Evaluation of Resilient Modulus Prediction Models for Saturated Pavement Base Aggregates

Given the increased frequency of heavy rainfall, there is a high probability that pavement base aggregates in the field, particularly in coastal regions, will become saturated and remain so for extended periods. The increase in moisture content can reduce the resilient modulus (M_R) of base materials. There are numerous constitutive models that help predict the resilient modulus of base aggregates based on moisture content and stress states, to assist pavement design. The performance of most of these models has been evaluated previously, but not at full saturation. This study is an evaluation of the two best performing prediction models, as determined in previous studies and current practices. The assessment was carried out using M_R test data for seven laboratory-saturated base aggregates with different properties and moisture sensitivities (high and low). The models were first calibrated based on M_R results at the optimum moisture content and then used to predict M_R of each base material at saturation. The accuracy of the prediction and the applicability to base aggregates with different moisture sensitivities formed the basis of the evaluation. The models were found to be more accurate and even conservative when predicting M_R of saturated base aggregates with low moisture susceptibility. However, the models were erroneous in their prediction and overestimated M_R at saturation of highly moisture susceptible pavement base aggregates. An adaptation solution was proposed to improve the prediction accuracy of the existing models. This study joins efforts to design our climate-vulnerable roadways more accurately and improve pavement resilience.