الفهرس 
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Abstract
AASHTOWare-Pavement ME Design approved by AASHTO is the production version of the Mechanistic-Empirical Pavement Design Guide (MEPDG). MEPDG represents an improved and advanced methodology for analysis, design and evaluation of both flexible and rigid pavement systems. The dynamic modulus (E*) is the primary hot-mix asphalt (HMA) material property input required in the MEPDG. MEPDG provides three hierarchical levels of inputs for the E* of the HMA. For level 1 HMA stiffness characterization, laboratory measured E* values at different temperatures and frequencies are required. For levels 2 and 3, predictive models are used to estimate the moduli at the anticipated pavement temperature and loading frequency. The selection of a particular level of input depends on the amount of information available to the designer and the importance and criticality of the project. Similarly, levels 1 and 2 inputs for the binder characterization require laboratory measurements based on the binder grading system. For Superpave performance grade binders, laboratory-measured binder shear modulus (Gb*) and phase angle (δ) values at different temperatures and one angular loading frequency of 10 rad/s (1.59 Hz) are required. For conventional binder grades, traditional asphalt testing results, such as penetration, ring and ball softening point, absolute and kinematic viscosities, and Brookfield viscsoisties, are required. For level 3 binder inputs, users are only required to select the binder grade based on one of the binder grading systems (i.e., Superpave performance grade, conventional penetration grade, or conventional viscosity grade). This study focused on developing a database containing mechanical properties of a wide variety of HMA mixtures typically used in the kingdom of Saudi Arabia (KSA). Then checking the accuracy of candidate E* predictive models along with the binder characterization input level for the kingdom HMA mixtures. Artificial Neural Networks (ANN) for E* predictions were also investigated and compared with the predictions of the regression models. Finally, computer simulation runs using MEPDG software were conducted to investigate the influence of E* on MEPDG pavement In this research, 25 Superpave mixes were collected from different ongoing construction projects. Laboratory E* tests were conducted on 11 different plant-produced HMA mixtures and 14 laboratory mixtures. A total of 108 samples were prepared for E* testing. The investigated mixes contain conventional as well as modified binders covering the three different superpave binder grades typically used in KSA. Different types of modifiers and polymers were used to improve the binder performance grade (PG). All The binder viscosity (η), Gb*, and δ for each binder were also measured in the laboratory. All binder tests were conducted on short term aged binders using roller thin film oven (RTFO) test. Three levels of binder characterization were investigated in this study: (level 1a: measured viscosity resulted from Brookfield rotational viscometer (BRV), level 1b: G* and δ from dynamic shear rehometer (DSR) interpolated at 10 rad/sec angular frequency and different temperatures, and level 3 typical MEPDG A-VTS values based on the binder performance grade. Five E* predictive models were evaluated in this study ( NCHRP 1-37A Witczak model , 1-40D Witczak model , Hirsh model, and ANNs models based on Witczak models input parameters (ANNsW) and Hirsh input parameters (ANNsH). Results showed that the performance of the investigated models varied by temperature and the binder characterization method. The investigated models yield biased predictions at all temperature ranges especially at the high and low temperatures which agrees with the cited literature. among the studied levels of binder characterization, the NCHRP 1-37A model with level 3 binder characterization yielded the most accurate least biased E* predictions compared to the NCHRP 1-40D, and Hirsh models. Among all investigated models, the ANNsW with Level 1a binder characterization yielded the least biased E* estimates for KSA mixes. Finally, E* has a great influence on pavement performance as predicted using the MEPDG.