An efficient dimensionality-independent algorithm for failure probability-based global sensitivity analysis by dual-stage adaptive kriging model

The failure probability-based global sensitivity index (FPGSI) analyses how the model inputs affect the failure probability of a model. It is useful for guiding reliability-based design optimization and enhancing reliability by controlling the uncertainty of the important input variables. Based on the law of total variance in successive intervals without overlapping and the dual-stage adaptive kriging (AK) model-based importance sampling (IS) method, an efficient dimensionality-independent method is proposed. First, an interval-conditional failure probability-based formula is established. Secondly, a dual-stage AK model is embedded into the formula to construct the IS probability density function and identify the state (failed or safe) of every IS sample. Thirdly, using different partitions of IS samples, all inputs’ FPGSIs can be simultaneously obtained by taking the corresponding subdomains’ samples into the proposed computational formula. The results of four case studies illustrate the effectiveness of the proposed algorithm, especially for cases with multiple failure regions.