This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.

In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.

Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.

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One of the main requirements of designing perpetual pavements is to determine the endurance limit of Hot Mix Asphalt (HMA). The purpose of this study was to validate the endurance limit for HMA using laboratory beam fatigue tests. A mathematical procedure was developed to determine the endurance limit of HMA

One of the main requirements of designing perpetual pavements is to determine the endurance limit of Hot Mix Asphalt (HMA). The purpose of this study was to validate the endurance limit for HMA using laboratory beam fatigue tests. A mathematical procedure was developed to determine the endurance limit of HMA due to healing that occurs during the rest periods between loading cycles. Relating healing to endurance limit makes this procedure unique compared to previous research projects that investigated these concepts separately. An extensive laboratory testing program, including 468 beam tests, was conducted according to AASHTO T321-03 test procedure. Six factors that affect the fatigue response of HMA were evaluated: binder type, binder content, air voids, test temperature, rest period and applied strain. The endurance limit was determined when no accumulated damage occurred indicating complete healing. Based on the test results, a first generation predictive model was developed to relate stiffness ratio to material properties. A second generation stiffness ratio model was also developed by replacing four factors (binder type, binder content, air voids, and temperature) with the initial stiffness of the mixture, which is a basic material property. The model also accounts for the nonlinear effects of the rest period and the applied strain on the healing and endurance limit. A third generation model was then developed by incorporation the number of loading cycles at different locations along the fatigue degradation curve for each test in order to account for the nonlinearity between stiffness ratio and loading cycles. In addition to predicting endurance limit, the model has the ability to predict the number of cycles to failure at any rest period and stiffness combination. The model was used to predict fatigue relationship curves for tests with rest period and determining the K1, K2, and K3 fatigue cracking coefficients. The three generation models predicted close endurance limit values ranging from 22 to 204 micro strains. After developing the third generation stiffness ratio model, the predicted endurance limit values were integrated in the strain-Nf fatigue relationships as a step toward incorporating the endurance limit in the MEPDG software. The results of this study can be used to design perpetual pavements that can sustain a large number of loads if traffic volumes and vehicle weights are controlled.

ContributorsSouliman, Mena (Author) / Mamlouk, Michael S. (Thesis advisor) / Witczak, Matthew W. (Thesis advisor) / Kaloush, Kamil (Committee member) / Arizona State University (Publisher)
Created2012
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Description
The fatigue resistance of asphalt concrete (AC) plays an important role in the service life of a pavement. For predicting the fatigue life of AC, there are several existing empirical and mechanistic models. However, the assessment and quantification of the ‘reliability’ of the predictions from these models is a substantial

The fatigue resistance of asphalt concrete (AC) plays an important role in the service life of a pavement. For predicting the fatigue life of AC, there are several existing empirical and mechanistic models. However, the assessment and quantification of the ‘reliability’ of the predictions from these models is a substantial knowledge gap. The importance of reliability in AC material performance predictions becomes all the more important in light of limited monetary and material resources. The goal of this dissertation research is to address these shortcomings by developing a framework for incorporating reliability into the prediction of mechanical models for AC and to improve the reliability of AC material performance prediction by using Fine Aggregate Matrix (FAM) phase data. The goal of the study is divided into four objectives; 1) development of a reliability framework for fatigue life prediction of AC materials using the simplified viscoelastic continuum damage (S-VECD) model, 2) development of test protocols for FAM in similar loading conditions as AC, 3) evaluation of the mechanical linkages between the AC and FAM mix through upscaling analysis, and 4) investigation of the hypothesis that the reliability of fatigue life prediction of AC can be improved with FAM data modeling.

In this research effort, a reliability framework is developed using Monte Carlo simulation for predicting the fatigue life of AC material using the S-VECD model. The reliability analysis reveals that the fatigue life prediction is very sensitive to the uncertainty in the input variables. FAM testing in similar loading conditions as AC, and upscaling of AC modulus and damage response using FAM properties from a relatively simple homogenized continuum approach shows promising results. The FAM phase fatigue life prediction and upscaling of FAM results to AC show more reliable fatigue life prediction than the fatigue life prediction of AC material using its experimental data. To assess the sensitivity of fatigue life prediction model to uncertainty in the input variables, a parametric sensitivity study is conducted on the S-VECD model. Overall, the findings from this research show promising results both in terms of upscaling FAM to AC properties and the reliability of fatigue prediction in AC using experimental data on FAM.
ContributorsGudipudi, Padmini Priyadarsini (Author) / Underwood, Benjamin S (Thesis advisor) / Kaloush, Kamil (Committee member) / Mamlouk, Michael (Committee member) / Neithalath, Narayanan (Committee member) / Arizona State University (Publisher)
Created2016