Selecting parameters to optimize in model calibration by inverse analysis

A study evaluating the benefits of using inverse analysis techniques to select the appropriate parameters to optimize when calibrating a soil constitutive model is presented. The factors that affect proper calibration are discussed with reference to the optimization of the elasto-plastic Hardening-Soil model for four layers of Chicago glacial clays. The models are initially calibrated using results from triaxial compression tests performed on specimens from four clay layers and subsequently re-calibrated using inclinometer data that recorded the displacements of a supported excavation in these clays. Finite element simulations of both the triaxial tests and the supported excavation are performed. A parameter optimization algorithm is used to fit the computed results and observed data, expressed in the form of stress-strain curves and inclinometer readings, respectively. A procedure is presented which uses the results of sensitivity analyses conducted on the soil model parameters for the identification of the relevant and uncorrelated parameters to calibrate. In both cases the inverse analysis methodology effectively calibrates the soil parameters considered, which numerically converge to realistic values that minimize the errors between computed responses and experimental observations.