Details
| Original language | English |
|---|---|
| Article number | 103387 |
| Journal | Probabilistic Engineering Mechanics |
| Volume | 71 |
| Early online date | 19 Nov 2022 |
| Publication status | Published - Jan 2023 |
Abstract
An efficient strategy to approximate the failure probability function in structural reliability problems is proposed. The failure probability function (FPF) is defined as the failure probability of the structure expressed as a function of the design parameters, which in this study are considered to be distribution parameters of random variables representing uncertain model quantities. The task of determining the FPF is commonly numerically demanding since repeated reliability analyses are required. The proposed strategy is based on the concept of augmented reliability analysis, which only requires a single run of a simulation-based reliability method. This paper introduces a new sample regeneration algorithm that allows to generate the required failure samples of design parameters without any additional evaluation of the structural response. In this way, efficiency is further improved while ensuring high accuracy in the estimation of the FPF. To illustrate the efficiency and effectiveness of the method, case studies involving a turbine disk and an aircraft inner flap are included in this study.
Keywords
- Bayesian theory, Failure probability function, Maximum Entropy method, Regeneration algorithm, Reliability
ASJC Scopus subject areas
- Physics and Astronomy(all)
- Statistical and Nonlinear Physics
- Engineering(all)
- Civil and Structural Engineering
- Energy(all)
- Nuclear Energy and Engineering
- Physics and Astronomy(all)
- Condensed Matter Physics
- Engineering(all)
- Aerospace Engineering
- Engineering(all)
- Ocean Engineering
- Engineering(all)
- Mechanical Engineering
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In: Probabilistic Engineering Mechanics, Vol. 71, 103387, 01.2023.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Sample regeneration algorithm for structural failure probability function estimation
AU - Yuan, Xiukai
AU - Wang, Shanglong
AU - Valdebenito, Marcos A.
AU - Faes, Matthias G.R.
AU - Beer, Michael
N1 - Funding Information: The authors would like to acknowledge financial support from the Aeronautical Science Foundation of China (Grant No. ASFC-20170968002 ).
PY - 2023/1
Y1 - 2023/1
N2 - An efficient strategy to approximate the failure probability function in structural reliability problems is proposed. The failure probability function (FPF) is defined as the failure probability of the structure expressed as a function of the design parameters, which in this study are considered to be distribution parameters of random variables representing uncertain model quantities. The task of determining the FPF is commonly numerically demanding since repeated reliability analyses are required. The proposed strategy is based on the concept of augmented reliability analysis, which only requires a single run of a simulation-based reliability method. This paper introduces a new sample regeneration algorithm that allows to generate the required failure samples of design parameters without any additional evaluation of the structural response. In this way, efficiency is further improved while ensuring high accuracy in the estimation of the FPF. To illustrate the efficiency and effectiveness of the method, case studies involving a turbine disk and an aircraft inner flap are included in this study.
AB - An efficient strategy to approximate the failure probability function in structural reliability problems is proposed. The failure probability function (FPF) is defined as the failure probability of the structure expressed as a function of the design parameters, which in this study are considered to be distribution parameters of random variables representing uncertain model quantities. The task of determining the FPF is commonly numerically demanding since repeated reliability analyses are required. The proposed strategy is based on the concept of augmented reliability analysis, which only requires a single run of a simulation-based reliability method. This paper introduces a new sample regeneration algorithm that allows to generate the required failure samples of design parameters without any additional evaluation of the structural response. In this way, efficiency is further improved while ensuring high accuracy in the estimation of the FPF. To illustrate the efficiency and effectiveness of the method, case studies involving a turbine disk and an aircraft inner flap are included in this study.
KW - Bayesian theory
KW - Failure probability function
KW - Maximum Entropy method
KW - Regeneration algorithm
KW - Reliability
UR - http://www.scopus.com/inward/record.url?scp=85143307850&partnerID=8YFLogxK
U2 - 10.1016/j.probengmech.2022.103387
DO - 10.1016/j.probengmech.2022.103387
M3 - Article
AN - SCOPUS:85143307850
VL - 71
JO - Probabilistic Engineering Mechanics
JF - Probabilistic Engineering Mechanics
SN - 0266-8920
M1 - 103387
ER -