Abstract
Resilient modulus is a fundamental material property used for pavement materials characterization. A new methodology for predicting the resilient modulus of unbound/stabilized pavement materials and subgrade soils is developed based on a master surface function at a reference water content. The resilient modulus measurements at different levels of water content are shifted horizontally with respect to the octahedral shear stress, and bulk normal stress; simultaneously. A total of 2754 resilient modulus laboratory measurements, obtained from literature, for five granular base materials, four subgrade soils, one recycled crushed concrete material, and three cement-treated stabilized fine materials are used to evaluate the proposed Master Surface model for the resilient modulus prediction as a function of the stress state, and water content. The proposed predictive methodology is compared to well-known models from literature. The comparison of the investigated models exhibits that the Master Surface model has the most precise and unbiased predictions after numerically optimizing the resilient modulus data.
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