An efficient surrogate-aided importance sampling framework for reliability analysis

Abstract

Surrogates in lieu of expensive-to-evaluate performance functions can accelerate the reliability analysis greatly. This paper proposes a new two-stage framework for surrogate-aided reliability analysis named Surrogates for Importance Sampling (S4IS). In the first stage, a coarse surrogate is built to gain the information about failure regions; the second stage zooms into the important regions and improves the accuracy of the failure probability estimator by adaptively selecting support points therein. The learning functions are proposed to guide the selection of support points such that the exploration and exploitation can be dynamically balanced. As a generic framework, S4IS has the potential to incorporate different types of surrogates (Gaussian Processes, Support Vector Machines, Neural Network, etc.). The effectiveness and efficiency of S4IS is validated by five illustrative examples, which involve system reliability, highly nonlinear limit-state function, small failure probability and moderately high dimensionality. The implementation of S4IS is made available to download at https://github.com/RobinSeaside/S4IS.