Date of Award

Spring 2018

Project Type

Thesis

Program or Major

Civil Engineering

Degree Name

Doctor of Philosophy

First Advisor

Jo S Daniel

Second Advisor

Eshan V Dave

Third Advisor

Majid Ghayoomi

Abstract

Cracking is one of the major distresses encountered in pavements. Pavements that fail prematurely due to cracking precipitate lower ride quality, elevate the chance of road accidents, and cause agencies to spend considerable amount of public funds on pavement maintenance and rehabilitation. As part of the concerted endeavor to ensure high performing pavements, extensive research is being undertaken throughout the United States to develop more effective and efficient performance based materials selection and specification procedures as well as mechanistic-empirical (M-E) methods for pavement cracking performance evaluation. However, agencies have been hesitant to introduce the methods to their specifications, pavement evaluation protocols and design procedures for reasons related to complexity and uncertainty associated to precisions and accuracy of these methods.

This dissertation contributes to the ongoing performance based specifications and design efforts by addressing known gaps related to linear viscoelastic and fracture characterization of asphalt concrete. Overarching goals of this dissertation research has been enhancement of performance property determination processes and increased confidence in asphalt pavement performance predictions. Specific research contributions include, a simple and robust method is provided to determine phase angle from stiffness data and BBR low temperature specification parameters, stiffness (S) and relaxation properties (m-value), from DSR measurement for linear viscoelastic characterization of asphalt concrete. The ability of dynamic modulus and phase angle master curve parameters to capture the changes in mixture properties is investigated. Finally, increased understanding is achieved regarding fracture properties of asphalt mixtures as it relates to the effect of mix variables and number of replicates to be tested to obtain representative measurement to help agencies make informed decision during mix design and production.

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