Reliability associated with the estimation of soil resilient modulus at different hierarchical levels of pavement design

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.