Research on Slope Stability Based on Bayesian Gaussian Mixture Model and Random Reduction Method Article Swipe
Jun He
,
Tao Deng
,
Shanshan Peng
,
Xiaoyan Pang
,
Daochun Wan
,
Shao-Jun Zhang
,
Xiaoqiang Zhang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/app15147926
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/app15147926
Slope stability analysis is conventionally performed using the strength reduction method with the proportional reduction in shear strength parameters. However, during actual slope failure processes, the attenuation characteristics of rock mass cohesion (c) and internal friction angle (φ) are often inconsistent, and their reduction paths exhibit clear nonlinearity. Relying solely on proportional reduction paths to calculate safety factors may therefore lack scientific rigor and fail to reflect true slope behavior. To address this limitation, this study proposes a novel approach that considers the non-proportional reduction of c and φ, without dependence on predefined reduction paths. The method begins with an analysis of slope stability states based on energy dissipation theory. A Bayesian Gaussian Mixture Model (BGMM) is employed for intelligent interpretation of the dissipated energy data, and, combined with energy mutation theory, is used to identify instability states under various reduction parameter combinations. To compute the safety factor, the concept of a “reference slope” is introduced. This reference slope represents the state at which the slope reaches limit equilibrium under strength reduction. The safety factor is then defined as the ratio of the shear strength of the target analyzed slope to that of the reference slope, providing a physically meaningful and interpretable safety index. Compared with traditional proportional reduction methods, the proposed approach offers more accurate estimation of safety factors, demonstrates superior sensitivity in identifying critical slopes, and significantly improves the reliability and precision of slope stability assessments. These advantages contribute to enhanced safety management and risk control in slope engineering practice.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app15147926
- https://www.mdpi.com/2076-3417/15/14/7926/pdf?version=1752662493
- OA Status
- gold
- Cited By
- 1
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4412473103
All OpenAlex metadata
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https://openalex.org/W4412473103Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/app15147926Digital Object Identifier
- Title
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Research on Slope Stability Based on Bayesian Gaussian Mixture Model and Random Reduction MethodWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-07-16Full publication date if available
- Authors
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Jun He, Tao Deng, Shanshan Peng, Xiaoyan Pang, Daochun Wan, Shao-Jun Zhang, Xiaoqiang ZhangList of authors in order
- Landing page
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https://doi.org/10.3390/app15147926Publisher landing page
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https://www.mdpi.com/2076-3417/15/14/7926/pdf?version=1752662493Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2076-3417/15/14/7926/pdf?version=1752662493Direct OA link when available
- Concepts
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Stability (learning theory), Reduction (mathematics), Gaussian, Mathematics, Computer science, Statistics, Machine learning, Physics, Geometry, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2032152396, https://openalex.org/W3131945974, https://openalex.org/W4394780523, https://openalex.org/W2973151661, https://openalex.org/W2383053788, https://openalex.org/W3106816302, https://openalex.org/W3152753258, https://openalex.org/W6871378450, https://openalex.org/W6854531006, https://openalex.org/W4221065154, https://openalex.org/W3196655202, https://openalex.org/W2944593093, https://openalex.org/W2982467462, https://openalex.org/W3009051932, https://openalex.org/W6855850173, https://openalex.org/W3198818350, https://openalex.org/W1999628021, https://openalex.org/W4395462733, https://openalex.org/W6849806813, https://openalex.org/W2989967073, https://openalex.org/W6801728675, https://openalex.org/W2757217159, https://openalex.org/W1967495697, https://openalex.org/W2052815170, https://openalex.org/W6786707726, https://openalex.org/W4317935261, https://openalex.org/W4401697938, https://openalex.org/W4322767564, https://openalex.org/W3202412422, https://openalex.org/W4386099939, https://openalex.org/W3107425167, https://openalex.org/W4384070740 |
| referenced_works_count | 32 |
| abstract_inverted_index.A | 110 |
| abstract_inverted_index.a | 77, 151, 197 |
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