International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 10 | Oct- 2025
p-ISSN: 2395-0072
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A Comprehensive Review on Methods of Slope Stability Assessment B Priyanka1, Lovepreet Kaur2, Kamalpreet Kaur3 1PG scholar, Dept. of Civil Engineering, SRM Global Group of Institutions, Haryana, India 2Professor, Dept. of Civil Engineering, SRM Global Group of Institutions, Haryana, India
rofessor, Dept. of Civil Engineering, SRM Global Group of Institutions, Haryana, India ---------------------------------------------------------------------***--------------------------------------------------------------------3P
Abstract - Slope stability assessment can be considered one
A variety of approaches for conducting stability analysis for finite and infinite slopes have been developed. Some of these methods are the limit equilibrium method (LEM), finite element method (FEM), finite difference method (FDM), limit analysis method, and rigid element method. Out of these methods, the limit equilibrium method is one of the most widely used. This method evaluates slope stability by calculating the factor of safety (FOS) at the critical failure surface and using assumptions regarding the shape and position of failure surfaces [5]. Variants of the LEM include the Bishop method [6], Janbu method [7], Morgenstern-Price method [8], and Sarma method [9], which are widely applied in slope stability analysis [10-12]. Although the limit equilibrium method is a simple method, it does not precisely represent the actual stress within the soil; thus, assumptions for the sliding surface in terms of force and moment equilibrium need to be used. Many researchers have developed a finite element method depending on elasticplastic theory in order to reduce these limitations and describe the behavior of soil under stress. This method has drawn a lot of attention in slope stability analysis [13-16], but its accuracy depends on precise and accurate input parameters and the application of an adequate constitutive model. Due to the difficulty of computing the model along with these parameters, this method has certain limitations [17].
of the most complicated challenges for the geotechnical engineers. This assessment plays a very important role in the design of civil engineering structures like dams, road embankments, and slopes found in hilly areas. The factor of safety (FOS) is expressed as the ratio of shear stress divided by the shear strength. Stable slopes are defined when driving forces are less than the resisting forces, and a factor of safety is defined as a ratio of resistive forces divided by the driving forces. Throughout the decades, engineers have analyzed slope stability through conventional methods such as limit equilibrium methods (LEM), limit analysis methods, and numerical simulation methods like the finite element method (FEM) and finite difference method (FDM). However, recent advancements in computational methods have resulted in the emergence of machine learning algorithms, consisting of artificial neural networks (ANN), support vector machines (SVM), decision trees (DT) and random forest (RF), as promising alternatives for analyzing complex slope stability problems under diverse geological and environmental conditions. This review paper will distinguish between traditional and machine learning based methods for the assessment of slope stability problems. It serves as a useful resource for researchers and practitioners, assisting in selecting the most suitable method for specific slope stability applications based on accuracy, computational efficiency, and complexity of the problem.
Classical deterministic slope stability methods mainly focus on the computation of a single FOS to assess slope stability. However, such analyses do not consider uncertainty in soil properties, groundwater level, site conditions, and the quality of geotechnical investigations. Such uncertainties may differ significantly at different locations and may play a major role in determining the accuracy of the results [18]. Probabilistic approaches have thus become more and more prominent, taking major soil parameters as random variables and characterizing them in terms of statistical parameters like the mean, standard deviation, variance, and coefficient of variation. Such parameters are usually assumed to be normally or log-normally distributed. Probabilistic approaches compute the probability of failure (Pf) and the reliability index (Ξ²), thus obtaining a wider and risk-based slope stability assessment, especially in regions of complex geological conditions, compared to deterministic approaches [19-21].
Key Words: Limit Equilibrium Method, Finite Element Method, Limit Analysis Method, Artificial Neural Network, Support Vector Machine, Decision Tree, Random Forest.
1. INTRODUCTION Slope stability assessment is an essential element in geotechnical engineering because it plays a significant role in ensuring the safety of industrial engineering projects [1, 2]. This analysis is more complex and thorough, making it riskier and more difficult than other operations involved in the practice of geotechnical engineering. Slope failure is a complicated natural phenomenon which results in serious disasters, including landslides, and causes economic burden on several countries [3]. Consequently, it is essential to develop a safe, reliable, and effective model for analyzing, assessing, and predicting slope stability to mitigate the geological risks associated with slope failures and to safeguard people and property [4].
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