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

Abstract

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.

KEYWORDS:

• Failure probability-based global sensitivity analysis
• importance sampling method
• law of total variance
• space-partition
• multiple failure regions

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant number 12002237], the National Postdoctoral Program for Innovative Talents [grant number BX20190244], the Project funded by China Postdoctoral Science Foundation [number 2019M661610] and the National Science and Technology Major Project [grant number 2017-IV-0009-0046].