An efficient global reliability sensitivity analysis algorithm based on classification of model output and subset simulation. (September 2018)

• Record Type: Journal Article
• Authors: Yun, Wanying; Lu, Zhenzhou; Zhang, Yu; Jiang, Xian
• Is Part Of: Structural safety. Volume 74(2018)
• Page Start: 49
• Page End: 57
• Publication Date: 2018-09
• Subjects: Global reliability sensitivity analysis -- Bayes formula -- Metropolis-Hastings algorithm -- First four-order conditional moments -- Edgeworth expansion -- Dimensional-independency; 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.2018.04.003
• Languages: English
• ISSNs: 0167-4730

Abstract

Highlights: An equivalent form of global reliability sensitivity index is derived. The proposed form is based on classification of model output. Subset simulation method is extended to estimate this equivalent form. The proposed algorithm is independent of the dimensionality of model inputs. Edgeworth expansion is employed to estimate the probability density function. Abstract: The global reliability sensitivity analysis measures the effect of each model input variable on the failure probability, which is very useful for reliability-based optimization design. The aim of this paper is to propose an alternative method to estimate the global reliability sensitivity indices by one group of model input–output samples. Firstly, Bayes formula is used to convert the original expression of global reliability sensitivity index into an equivalent form where only the unconditional failure probability and the failure-conditional probability density function (PDF) of each model input variable are required. All global reliability sensitivity indices can be simultaneously estimated by this new equivalent form, and the computational cost of the process is independent of the dimensionality of model input variables. Secondly, to improve the efficiency of sampling which aims at calculating the unconditional failure probability and estimating the failure-conditional PDF of every model input simultaneously, subset simulation method is extended to achieve these two aims. In the proposed procedure, subset simulation is used to estimate the unconditional failure probability, and Metropolis-Hastings algorithm is employed to convert the samples in failure domain from the current PDF in subset simulation to the PDF corresponding to the original PDF of model inputs for estimating the failure-conditional PDF of each model input variable. Thirdly, Edgeworth expansion is employed to approximate the failure-conditional PDF of each model input variable. Finally, the global reliability sensitivity index can be easily computed as byproducts using the unconditional failure probability and the failure-conditional PDF of each model input in failure probability analysis, and this process does not need any extra model evaluations after the unconditional failure probability analysis is completed by subset simulation. A headless rivet model, a roof truss structure and a composite cantilever beam structure are analyzed, and the results demonstrate the effectiveness of the proposed method in global reliability sensitivity analysis.