Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 102351 |
| Fachzeitschrift | Structural safety |
| Jahrgang | 104 |
| Frühes Online-Datum | 16 Mai 2023 |
| Publikationsstatus | Veröffentlicht - Sept. 2023 |
Abstract
Line sampling has been demonstrated to be a promising simulation method for structural reliability analysis, especially for assessing small failure probabilities. However, its practical performance can still be significantly improved by taking advantage of, for example, Bayesian active learning. Along this direction, a recently proposed ‘partially Bayesian active learning line sampling’ (PBAL-LS) method has shown to be successful. This paper aims at offering a more complete Bayesian active learning treatment of line sampling, resulting in a new method called ‘Bayesian active learning line sampling’ (BAL-LS). Specifically, we derive the exact posterior variance of the failure probability, which can measure our epistemic uncertainty about the failure probability more precisely than the upper bound given in PBAL-LS. Further, two essential components (i.e., learning function and stopping criterion) are proposed to facilitate Bayesian active learning, based on the uncertainty representation of the failure probability. In addition, the important direction can be automatically updated throughout the simulation, as one advantage directly inherited from PBAL-LS. The performance of BAL-LS is illustrated by four numerical examples. It is shown that the proposed method is capable of evaluating extremely small failure probabilities with desired efficiency and accuracy.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Tief- und Ingenieurbau
- Ingenieurwesen (insg.)
- Bauwesen
- Ingenieurwesen (insg.)
- Sicherheit, Risiko, Zuverlässigkeit und Qualität
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
in: Structural safety, Jahrgang 104, 102351, 09.2023.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Structural reliability analysis by line sampling
T2 - A Bayesian active learning treatment
AU - Dang, Chao
AU - Valdebenito, Marcos A.
AU - Faes, Matthias G.R.
AU - Song, Jingwen
AU - Wei, Pengfei
AU - Beer, Michael
N1 - Publisher Copyright: © 2023 Elsevier Ltd
PY - 2023/9
Y1 - 2023/9
N2 - Line sampling has been demonstrated to be a promising simulation method for structural reliability analysis, especially for assessing small failure probabilities. However, its practical performance can still be significantly improved by taking advantage of, for example, Bayesian active learning. Along this direction, a recently proposed ‘partially Bayesian active learning line sampling’ (PBAL-LS) method has shown to be successful. This paper aims at offering a more complete Bayesian active learning treatment of line sampling, resulting in a new method called ‘Bayesian active learning line sampling’ (BAL-LS). Specifically, we derive the exact posterior variance of the failure probability, which can measure our epistemic uncertainty about the failure probability more precisely than the upper bound given in PBAL-LS. Further, two essential components (i.e., learning function and stopping criterion) are proposed to facilitate Bayesian active learning, based on the uncertainty representation of the failure probability. In addition, the important direction can be automatically updated throughout the simulation, as one advantage directly inherited from PBAL-LS. The performance of BAL-LS is illustrated by four numerical examples. It is shown that the proposed method is capable of evaluating extremely small failure probabilities with desired efficiency and accuracy.
AB - Line sampling has been demonstrated to be a promising simulation method for structural reliability analysis, especially for assessing small failure probabilities. However, its practical performance can still be significantly improved by taking advantage of, for example, Bayesian active learning. Along this direction, a recently proposed ‘partially Bayesian active learning line sampling’ (PBAL-LS) method has shown to be successful. This paper aims at offering a more complete Bayesian active learning treatment of line sampling, resulting in a new method called ‘Bayesian active learning line sampling’ (BAL-LS). Specifically, we derive the exact posterior variance of the failure probability, which can measure our epistemic uncertainty about the failure probability more precisely than the upper bound given in PBAL-LS. Further, two essential components (i.e., learning function and stopping criterion) are proposed to facilitate Bayesian active learning, based on the uncertainty representation of the failure probability. In addition, the important direction can be automatically updated throughout the simulation, as one advantage directly inherited from PBAL-LS. The performance of BAL-LS is illustrated by four numerical examples. It is shown that the proposed method is capable of evaluating extremely small failure probabilities with desired efficiency and accuracy.
KW - Bayesian active learning
KW - Bayesian inference
KW - Gaussian process
KW - Line sampling
KW - Structural reliability analysis
UR - http://www.scopus.com/inward/record.url?scp=85159196233&partnerID=8YFLogxK
U2 - 10.1016/j.strusafe.2023.102351
DO - 10.1016/j.strusafe.2023.102351
M3 - Article
AN - SCOPUS:85159196233
VL - 104
JO - Structural safety
JF - Structural safety
SN - 0167-4730
M1 - 102351
ER -