Surrogate Model Validation and Verification for Random Failure Analyses of Composites

In the present chapter, multivariate adaptive regression splines (MARS) is explored as a surrogate model in conjunction to Monte Carlo simulation (MCS) to analyse the random first-ply failure loads of graphite–epoxy laminated composite plates. The five failure criteria, namely maximum strain theory, maximum stress theory, Tsai–Hill theory, Tsai–Wu theory, and Hoffman theory, are considered. The numerical validation of deterministic failure load is presented first. Thereafter, a concise investigation is carried out to examine the capability of MARS model for efficiently predicting the first-ply failure loads. Comparative results are presented using scatter plots and probability density function plots to access the prediction capability with respect to direct MCS. The current results portray the successful application of MARS as the surrogate model to achieve computational efficiency and analyse the first-ply failure loads.