Matching Items (2)
Filtering by
- Creators: Kaloush, Kamil
Description
Deterministic solutions are available to estimate the resilient modulus of unbound materials, which are difficult to interpret because they do not incorporate the variability associated with the inherent soil heterogeneity and that associated with environmental conditions. This thesis presents the stochastic evaluation of the Enhanced Integrated Climatic Model (EICM), which is a model used in the Mechanistic-Empirical Pavement Design Guide to estimate the soil long-term equilibrium resilient modulus. The stochastic evaluation is accomplished by taking the deterministic equations in the EICM and applying stochastic procedures to obtain a mean and variance associated with the final design parameter, the resilient modulus at equilibrium condition. In addition to the stochastic evaluation, different statistical analyses were applied to determine that the uses of hierarchical levels are valid in the unbound pavement material design and the climatic region has an impact on the final design resilient moduli at equilibrium. After determining that the climatic regions and the hierarchical levels are valid, reliability was applied to the resilient moduli at equilibrium. Finally, the American Association of State Highway and Transportation Officials (AASHTO) design concept based on the Structural Number (SN) was applied in order to illustrate the true implications the hierarchical levels of design and the variability associated with environmental effects and soil properties have in the design of pavement structures. The stochastic solutions developed as part of this thesis work together with the SN design concept were applied to five soils with different resilient moduli at optimum compaction condition in order to evaluate the variability associated with the resilient moduli at equilibrium condition. These soils were evaluated in five different climatic regions ranging from arid to extremely wet conditions. The analysis showed that by using the most accurate input parameters obtained from laboratory testing (hierarchical Level 1) instead of Level 3 analysis could potentially save the State Department of Transportation up to 10.12 inches of asphalt in arid and semi-arid regions.
ContributorsRosenbalm, Daniel (Author) / Zapata, Claudia (Thesis advisor) / Witczak, Matthew (Committee member) / Kaloush, Kamil (Committee member) / Arizona State University (Publisher)
Created2011
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 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.
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