Implementation of Sobol’s sensitivity analysis to cyclic plasticity model with parameter uncertainty

Abstract

Though modeling material cyclic plasticity in a probabilistic way can yield a more accurate result than deterministic modelling method, it is quite hard to perform uncertainty quantification for all the material parameters. In this paper, Sobol’s global sensitivity analysis is applied to cyclic plasticity model to investigate how the variation of these parameters affect fatigue reliability evaluation. A machine learning algorithm is proposed to improve the computational efficiency for failure probability assessment. The results of global sensitivity analysis show that fatigue damage parameters, elastic modulus and initial yield stress are very influential to fatigue reliability analysis while others are not.