Global reliability sensitivity estimation based on failure samples. (November 2019)

• Record Type: Journal Article
• Authors: Li, Luyi; Papaioannou, Iason; Straub, Daniel
• Is Part Of: Structural safety. Volume 81(2019)
• Publication Date: 2019-11
• Subjects: Global reliability sensitivity analysis -- Monte Carlo methods -- Bayes' rule -- Kernel density estimation; Structural stability -- Periodicals; Safety factor in engineering -- Periodicals; Reliability (Engineering) -- Periodicals; Constructions -- Stabilité -- Périodiques; Coefficient de sécurité en ingénierie -- Périodiques; Fiabilité -- Périodiques; 620.86
• DOI: 10.1016/j.strusafe.2019.101871
• Languages: English
• ISSNs: 0167-4730

Abstract

Highlights: A new method for performing global reliability sensitivity analysis is proposed. It requires as an input only samples that fall in the failure domain. It can be carried out without extra model evaluations following reliability analysis. It can be implemented with any sampling-based reliability analysis method. It is much more efficient than currently existing methods. Abstract: Global reliability sensitivity analysis (RSA) can help to assess the effects of input random variables X on the probability of failure Pr( F ) of an engineering system. Conventionally, this requires repeated evaluations of the conditional failure probability Pr( F | Xi = xi ) for multiple values of the input random variable Xi and for all Xi of interest. Such a solution is straightforward but computationally expensive. In this paper, we propose a new method to perform global RSA, which requires as an input only samples of X that fall in the failure domain. Such samples are a by-product of many sampling-based reliability analysis methods. The proposed method constructs the Pr( F | Xi = xi ) by application of Bayes' rule, based on the probability density function (PDF) of X conditioned on system failure F . This conditional PDF is approximated with a kernel density estimation from the failure samples. In this way, the reliability sensitivities of all the input random variables can be computed following a sampling-based reliability analysis with no additional computation cost. The approach is investigated on numerical examples in conjunction with crude Monte Carlo simulation, importance sampling and subset simulation. The results demonstrate the computational advantages over existing single-loop sampling methods for global RSA.