Nonprobabilistic Credible Bayesian Reliability Model for Composite Structures Using Truncated Normal Priors

Compared to metallic materials, composites exhibit greater uncertainty, with some input variables characterized by limited and sparse data. Traditional nonprobabilistic methods provide only a qualitative range for uncertainty parameters, which is insufficient for quantifying credibility effectively and consequently increases the conservativeness of structural reliability analysis. To quantify uncertainty parameters in composites, a nonprobabilistic interval approach is employed to address the challenges posed by various failure modes and limited sample sizes. Based on Bayesian theory, a truncated normal distribution is utilized as the prior for the interval radius, with new sample points introduced to update this radius. The MCMC method is employed to numerically solve for the posterior distribution, thereby establishing a connection between the nonprobabilistic quantification sets and the credible levels. A nonprobabilistic credible Bayesian reliability model for composites is developed by integrating the Hashin failure criterion with Taylor series expansion for uncertainty propagation analysis. The effectiveness and feasibility of the proposed model are demonstrated through two relevant numerical examples.