Date of Award

Fall 2021

Project Type

Dissertation

Program or Major

Civil Engineering

Degree Name

Doctor of Philosophy

First Advisor

Eshan ED Dave

Second Advisor

Jo JS Sias

Third Advisor

Majid MG Ghayoomi

Abstract

The asphalt mix design and evaluation approaches are divided into two main categories as empirical and mechanistic-empirical (M-E) methods. The empirical methods are based on empirical observations of in-service pavement performance, and they do not take into account engineering properties or failure criteria. The M-E methods were introduced as a new generation for design and evaluation approaches that consider fundamental mixture properties such as material stiffness to determine the pavement's structural response. However, the need for expensive and time-consuming performance-based laboratory tests and local calibration makes the M-E methods unsuitable for routing design. In addition, during the last few years, the asphalt paving industry has been consistently tried to improve pavement performance by introducing new types of materials in asphalt mixtures. Regardless of all the positive effects of innovative materials on mix performance, the M-E design and evaluation methods might not be able to fully capture the benefits that may be achieved through using these materials. It likely stems from the fact that the M-E methods only utilize mix stiffness to evaluate the performance with respect to different distresses. Therefore, a methodology needs to be developed within the framework of current design and evaluation approaches to consider the mixture performance and the impact of innovative materials on pavement performance.This dissertation research aimed to assess the mixture properties indices that can be implemented in performance-based design methods. The proposed endeavor will yield a more precise evaluation of the innovative materials impact on asphalt mixture performance through consideration of the viscoelastic nature of asphalt mixtures to determine mechanistic damage effect. Furthermore, several prediction models for a simplified viscoelastic continuum damage-based fatigue index (as crack initiation phase) and mixture fracture energy (as crack propagation phase) were developed to investigate asphalt mixture performance with respect to cracking. The models include the simultaneous impact of various mix variables that are available during the mix design process. Thus, they can be used as a predesign tool to investigate mixtures' cracking properties without the need for any performance laboratory test data. Finally, a cracking balance design diagram (CBDD) was generated with a combination of prediction models for crack initiation and propagation. The CBDD helps toward better identification of cracking performance considering the simultaneous effects of both cracking phases in a single diagram.

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