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Reliability measure approach considering mixture uncertainties under insufficient input data
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Zhen-yu LIU, Yu-feng LYU, Guo-dong SA, Jian-rong TAN. Reliability measure approach considering mixture uncertainties under insufficient input data[J]. Journal of Zhejiang University Science A, 1998, -1(-1): .
@article{title="Reliability measure approach considering mixture uncertainties under insufficient input data",
author="Zhen-yu LIU, Yu-feng LYU, Guo-dong SA, Jian-rong TAN",
journal="Journal of Zhejiang University Science A",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A2200300"
}
%0 Journal Article
%T Reliability measure approach considering mixture uncertainties under insufficient input data
%A Zhen-yu LIU
%A Yu-feng LYU
%A Guo-dong SA
%A Jian-rong TAN
%J Journal of Zhejiang University SCIENCE A
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%@ 1673-565X
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2200300
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T1 - Reliability measure approach considering mixture uncertainties under insufficient input data
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A1 - Yu-feng LYU
A1 - Guo-dong SA
A1 - Jian-rong TAN
J0 - Journal of Zhejiang University Science A
VL - -1
IS - -1
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%@ 1673-565X
Y1 - 1998
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A2200300
Abstract: 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.
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