Mohamed M El-Basyouny

Effective Temperature for Analysis of Permanent Deformation and Fatigue Distress on Asphalt Mixtures

The determination of a single test temperature for asphalt mixtures to simulate major distresses on flexible pavement is quite significant in characterizing asphalt mixtures. It is apparent that the practical use of a single temperature would reduce testing needs as well as relevant analysis efforts. Effective temperature (Teff) can be defined as a single test temperature at which an amount of distress would be equivalent to that which occurs from the seasonal temperature fluctuation throughout the annual temperature cycle. The primary goal of this study was to develop the Teff for permanent deformation and fatigue cracking of asphalt mixtures. The predicted distress for both permanent deformation and fatigue cracking that is used to optimize the Teff models and to find optimal coefficients was obtained by running the latest version of the Mechanistic–Empirical Pavement Design Guide (MEPDG). A feature of the developed Teff models is that they incorporate comprehensive climatic characteristics as well as loading frequency. By incorporating those parameters, it was observed that the model is well correlated with the MEPDG distress results. Therefore, the use of the Teff models is recommended as a means to suggest a testing temperature in the simple performance test (SPT) for the distresses. This research work addresses the integration of the MEPDG methodology (NCHRP 1-37A and NCHRP 1-40D) with the SPT (NCHRP 9-19) to develop the Teff models for the two distresses. Furthermore, the Teff methodology will be incorporated into the performance-related specification being developed (NCHRP 9-22).

Calibration of alligator fatigue cracking model for 2002 design guide

In AASHTOs 2002 design guide, the classic fatigue cracking mechanism, which normally initiates at the bottom of the asphalt layer and propagates to the surface (bottom-up cracking), was studied. The prediction of bottom-up alligator fatigue cracking was based on a mechanistic approach to calculate stress and strain. An empirical approach then related these strains to fatigue damage in pavement caused by traffic loads. To provide credibility to the new design procedure, the theoretically predicted distress models must be calibrated to “real-world” performance. In fact, calibration of these distress models is considered to be the most important activity to facilitate implementation, acceptance, and adoption of the design procedure and to establish confidence in the entire procedure. The procedure followed for the national calibration of the alligator fatigue–cracking model used in the AASHTO 2002 design guide is discussed. This calibration study used data from all over the United States, with different environments, material, and traffic. A total of 82 pavement sections from 24 different states were used in the calibration. Tensile strain at the bottom of each asphalt layer was calculated using a linear layer elastic analysis procedure. The initial (base) reference model used in the calibration was the Asphalt Institute MS-1 model. This model was used to compute the damage caused by traffic loads and pavement structure. Predicted damage was then correlated to the measured fatigue cracking in the field, and a transfer function was obtained for the alligator fatigue–cracking distress.

Probabilistic Performance-Related Specifications Methodology Based on "Mechanistic–Empirical Pavement Design Guide"

In current construction practice, as-built asphalt concrete acceptance is typically based on several key quality assurance–quality control parameters. These parameters have pay factors that are empirically established by judgment of the pavement community. The pay factor has been used to reward or penalize the contractor for construction quality. These properties are known to influence pavement performance, but the specific effect on performance and project life has been impossible to estimate. Because of this, there is an urgent need to tie deviations of the field production from the specified job mix to performance and pavement life. This research focused on the integration of AASHTOs Mechanistic–Empirical Pavement Design Guide (NCHRP 1-37A and NCHRP 1-40D) with the Simple Performance Test (NCHRP 9-19) to develop a probabilistic quality specification for quality assurance of hot-mix asphalt (HMA) construction. This has been the major objective of NCHRP 9-22. The methodology is based on relating the HMA dynamic modulus, through a closed-form solution, to major pavement distress. A Monte Carlo simulation is run by using the project mix design to predict the as-designed distress and its associated variability. Then the remaining service life of the pavement is predicted from the as-designed distress. Similarly, Monte Carlo simulation is run for the as-built material to estimate the remaining service life. The difference between the as-built and the as-design distress provides the predicted difference in quality of construction from the mix design. The difference is used to calculate the pay factor for each distress. The total pay factor is the sum of the individual distress pay factors. Finally, the initial international roughness index, representing the ride quality pay factor, is added.

Methodology to Predict Alligator Fatigue Cracking Distress Based on Asphalt Concrete Dynamic Modulus

This study focused on integrating the Mechanistic–Empirical Pavement Design Guide (MEPDG) methodology (NCHRP 1-37A and NCHRP 1-40D) with the simple performance test methodology (NCHRP 9-19) to develop a comprehensive bottom-up fatigue cracking distress prediction model based on the dynamic modulus in a methodology that can be implemented in a probabilistic performance-related specification (PRS) methodology for quality assurance (QA) of hot-mix asphalt (HMA) construction (NCHRP 9-22). The main approach for a comprehensive fatigue cracking–damage predictive methodology was to run MEPDG for a large number of simulations using combinations of inputs that were believed significant in classic load-associated bottom-up alligator fatigue cracking and to develop an accurate closed-form solution for the fatigue damage–cracking distress prediction based on the dynamic modulus of HMA. Four major studies have been completed for fatigue cracking prediction methodology: (a) development of a fatigue damage model based on a two-layer pavement system, (b) development of an asphalt concrete (AC) effective dynamic modulus model, (c) development of a predictive methodology to transform a multilayer to a two-layer pavement system, and (d) validation of the prediction process. The developed methodology approximately but accurately predicts the classic AC alligator fatigue cracking distress that is close to that from MEPDG. Thus, implementation of the methodology into the PRS-based QA system in NCHRP 9-22 is recommended.

Impact of Traffic Data on the Pavement Distress Predictions using the Mechanistic Empirical Pavement Design Guide

ABSTRACT This study examines the adequacy of using conventional traffic data and national default values in the absence of weigh-in-motion (WIM) data for pavement design. A comparative study was conducted on 14 unique sections in Arizona (AZ), where WIM data are available through the Long Term Pavement Performance (LTPP) program. The study consists of two parts: 1) comparisons of input traffic data and 2) comparisons of pavement distresses predicted by the Mechanistic Empirical Pavement Design Guide (MEPDG). The traffic related input parameters include average design-lane truck volumes, Vehicle Classification Percentages (VCP), Monthly Adjustment Factors (MAF), axle load distribution factors and the number of axles per truck. The truck volumes and VCPs are available through the Arizona Department of Transportation (ADOT) while only national average values are available for the other traffic inputs in the absence of WIM data. The comparisons of the input variables showed that the truck volumes for a design lane estimated from the ADOT database and default MAFs differed significantly from those in the LTPP database. The national default axle load distribution factors differed somewhat from the site-specific values. The differences in input data were reflected in the pavement distress values that were predicted by MEPDG. The outputs of the design guide reveal large prediction errors, particularly for longitudinal cracking, exceeding 40 percent in absolute percent error on average. The large difference in design-lane truck volume was the major source of the large prediction errors. The national default factors also generated moderate prediction errors, and the performance improved slightly with the use of the AZ average factors. Finally, the differences in MAF had little impact on predictions of pavement distress.