Applications of importance sampling based on Kriging metamodel in structural reliability analysis

The early surrogate-based reliability analysis could not quantify the error on account of the substitution, and the existing variance reduction techniques remained time-consuming when the performance function involved the output of an expensive-to-evaluate black box function. For these problems, an approach of importance sampling based on Kriging metamodel to compute the failure probability was proposed. An initial surrogate for the performance function was established and then updated to specified precision based on active learning function. An augmented probability of failure was calculated, and a quasi-optimal importance sampling density function was devised. Thus, the samples used to estimate the correction term were acquired through Markov Chain Monte Carlo (MCMC). Eventually the probability of failure was obtained as an augmented probability of failure and correction term. The applications in various reliability problems showed that the proposed approach was efficient, robust and accurate. © 2016, Editorial Department of CIMS. All right reserved.