Optimization Techniques in Soil Constitutive Model Calibration

Abstract

Traditionally, constitutive model calibration from experimental data is based on the method of linear regression. However, not all constitutive model parameters can be obtained by the method of linear regression. Once these parameters are determined, numerical simulation such as finite element or finite difference analyses can be carried out accordingly. This research used the numerical optimization techniques including DIRECT Optimization Algorithm, Nonlinear Least Squares Method and Genetic Algorithm to evaluate the applicability to constitutive model calibration. The objective function is defined by the distance between the measured and computed data. When a minimum value of the objective function is reached, the corresponding variables are the optimized model parameters. This research used four groups of experimental test results, which are soil triaxial compression tests, rock triaxial compression tests, rock pure shear tests and rock triaxial extension tests. Three constitutive models were used in this study including Duncan and Chang Model, Modified Cam Clay Model and Fuzzy Set Plasticity Model. Genetic Algorithm works effectively in all three constitutive models used in this study. DIRECT Optimization Algorithm works well in calibrating Modified Cam Clay Model and Duncan and Chang Model while Nonlinear Least Squares Method only works in Duncan and Chang Model. In conclusion, Genetic Algorithm works better than DIRECT Optimization Algorithm and Nonlinear Least Squares Method.