Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 111194 |
| Seitenumfang | 12 |
| Fachzeitschrift | Reliability Engineering and System Safety |
| Jahrgang | 262 |
| Frühes Online-Datum | 9 Mai 2025 |
| Publikationsstatus | Elektronisch veröffentlicht (E-Pub) - 9 Mai 2025 |
Abstract
Active learning single-loop Kriging methods have gained significant attention for time-dependent reliability analysis. However, it still remains a challenge to estimate the time-dependent failure probability efficiently and accurately in practical engineering problems. This study proposes a new method, called ‘Error-informed Parallel Adaptive Kriging’ (EPAK) for efficient time-dependent reliability analysis. First, a sequential variance-amplified importance sampling technique is developed to estimate the time-dependent failure probability based on the trained global response Kriging model of the true performance function. Then, the maximum relative error of the time-dependent failure probability is derived to facilitate the construction of stopping criterion. Finally, a parallel sampling strategy is proposed through combining the relative error contribution and an influence function, which enables parallel computing and reduces the unnecessary limit state function evaluations caused by excessive clustering. The superior performance of the proposed method is validated through several examples. Numerical results demonstrate that the method can accurately estimate the time-dependent failure probability with higher efficiency than several compared methods.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Sicherheit, Risiko, Zuverlässigkeit und Qualität
- Ingenieurwesen (insg.)
- Wirtschaftsingenieurwesen und Fertigungstechnik
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in: Reliability Engineering and System Safety, Jahrgang 262, 111194, 10.2025.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Error-informed parallel adaptive Kriging method for time-dependent reliability analysis
AU - Hu, Zhuo
AU - Dang, Chao
AU - Wang, Da
AU - Beer, Michael
AU - Wang, Lei
N1 - Publisher Copyright: © 2025 Elsevier Ltd
PY - 2025/5/9
Y1 - 2025/5/9
N2 - Active learning single-loop Kriging methods have gained significant attention for time-dependent reliability analysis. However, it still remains a challenge to estimate the time-dependent failure probability efficiently and accurately in practical engineering problems. This study proposes a new method, called ‘Error-informed Parallel Adaptive Kriging’ (EPAK) for efficient time-dependent reliability analysis. First, a sequential variance-amplified importance sampling technique is developed to estimate the time-dependent failure probability based on the trained global response Kriging model of the true performance function. Then, the maximum relative error of the time-dependent failure probability is derived to facilitate the construction of stopping criterion. Finally, a parallel sampling strategy is proposed through combining the relative error contribution and an influence function, which enables parallel computing and reduces the unnecessary limit state function evaluations caused by excessive clustering. The superior performance of the proposed method is validated through several examples. Numerical results demonstrate that the method can accurately estimate the time-dependent failure probability with higher efficiency than several compared methods.
AB - Active learning single-loop Kriging methods have gained significant attention for time-dependent reliability analysis. However, it still remains a challenge to estimate the time-dependent failure probability efficiently and accurately in practical engineering problems. This study proposes a new method, called ‘Error-informed Parallel Adaptive Kriging’ (EPAK) for efficient time-dependent reliability analysis. First, a sequential variance-amplified importance sampling technique is developed to estimate the time-dependent failure probability based on the trained global response Kriging model of the true performance function. Then, the maximum relative error of the time-dependent failure probability is derived to facilitate the construction of stopping criterion. Finally, a parallel sampling strategy is proposed through combining the relative error contribution and an influence function, which enables parallel computing and reduces the unnecessary limit state function evaluations caused by excessive clustering. The superior performance of the proposed method is validated through several examples. Numerical results demonstrate that the method can accurately estimate the time-dependent failure probability with higher efficiency than several compared methods.
KW - Active learning
KW - Estimation error
KW - Importance sampling
KW - Kriging model
KW - Parallel computing
KW - Time-dependent reliability analysis
UR - http://www.scopus.com/inward/record.url?scp=105004798807&partnerID=8YFLogxK
U2 - 10.1016/j.ress.2025.111194
DO - 10.1016/j.ress.2025.111194
M3 - Article
AN - SCOPUS:105004798807
VL - 262
JO - Reliability Engineering and System Safety
JF - Reliability Engineering and System Safety
SN - 0951-8320
M1 - 111194
ER -