Details
| Original language | English |
|---|---|
| Article number | 107022 |
| Number of pages | 11 |
| Journal | Computers and geotechnics |
| Volume | 179 |
| Early online date | 28 Dec 2024 |
| Publication status | Published - Mar 2025 |
Abstract
Evaluating the reliability of slopes with spatial variability is a challenging issue, especially when the failure probably of the target event is at a low level, because of unaffordable computational cost required in such cases. In this context, an adaptive surrogate model-based approach, namely active learning-assisted bootstrap polynomial chaos expansion, is proposed to alleviate the above computational burden. The proposed approach extends the traditional polynomial chaos expansion by introducing the bootstrap resampling method so that it can deal with reliability issues smoothly and provide a feasible configuration environment to support the active learning algorithm. The computational efficiency can thus be greatly improved by adaptively searching for the most informative samples to train the surrogate model through iterative program. Two spatially varying soil slopes are studied to illustrate the validity of the active learning-assisted bootstrap polynomial chaos expansion. The results show that the proposed approach has superior advantages in terms of efficiency and accuracy, and it is also suitable for handling problems with complex parameter configurations, including high dimensionality and cross-correlation. Besides, the proposed approach has potential in addressing geotechnical engineering problems with low probability levels.
Keywords
- Active learning algorithm, Bootstrap polynomial chaos expansion, Spatial variability, Surrogate model
ASJC Scopus subject areas
- Earth and Planetary Sciences(all)
- Geotechnical Engineering and Engineering Geology
- Computer Science(all)
- Computer Science Applications
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In: Computers and geotechnics, Vol. 179, 107022, 03.2025.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Efficient reliability analysis of slopes in spatially variable soils with active learning-assisted bootstrap polynomial chaos expansion
AU - Liao, Kang
AU - Zhao, Xiaoyan
AU - Wu, Yiping
AU - Miao, Fasheng
AU - Pan, Yutao
AU - Beer, Michael
N1 - Publisher Copyright: © 2024 Elsevier Ltd
PY - 2025/3
Y1 - 2025/3
N2 - Evaluating the reliability of slopes with spatial variability is a challenging issue, especially when the failure probably of the target event is at a low level, because of unaffordable computational cost required in such cases. In this context, an adaptive surrogate model-based approach, namely active learning-assisted bootstrap polynomial chaos expansion, is proposed to alleviate the above computational burden. The proposed approach extends the traditional polynomial chaos expansion by introducing the bootstrap resampling method so that it can deal with reliability issues smoothly and provide a feasible configuration environment to support the active learning algorithm. The computational efficiency can thus be greatly improved by adaptively searching for the most informative samples to train the surrogate model through iterative program. Two spatially varying soil slopes are studied to illustrate the validity of the active learning-assisted bootstrap polynomial chaos expansion. The results show that the proposed approach has superior advantages in terms of efficiency and accuracy, and it is also suitable for handling problems with complex parameter configurations, including high dimensionality and cross-correlation. Besides, the proposed approach has potential in addressing geotechnical engineering problems with low probability levels.
AB - Evaluating the reliability of slopes with spatial variability is a challenging issue, especially when the failure probably of the target event is at a low level, because of unaffordable computational cost required in such cases. In this context, an adaptive surrogate model-based approach, namely active learning-assisted bootstrap polynomial chaos expansion, is proposed to alleviate the above computational burden. The proposed approach extends the traditional polynomial chaos expansion by introducing the bootstrap resampling method so that it can deal with reliability issues smoothly and provide a feasible configuration environment to support the active learning algorithm. The computational efficiency can thus be greatly improved by adaptively searching for the most informative samples to train the surrogate model through iterative program. Two spatially varying soil slopes are studied to illustrate the validity of the active learning-assisted bootstrap polynomial chaos expansion. The results show that the proposed approach has superior advantages in terms of efficiency and accuracy, and it is also suitable for handling problems with complex parameter configurations, including high dimensionality and cross-correlation. Besides, the proposed approach has potential in addressing geotechnical engineering problems with low probability levels.
KW - Active learning algorithm
KW - Bootstrap polynomial chaos expansion
KW - Spatial variability
KW - Surrogate model
UR - http://www.scopus.com/inward/record.url?scp=85213277938&partnerID=8YFLogxK
U2 - 10.1016/j.compgeo.2024.107022
DO - 10.1016/j.compgeo.2024.107022
M3 - Article
AN - SCOPUS:85213277938
VL - 179
JO - Computers and geotechnics
JF - Computers and geotechnics
SN - 0266-352X
M1 - 107022
ER -