A Bayesian Surrogate Constitutive Model to Estimate Failure Probability of Rubber-Like Materials Aref Ghaderia,b, Vahid Morovatia,c, Roozbeh Dargazany1a,d aDepartment of Civil and Environmental Engineering, Michigan State University
[email protected] [email protected] [email protected] Abstract In this study, a stochastic constitutive modeling approach for elastomeric materials is developed to consider uncer- tainty in material behavior and its prediction. This effort leads to a demonstration of the deterministic approaches error compared to probabilistic approaches in order to calculate the probability of failure. First, the Bayesian lin- ear regression model calibration approach is employed for the Carroll model representing a hyperelastic constitutive model. The developed model is calibrated based on the Maximum Likelihood Estimation (MLE) and Maximum a Priori (MAP) estimation. Next, a Gaussian process (GP) as a non-parametric approach is utilized to estimate the probabilistic behavior of elastomeric materials. In this approach, hyper-parameters of the radial basis kernel in GP are calculated using L-BFGS method. To demonstrate model calibration and uncertainty propagation, these approaches are conducted on two experimental data sets for silicon-based and polyurethane-based adhesives, with four samples from each material. These uncertainties stem from model, measurement, to name but a few. Finally, failure proba- bility calculation analysis is conducted with First Order Reliability Method (FORM) analysis and Crude Monte Carlo (CMC) simulation for these data sets by creating a limit state function based on the stochastic constitutive model at failure stretch. Furthermore, sensitivity analysis is used to show the importance of each parameter of the probability of failure.