Efficient reliability analysis of soil slopes by combining strength reduction sampling with SVM surrogate model

LU Jian; ZENG Peng; FENG Bing; WANG Xin; YAN Zulong

doi:10.19509/j.cnki.dzkq.tb20240756

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LU Jian，ZENG Peng，FENG Bing，et al. Efficient reliability analysis of soil slopes by combining strength reduction sampling with SVM surrogate model[J]. Bulletin of Geological Science and Technology，2026，45（3）：1-12 doi: 10.19509/j.cnki.dzkq.tb20240756

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Efficient reliability analysis of soil slopes by combining strength reduction sampling with SVM surrogate model

doi: 10.19509/j.cnki.dzkq.tb20240756

LU Jian^a
,,
ZENG Peng^{b
,
,},
FENG Bing^a,
WANG Xin^a,
YAN Zulong^a

a.
College of Environment and Civil Engineering, Chengdu University of Technology
b.
State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu 610059, China

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Author Bio:
E-mail：1633580118@qq.com
Corresponding author: E-mail：zengpeng15@cdut.edu.cn
Received Date: 05 Dec 2024
Accepted Date: 30 Jun 2025
Rev Recd Date: 28 Jun 2025

Available Online: 30 Jun 2025

Abstract

Abstract

Objective
Landslides caused by slope instability have posed a considerable threat to human lives and property, especially under the background of rapid infrastructure development and drastic global climate change. Traditional slope stability evaluation methods usually ignore the randomness of soil parameters, and conventional reliability analysis approaches such as monte carlo simulation (MCS) suffer from excessively high computational costs, which seriously restrict their practical engineering applications.
Methods
To address these problems, this study develops a novel and efficient method for reliability analysis of soil slopes, termed strength reduction sampling-support vector machine (SRS-SVM), by integrating the finite difference strength reduction sampling strategy and an active learning support vector machine surrogate model. The proposed method employs strength reduction sampling to generate highly informative training points strictly near the limit state surface (LSS), where one single numerical model evaluation can produce three key samples, thus remarkably improving the training efficiency of the SVM surrogate model. Meanwhile, an improved active learning function based on k-fold cross-validation and jackknifing estimation is adopted to select points with high uncertainty close to the LSS, and a convergence criterion based on the stability of system failure probability is applied to balance computational accuracy and efficiency. Four typical slope cases, including a single-layer soil slope, a two-layer clay slope, a three-layer slope with four random variables, and a four-layer complex dam slope with six random variables, are adopted to verify the performance of the SRS-SVM method. The proposed method is compared with several classic reliability approaches, such as the classical response surface method (CRSM), radial basis function (RBF), and strength reduction sampling-gaussian process regression (SRS-GPR).
Results
The results demonstrate that the SRS-SVM method requires fewer than 40 numerical model evaluations for all cases, and the absolute relative errors of the system failure probability are controlled within 1.5%, showing overwhelming advantages over traditional methods in both computational efficiency and accuracy. Furthermore, the method presents strong adaptability in dealing with highly nonlinear performance functions and multi-variable complex slope conditions.
Conclusion
This study combines the high-efficiency sampling characteristic of strength reduction technique and the superior classification ability of SVM, providing a new high-performance solution for accurate and fast reliability analysis of soil slopes. The SRS-SVM method has broad application prospects in practical engineering risk assessment, disaster prevention and mitigation of slopes, and can effectively support the reliability-based design of geotechnical engineering under uncertain conditions.
- slope stability,
- uncertainty,
- strength reduction sampling,
- SVM surrogate model,
- reliability analysis

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References(43)

References

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Efficient reliability analysis of soil slopes by combining strength reduction sampling with SVM surrogate model

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