The mechanistic-empirical pavement design guide (MEPDG) is the research version of the newly released DARWin-ME software by AASHTO. MEPDG includes two models for Levels 2 and 3 hot-mix asphalt (HMA) dynamic modulus (E*) predictions. The two models are NCHRP 1-37A and NCHRP 1-40D. The primary difference between the two is the binder stiffness parameter; viscosity or shear modulus. Moreover, MEPDG includes three levels for binder stiffness characterization: viscosity for Level 1 conventional binders, shear modulus for Level 1 superpave binders, and default values for Level 3. The influence of the binder characterization input level on the performance of the MEPDG E* predictive models is evaluated in this paper. To calibrate the models for Idaho, 27 HMA mixtures commonly used in Idaho were investigated. Results showed that the performance of the investigated models varied based on the temperature and the binder characterization method. The NCHRP1-37A E* model with MEPDG Level 3 binder inputs yielded ...

The hot-mix asphalt (HMA) dynamic modulus ($E^{*}$ ) is a fundamental mechanistic property that defines the strain response of asphalt concrete mixtures as a function of loading rate and temperature. It is one of the HMA primary material inputs for common software for the mechanistic-empirical design of pavements. Laboratory testing of dynamic modulus requires expensive advanced testing equipment that is not readily available in the majority of laboratories in Middle Eastern countries, yet some of these countries are looking for implementing new pavement design methods such as those given in current standards. Thus, many research studies have been dedicated to develop predictive models for $E^{*}$ . This paper aims to apply artificial neural networks (ANNs) for $E^{*}$ predictions based on the inputs of the models most widely used today, namely: Witczak NCHRP 1-37A, Witczak NCHRP 1-40D and Hirsch $E^{*}$ predictive models. A total of 25 mixes from the Kingdom of Saudi Arabia (KSA), and 25 mixes from Idaho state were combined together in one database containing $\mathrm{3,720}{E}^{*}$ measurements. The database also contains the volumetric properties and aggregate gradations for all mixes as well as the binder complex shear modulus ($G_b^{*}$ ), phase angle ($\delta$ ), and Brookfield viscosity ($\eta$ ). A global sensitivity analysis (GSA) was applied to investigate the most significant parameters that affect $E^{*}$ predictions. The GSA procedures based on the Fourier amplitude sensitivity test (FAST) and Sobol sequence approaches were implemented in commercially available software to evaluate the sensitivity of the three regression models to their input parameters. The ANN models, using the same input variables of the three predictive models, generally yielded more accurate $E^{*}$ predictions. Moreover, the GSA showed that aggregate, binder, and mixture representative parameters have convergent effects on $E^{*}$ predictions using one model applied, whereas binder representative parameters have the dominant effect on $E^{*}$ predictions using both of the other two models.

Pavement friction is one of the primary factors that affect highway safety. Pavements with adequate surface friction reduce the number of wet skidding crashes. The objective of this study was to develop a predictive model for friction loss on pavement surfaces. The model incorporates parameters that describe aggregate shape characteristics, aggregate resistance to abrasion and polishing, aggregate gradation, and polishing cycles. This model was developed on the basis of the results of a comprehensive experimental program. Square-shaped slabs of different asphalt mixtures were prepared in the laboratory by using a linear kneading compactor and polished with a wheel-polishing device. The frictional characteristics of the surface of the test slabs were measured after different intervals of polishing, and statistical analysis was performed to relate friction to mixture and aggregate characteristics.

The mineral filler is an integral constituent of asphalt mixture, which affects its performance. This research investigates the effect of using four local industrial waste/by-product materials (marble, granite, steel slag and hydrated lime powder) as mineral fillers on asphalt mixtures characteristics. The detailed performance of these materials as well as asphalt binder and asphalt mixture characteristics are presented. In Egypt, limestone powder is a typical mineral filler in asphalt mixture composition. A control asphalt mixture with limestone mineral filler is designed using Marshall method. Other four asphalt mixtures are prepared using similar constituents and volumetrics except replacing the limestone filler with other types of industrial waste/by-product materials, while maintaining the same percentage of mineral filler as in the control mix. Marshall stability and moisture-induced damage of the investigated asphalt mixtures are determined and compared to the control asphalt mixture results. In addition, the performance characteristics of the investigated mixtures are evaluated by conducting cyclic performance tests including the dynamic modulus (E*) and flow number (FN). Testing results show that the asphalt mixture containing marble as a filler yields the highest stability; while, the hydrated lime filler noticeably enhances the performance of the asphalt mixture in terms of moisture damage resistance, E*, and FN. Unexpectedly, steel slag filler does not improve the asphalt mixture performance. Finally, the stiffness of the investigated mixtures is evaluated using three well-known E*-predictive models (NCHRP 1-37A, Hirsh, and NCHRP 1-40D); while, the resistance of the investigated mixtures to permanent deformation is evaluated using a calibrated version of Apeagyei-literary model based on the investigated mixtures to predict FN at 54.4°C and 1 Hz frequency.