Prediction of rock slope failure based on multiple machine learning algorithms
Open accessAbstract
Slope failures have the potential to seriously jeopardize access to sustainable development since they cause numerous casualties as well as disastrous effects on society and the economy. It is imperative to use precise operable computational designs in this case. This study examined the efficacy of five distinct machine learning models, namely support vector machines, decision trees, gradient boost machine learning, and random forest, in predicting the slope safety factors. This article?s primary goal is to assess and improve the different machine learning-based analytical representations in relation factor of safety computations. The genetic algorithm mimics the processes of growth, hybridization, and mutagenesis found in the expected collection and inherent procedures to resolve the hyperparameters of machine learning algorithms. A total of 217 cases were collected in order to train and evaluate these models. Multiple convergence analysis is also used to study the independence of individual characteristics. The assessed methods? competence was assessed through the application of diverse performance assessment indicators. The various classifiers function satisfactorily for slope failure inquiry, according to the evaluation and comparison of the data. Random forest was found to be the best classification method for slope failure prediction, with an accuracy of 91%.
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ML models (including ANNs and support vector machines) are used to estimate soil parameters (such as shear strength and permeability) from accessible data (borehole logs, sensor readings), reducing the need for extensive laboratory tests.
“ML models (including ANNs and support vector machines) are used to estimate soil parameters (such as shear strength and permeability) from accessible data (borehole logs, sensor readings), reducing the need for extensive laboratory testsand to predict phenomena such as rock slope failure (Mnzool, 2024).”
From Artificial intelligence in civil engineering: emerging applications and opportunities (2025) · cites this paper as Mnzool, 2024
The Discussion and Conclusion support the citing sentence’s point that machine-learning models, including an SVM, can predict slope failure; the Introduction also discusses neural networks for slope stability. The Slope stability analysis dataset section instead lists soil properties as inputs for predicting stability. It does not support estimating shear strength or permeability from borehole logs or sensor readings, or reducing laboratory tests. (LLM, conf=0.86)
Experimental — accuracy not yet measuredML models can predict phenomena such as rock slope failure.
“ML models (including ANNs and support vector machines) are used to estimate soil parameters (such as shear strength and permeability) from accessible data (borehole logs, sensor readings), reducing the need for extensive laboratory testsand to predict phenomena such as rock slope failure (Mnzool, 2024).”
From Artificial intelligence in civil engineering: emerging applications and opportunities (2025) · cites this paper as Mnzool, 2024
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- Authors
- Mnzool, M.
- Affiliations
- Taif University (SA)
- Corresponding author
- Mnzool, Mohammed
- ORCID
- Mnzool, Mohammed
- Venue
- Thermal Science
- Publisher
- National Library of Serbia
- Published
- 2024
- Volume
- 28
- Issue
- 6 Part B
- Pages
- 4907-4916
- ISSN
- 0354-9836 (ISSN-L)
- 2334-7163
- OpenAlex
- W4406530599
- Cited by
- 4
- References
- 35
- Citations by year
- 2026: 2 · 2025: 2
- FWCI
- 1.04
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Thermal Science (publishedVersion, cc-by-nc-nd)
- Funders
- Taif University
- Topics
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Geotechnical Engineering and Analysis
- Physical Sciences › Environmental Science › Management, Monitoring, Policy and Law › Landslides and related hazards
- Physical Sciences › Engineering › Civil and Structural Engineering › Tunneling and Rock Mechanics
- Keywords
- rock slope failure, slope stability, slope safety factor, failure prediction, random forest, gradient boosting, support vector machine, decision tree, genetic algorithm hyperparameter optimization, machine learning classifier comparison
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