberg Limits, maximum dry density, optimum water content, specific gravity of solids, SWCC, methylene blue value, percent fines content, r-value, and saturated hydraulic conductivity. The model used to fit the SWCCs, consistent with the MEPDG, was the Fredlund and Xing model, which fit the SWCC data well. A sensitivity analysis was conducted in AASHTOWare® Pavement ME, where directly measured unbound material properties, estimated unbound material properties, and internally estimated unbound material properties were used. It was found that for District 1, the internally estimated properties underestimate the impact of SWCC and saturated hydraulic conductivity, which is seen in an under-prediction of AC bottom-up fatigue cracking. For District 2, the internally estimated properties overestimate the impact of SWCC and saturated hydraulic conductivity, which is seen in an over-prediction of AC bottom-up fatigue cracking. In District 3, there was little impact from SWCC and saturated hydraulic conductivity. Additionally, historical records were collected from recent NDOT pavement projects and summarized in an electronic format. Combined with the laboratory evaluation, a comprehensive database for Nevada unbound material properties was produced. This database was used to make recommendations for unbound material properties for use in Nevada Pavement ME Design