Importance sampling-based algorithms for efficiently estimating failure chance index under two-fold random uncertainty

Under condition of two-fold random uncertainty, a key for evaluating failure chance index (FCI) of structure system is estimating failure probability function (FPF) with respect to arbitrary realization of random distribution parameters. However, the estimation of FPF in small failure probability pr...

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Vydané v:Structural and multidisciplinary optimization Ročník 65; číslo 7
Hlavní autori: Li, Fen, Lu, Zhenzhou, Feng, Kaixuan, Jiang, Xia
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.07.2022
Springer Nature B.V
Predmet:
Algorithms
Computational Mathematics and Numerical Analysis
Conditional probability
Efficiency
Engineering
Engineering Design
Estimation
Failure
Importance sampling
Mathematical models
Parameters
Probability theory
Research Paper
Theoretical and Applied Mechanics
Uncertainty
Failure probability function
Reduction of candidate sample pool
Failure chance
Adaptive kriging
Random distribution parameters
Importance sampling
ISSN:1615-147X, 1615-1488
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Shrnutí:Under condition of two-fold random uncertainty, a key for evaluating failure chance index (FCI) of structure system is estimating failure probability function (FPF) with respect to arbitrary realization of random distribution parameters. However, the estimation of FPF in small failure probability problem remains a challenge. For addressing this heavy computational burden of estimating FCI, importance sampling-based algorithms are proposed in the paper. In the proposed algorithm, by standard normal transformation of random inputs in performance function, the random distribution parameters are firstly transformed into the performance function as arguments. And an augmented performance function is established in space spanned by random inputs and their random distribution parameters. Secondly, by using differential approximation and conditional probability theory, the proposed algorithms obtain the whole FPF by one numerical simulation of estimating the failure probability of the augmented performance function, which improves the efficiency of estimating FPF and FCI. Subsequently, combined with adaptive Kriging model and reduction of candidate sample pool, the proposed algorithms design importance sampling-based strategies for estimating FPF, which further improves the efficiency of estimating FPF and FCI. Finally, the accuracy and efficiency of the proposed algorithm are verified by numerical and engineering application examples.
Bibliografia:ObjectType-Article-1
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ISSN:1615-147X
1615-1488
DOI:10.1007/s00158-022-03286-x