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On-line Access: 2023-02-24

Received: 2022-06-06

Revision Accepted: 2022-09-20

Crosschecked: 2023-02-24

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Journal of Zhejiang University SCIENCE A 2023 Vol.24 No.2 P.146-161


Reliability measure approach considering mixture uncertainties under insufficient input data

Author(s):  Zhenyu LIU, Yufeng LYU, Guodong SA, Jianrong TAN

Affiliation(s):  State Key Laboratory of CAD&CG, Zhejiang University, Hangzhou 310058, China; more

Corresponding email(s):   sgd@zju.edu.cn

Key Words:  Insufficient data, Reliability index, Sensitivity analysis, Sparse variable, Uncertainty propagation

Zhenyu LIU, Yufeng LYU, Guodong SA, Jianrong TAN. Reliability measure approach considering mixture uncertainties under insufficient input data[J]. Journal of Zhejiang University Science A, 2023, 24(2): 146-161.

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journal="Journal of Zhejiang University Science A",
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%T Reliability measure approach considering mixture uncertainties under insufficient input data
%A Zhenyu LIU
%A Yufeng LYU
%A Guodong SA
%A Jianrong TAN
%J Journal of Zhejiang University SCIENCE A
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2200300

T1 - Reliability measure approach considering mixture uncertainties under insufficient input data
A1 - Zhenyu LIU
A1 - Yufeng LYU
A1 - Guodong SA
A1 - Jianrong TAN
J0 - Journal of Zhejiang University Science A
VL - 24
IS - 2
SP - 146
EP - 161
%@ 1673-565X
Y1 - 2023
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A2200300

Reliability analysis and reliability-based optimization design require accurate measurement of failure probability under input uncertainties. A unified probabilistic reliability measure approach is proposed to calculate the probability of failure and sensitivity indices considering a mixture of uncertainties under insufficient input data. The input uncertainty variables are classified into statistical variables, sparse variables, and interval variables. The conservativeness level of the failure probability is calculated through uncertainty propagation analysis of distribution parameters of sparse variables and auxiliary parameters of interval variables. The design sensitivity of the conservativeness level of the failure probability at design points is derived using a semi-analysis and sampling-based method. The proposed unified reliability measure method is extended to consider p-box variables, multi-domain variables, and evidence theory variables. Numerical and engineering examples demonstrate the effectiveness of the proposed method, which can obtain an accurate confidence level of reliability index and sensitivity indices with lower function evaluation number.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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