Bayesian Parameter Identification and Model Selection for Normalized Modulus Reduction Curves of Soils

This work develops a procedure that involves the use of Bayesian approach to quantify data scatterness, estimates the optimal values of model parameters, and selects the most appropriate model for the construction of normalized modulus reduction curves of soils. The proposed procedure is then demonstrated using real observation data based on a set of comprehensive resonant column tests on coarse-grained soils conducted in the study.