Identification of material parameters for inelastic constitutive models: Stochastic simulations for the analysis of deviations

Abstract

For parameter identification a distance function between the measured and the simulated data has to be minimized. Therefore, the influence of three different norms used in the definition of such a distance function is investigated. The nonlinear optimization problem is solved using a modified random search algorithm originally proposed by Price (1978). Next a stochastic model for the generation of artificial test data is presented. This model is used for a stochastic simulation of test data (constant strain rate tension with relaxation and creep). From these artificial data the material parameters of the model of Chan, Bodner and Lindholm are identified. To measure the quality of the identified material parameters their mean values and empirical standard deviations are computed. Furthermore, the coefficients of the empirical correlation matrix for the material parameters are computed. The model responses for tensile tests with the parameter vector generated from all tests and with the estimated parameters (from stochastic simulations) differ not considerably. However, for the creep tests the different parameter estimations lead to quite different model responses.