  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.
