Calibration of a constitutive model using genetic algorithms

Before any constitutive model can be used in a numerical procedure, the model needs to be calibrated using laboratory test results. The traditional calibration techniques use stress and strain levels at certain states that a material undergoes during certain types of laboratory tests. Sometimes this method of calibrating a constitutive model fails to capture the overall behavior of a material, i.e. behavior at every point in stress/strain path. In this paper, we have shown how a random search technique, genetic algorithm (GA), can be used to calibrate constitutive models. The advantages of using GA are that it considers the overall behavior of a material, not the behavior at some specific states as the traditional method does, and it can work with many types of laboratory tests. The concept is applied to calibrate the hierarchical single surface (HiSS) δ_l model for geologic materials. Three cases have been studied where the difference is in the type of test data used to calibrate the model. These three different test data are: (1) simulated conventional test data, (2) simulated cyclic test data, and (3) real test data. A comparison of two different cross-over schemes, one-point and six-point, has been made.