Making importance sampling (IS) method suitable for sensitivity analysis of reliability parameter sensitivity

IS method is known to be fast for sensitivity analysis of reliability parameters but unfortunately it cannot be used for such analysis because it does not satisfy a necessary condition required by such analysis. We propose combining IS method with Markov chain simulation in such a way that IS-Markov method can be used for sensitivity analysis of reliability parameters. In the full paper, we explain our IS-Markov method in detail; in this abstract, we just add some pertinent remarks to listing the three topics of explanation: (1) the basic line of thinking of reliability sensitivity analysis, (2) the method of transforming importance sampling conditional samples into conditional samples, (3) obtaining approximate limit state function by weighted linear regression analysis; under topic 1, we point out that the reliability parameter sensitivities can be obtained with eqs.(4) and (5) in the full paper taken from the open literature; under topic 2, we explain how to combine Markov chain simulation with IS method; also under topic 2, we list the four steps given by S. K. Au in Ref. 5 for transforming importance sampling conditional samples into conditional samples; under topic 3, we use the conditional samples obtained in topic 2 to obtain by weighted linear regression analysis the approximate limit state function, which is needed in calculating sensitivities with eqs.(4) and (5). To illustrate our IS-Markov method, we give three numerical examples, whose sensitivity results calculated by several different methods, including IS-Markov method, are summarized respectively in Tables 1, 2, and 4 in the full paper. These results show preliminarily that, by comparison, IS-Markov method is remarkably high in efficiency.