Details
| Original language | English |
|---|---|
| Article number | 117105 |
| Number of pages | 19 |
| Journal | Computer Methods in Applied Mechanics and Engineering |
| Volume | 428 |
| Early online date | 7 Jun 2024 |
| Publication status | Published - 1 Aug 2024 |
Abstract
Estimating the small failure probability is a crucial task in structural engineering, and directional sampling has long been recognized as one of the most promising stochastic simulation method for problems with multiple disconnected failure domains. However, when applied to problems with expensive-to-evaluate and highly nonlinear limit state functions, it is still less satisfactory in terms of numerical accuracy and efficiency. To fill this gap, this paper develops a directional filter equipped active learning (DirFAL) algorithm. It integrates three key components: (1) an active learning directional filter scheme, which plays as a role for adaptively prioritizing the directional samples that dominantly contribute to the failure probability; (2) a tailored closed-form expression of acquisition function, which is newly defined and incorporated into DirFAL to effectively select new training data for enriching a Gaussian process regression surrogate model; (3) an active learning stratification strategy for tackling the performance degradation issue when applied to problems with relatively high dimension and extremely small failure probability. The performance of the proposed methods is ultimately demonstrated through two numerical examples and two engineering problems, and results show that they are of superiority over other parallel methods in terms of numerical efficiency given required accuracy.
Keywords
- Active learning, Directional filter, Gaussian process regression, Most probable point, Rare failure event
ASJC Scopus subject areas
- Engineering(all)
- Computational Mechanics
- Engineering(all)
- Mechanics of Materials
- Engineering(all)
- Mechanical Engineering
- Physics and Astronomy(all)
- Computer Science(all)
- Computer Science Applications
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
In: Computer Methods in Applied Mechanics and Engineering, Vol. 428, 117105, 01.08.2024.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Directional filter combined with active learning for rare failure events
AU - Song, Jingwen
AU - Cui, Yifan
AU - Wei, Pengfei
AU - Rashki, Mohsen
AU - Zhang, Weihong
AU - Beer, Michael
N1 - Publisher Copyright: © 2024 Elsevier B.V.
PY - 2024/8/1
Y1 - 2024/8/1
N2 - Estimating the small failure probability is a crucial task in structural engineering, and directional sampling has long been recognized as one of the most promising stochastic simulation method for problems with multiple disconnected failure domains. However, when applied to problems with expensive-to-evaluate and highly nonlinear limit state functions, it is still less satisfactory in terms of numerical accuracy and efficiency. To fill this gap, this paper develops a directional filter equipped active learning (DirFAL) algorithm. It integrates three key components: (1) an active learning directional filter scheme, which plays as a role for adaptively prioritizing the directional samples that dominantly contribute to the failure probability; (2) a tailored closed-form expression of acquisition function, which is newly defined and incorporated into DirFAL to effectively select new training data for enriching a Gaussian process regression surrogate model; (3) an active learning stratification strategy for tackling the performance degradation issue when applied to problems with relatively high dimension and extremely small failure probability. The performance of the proposed methods is ultimately demonstrated through two numerical examples and two engineering problems, and results show that they are of superiority over other parallel methods in terms of numerical efficiency given required accuracy.
AB - Estimating the small failure probability is a crucial task in structural engineering, and directional sampling has long been recognized as one of the most promising stochastic simulation method for problems with multiple disconnected failure domains. However, when applied to problems with expensive-to-evaluate and highly nonlinear limit state functions, it is still less satisfactory in terms of numerical accuracy and efficiency. To fill this gap, this paper develops a directional filter equipped active learning (DirFAL) algorithm. It integrates three key components: (1) an active learning directional filter scheme, which plays as a role for adaptively prioritizing the directional samples that dominantly contribute to the failure probability; (2) a tailored closed-form expression of acquisition function, which is newly defined and incorporated into DirFAL to effectively select new training data for enriching a Gaussian process regression surrogate model; (3) an active learning stratification strategy for tackling the performance degradation issue when applied to problems with relatively high dimension and extremely small failure probability. The performance of the proposed methods is ultimately demonstrated through two numerical examples and two engineering problems, and results show that they are of superiority over other parallel methods in terms of numerical efficiency given required accuracy.
KW - Active learning
KW - Directional filter
KW - Gaussian process regression
KW - Most probable point
KW - Rare failure event
UR - http://www.scopus.com/inward/record.url?scp=85195212268&partnerID=8YFLogxK
U2 - 10.1016/j.cma.2024.117105
DO - 10.1016/j.cma.2024.117105
M3 - Article
AN - SCOPUS:85195212268
VL - 428
JO - Computer Methods in Applied Mechanics and Engineering
JF - Computer Methods in Applied Mechanics and Engineering
SN - 0045-7825
M1 - 117105
ER -