Development of New Dynamic Modulus (E*) Predictive Models for Hot Mix Asphalt Mixtures

This Ph.D. dissertation presents the findings of a research program that was conducted to develop a new, robust, and effective set of dynamic modulus (|E*|) predictive models for hot mix asphalt (HMA) mixtures. These models are capable of implementing different types of mixture and binder properties, i.e., resilient modulus (MR), viscosity(eta), and binder shear modulus (|G*|) as its primary input data. Of equal significance was being capable to estimate changes in modulus as a function of changes in mixture volumetric properties, aggregate gradation, binder properties, test conditions including temperature and loading frequency (time) for various lab results based on different test protocols measurements.;The dynamic modulus, |E*|, is a fundamental property that defines the strain response characteristics of asphalt concrete mixtures as a function of loading rate and temperature. The significance of this material property is three-fold. First, it is one of the primary material property inputs in the Mechanistic Empirical Pavement Design Guide (MEPDG) and software developed by NCHRP Project 1-37A. The MEPDG uses a "master curve" and timetemperature shift factors in its internal computations of modulus. In the MEPDG, the master curve is constructed using a hierarchical structure of inputs ranging from estimates based on mixture volumetric properties and binder tests to full scale mixture dynamic modulus testing. Second, the | E*| is one of the primary properties measured in the Asphalt Mixture Performance Test (AMPT) protocol that complements the volumetric mix design. Third, the |E*| is one of the fundamental linear viscoelastic (LVE) material properties that can be used in advanced pavement response models that are based on viscoelasticity.;For this study, a large data set that covers the complete range of potential input conditions is needed. Therefore, modulus values from multiple mixtures and binders were required and were assembled from existing national efforts and from data obtained at North Carolina State University. The data consists of measured moduli from both modified and unmodified mixtures from numerous geographical locations across the United States, and mixture dynamic moduli measured using two test protocols, the asphalt mixture performance tester (AMPT) and AASHTO TP-62 under different aging conditions. The data also consist of binder shear moduli values measured under different aging conditions.;In spite of the demonstrated significance of the |E*|, it is not included in the current longterm pavement performance (LTPP) materials tables. Therefore, a joint study including NCSU researchers and Nichols Consulting Engineers was conducted to populate the LTPP layers with |E*| values and some of the findings are presented in this dissertation. As such, the objective of the dynamic modulus project was to use readily available binder, volumetric, and resilient material properties in the LTPP database to develop |E*| estimates. A part of the research presented in this dissertation provides a thorough review of existing prediction models. In addition, several models have been developed using Artificial Neural Networks (ANNs) for use in this project. Included in this study are assessments of each model, quality control checks applied to the data, and the final structure and format of the dynamic modulus data added to the LTPP database. A program was also developed by the researchers involved in this project at NCSU to assist in populating the LTPP database.;The comprehensive study completed at NCSU for populating the LTPP layers with dynamic modulus values resulted in assembling a numerous |E*| and corresponding binder |G*| test measurements. This extensive master database was then utilized to develop a new set of rational, unbiased and accurate |E*| closed-form models. Two of the proposed models are capable of accurately estimating |E*| of HMA mixtures over the entire range of customarily used testing conditions. In addition to that a set of five different regression models were developed and calibrated for typical temperatures recommended in AASHTO TP-62 test protocol including -10°, 4.4°, 21.1°, 37.8°, and 54.4 °C.;The newly developed |E*| models has similar mathematical sigmoid structure as the one used in the guide, and it is believed that they can be considered for implementation in a future revision of pavement design guide. Given the significance of |E*|, the work done in this study to evaluate existing prediction models, develop new models, and populating the LTPP database will provide a very valuable data source for the pavement community. Supplementing the full suite of material properties, performance history, traffic, and climate with |E*| estimates will be advantageous in conducting MEPDG calibration, validation, and implementation.