A novel reliability updating based method for efficient estimation of failure-probability global sensitivity

Failure-probability (FP) global sensitivity (FP-GS) can measure the average effect of random input on FP, and it is significant in reliability-based design optimization. The key of FP-GS is estimating the conditional FPs on the different realizations of random inputs, which usually requires a time-demanding double-loop structure analysis. This paper originally discovers a reliability updating perspective to efficiently estimate FP-GS, in which all required conditional FPs can be approximated by the posterior FPs based on reliability updating strategy, and the double-loop structure is avoided in estimating the conditional FPs required by FP-GS. In the proposed novel reliability updating based FP-GS analysis method, all conditional FPs required by FP-GS are derived with the likelihood function on the given quasi observations, and they can be simultaneously estimated by a single random input sample set for analyzing the unconditional FP. To reduce the computational cost further, adaptive Kriging model is updated to replace the performance function for efficiently estimating the unconditional FP and all conditional FPs required by FP-GS. Several examples are presented to verify the efficiency and accuracy of the proposed novel reliability updating method for estimating the FP-GS.