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Published (Version of record)CC BY-NC-ND V4.0, Open Access
Published (Version of record)CC BY-NC-ND V4.0, Open
Journal article
Weighted low-discrepancy samplings: A novel method for slope system reliability analysis
Computers and geotechnics, Vol.160, 105530
08/2023
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Abstract
This paper presents an efficient method for system reliability analysis of geotechnical examples based on the low-discrepancy sequences (LDS) sampling technique in combination with probabilistic weighting of the samples, i.e., the weighted low-discrepancy samplings (WLDS). In the framework of quasi-Monte Carlo simulation (QMCS), the Sobol’s quasi-random LDS are employed to generate the basic random variable samples that can significantly improve the efficiency of probabilistic weighting for the probability of failure calculation. In particular, the most probable system failure points (MPPs) can be directly captured according to the probabilistic weighting calculation. Via Nataf transformation, the WLDS computational framework incorporating correlated random variables in soils is also illustrated. Focusing on geotechnical applications, the reliability analyses of example soil slopes are presented to verify the performance of the proposed method. It is found that the proposed WLDS method not only can accurately obtain the probability of system failure but also can capture the multi-MPPs defining different earth slope failure modes in a cost-effective manner. The results in this study demonstrate that the proposed WLDS method is a feasible and promising method for performing the system reliability analysis of complex geotechnical earth structures.
Details
- Title
- Weighted low-discrepancy samplings: A novel method for slope system reliability analysis
- Creators
- Tao Wang - Hohai UniversityJian Ji - Hohai UniversityGuoyang Fu - Monash UniversityQing Lü - Zhejiang University
- Publication Details
- Computers and geotechnics, Vol.160, 105530
- Publisher
- Elsevier Ltd
- Grant note
- This work is financially supported by the National Natural Science Foundation of China [NSFC Grant Nos. 52079045, U22A20594, 42277132], and the Key R&D Project of Zhejiang Province (2021C03159).
- Identifiers
- 991013126109702368
- Copyright
- © 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license, (http://creativecommons.org/licenses/by-nc-nd/4.0/).
- Academic Unit
- Faculty of Science and Engineering
- Language
- English
- Resource Type
- Journal article