THERMAL SCIENCE

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Mohammed Mnzool

PREDICTION OF ROCK SLOPE FAILURE BASED ON MULTIPLE MACHINE LEARNING ALGORITHMS

ABSTRACT
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%.
KEYWORDS
slope failure, rock slope, support vector machine, Decision Tree, random forest, genetic algorithms
PAPER SUBMITTED: 2024-06-25
PAPER REVISED: 2024-08-20
PAPER ACCEPTED: 2024-10-17
PUBLISHED ONLINE: 2025-01-25
DOI REFERENCE: https://doi.org/10.2298/TSCI2406907M
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2024, VOLUME 28, ISSUE Issue 6, PAGES [4907 - 4916]
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Prediction of rock slope failure based on multiple machine learning algorithms

2025 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence