Details
| Original language | English |
|---|---|
| Article number | 102104 |
| Journal | Structural Safety |
| Volume | 92 |
| Early online date | 24 May 2021 |
| Publication status | Published - Sept 2021 |
Abstract
An efficient procedure is proposed to estimate the failure probability function (FPF) with respect to design variables, which correspond to distribution parameters of basic structural random variables. The proposed procedure is based on the concept of an augmented reliability problem, which assumes the design variables as uncertain by assigning a prior distribution, transforming the FPF into an expression that includes the posterior distribution of those design variables. The novel contribution of this work consists of expressing this target posterior distribution as an integral, allowing it to be estimated by means of sampling, and no distribution fitting is needed, leading to an efficient estimation of FPF. The proposed procedure is implemented within three different simulation strategies: Monte Carlo simulation, importance sampling and subset simulation; for each of these cases, expressions for the coefficient of variation of the FPF estimate are derived. Numerical examples illustrate performance of the proposed approaches.
Keywords
- Bayesian theory, Failure probability function, Reliability analysis, Reliability-based optimization
ASJC Scopus subject areas
- Engineering(all)
- Civil and Structural Engineering
- Engineering(all)
- Building and Construction
- Engineering(all)
- Safety, Risk, Reliability and Quality
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In: Structural Safety, Vol. 92, 102104, 09.2021.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Efficient procedure for failure probability function estimation in augmented space
AU - Yuan, Xiukai
AU - Liu, Shaolong
AU - Valdebenito, M. A.
AU - Gu, Jian
AU - Beer, Michael
N1 - Funding Information: The authors would like to acknowledge financial support from NSAF (Grant No. U1530122 ), the Aeronautical Science Foundation of China (Grant No. ASFC-20170968002 ), the Fundamental Research Funds for the Central Universities of China ( XMU-20720180072 ) and ANID (National Agency for Research and Development, Chile) under its program FONDECYT , grant number 1180271 .
PY - 2021/9
Y1 - 2021/9
N2 - An efficient procedure is proposed to estimate the failure probability function (FPF) with respect to design variables, which correspond to distribution parameters of basic structural random variables. The proposed procedure is based on the concept of an augmented reliability problem, which assumes the design variables as uncertain by assigning a prior distribution, transforming the FPF into an expression that includes the posterior distribution of those design variables. The novel contribution of this work consists of expressing this target posterior distribution as an integral, allowing it to be estimated by means of sampling, and no distribution fitting is needed, leading to an efficient estimation of FPF. The proposed procedure is implemented within three different simulation strategies: Monte Carlo simulation, importance sampling and subset simulation; for each of these cases, expressions for the coefficient of variation of the FPF estimate are derived. Numerical examples illustrate performance of the proposed approaches.
AB - An efficient procedure is proposed to estimate the failure probability function (FPF) with respect to design variables, which correspond to distribution parameters of basic structural random variables. The proposed procedure is based on the concept of an augmented reliability problem, which assumes the design variables as uncertain by assigning a prior distribution, transforming the FPF into an expression that includes the posterior distribution of those design variables. The novel contribution of this work consists of expressing this target posterior distribution as an integral, allowing it to be estimated by means of sampling, and no distribution fitting is needed, leading to an efficient estimation of FPF. The proposed procedure is implemented within three different simulation strategies: Monte Carlo simulation, importance sampling and subset simulation; for each of these cases, expressions for the coefficient of variation of the FPF estimate are derived. Numerical examples illustrate performance of the proposed approaches.
KW - Bayesian theory
KW - Failure probability function
KW - Reliability analysis
KW - Reliability-based optimization
UR - http://www.scopus.com/inward/record.url?scp=85104959492&partnerID=8YFLogxK
U2 - 10.1016/j.strusafe.2021.102104
DO - 10.1016/j.strusafe.2021.102104
M3 - Article
AN - SCOPUS:85104959492
VL - 92
JO - Structural Safety
JF - Structural Safety
SN - 0167-4730
M1 - 102104
ER -