Application of Artificial Intelligence Techniques in Slope Stability Analysis

Abstract

Artificial intelligence (AI) techniques have become a trusted methodology among researchers in the recent decades for handling a variety of geotechnical and geological problems. Machine learning (ML) algorithms are distinguished by their superior feature learning and expression capabilities as compared to traditional approaches, attracting researchers from a variety of domains to their growing number of applications. Different ML models are extensively used in the field of geotechnical engineering to accounting for the inherent spatial variability of soils in slope stability assessments. This study presents a brief overview of the application of several AI techniques in the area of slope stability, including adaptive neuro-fuzzy inference system, artificial neural network, extreme learning machine, functional network, genetic programming, Gaussian process regression, least-square support vector machine, multivariate adaptive regression spline, minimax probability machine regression, relevance vector machine, and support vector machine. Additionally, a summary containing published literature, the corresponding reference cases with the type of input soil parameters, and the implemented ML algorithms was compiled. Recent applications of various hybrid ML models in slope stability assessment are also discussed. Furthermore, the challenges and future prospects of AI techniques development in solving slope stability problems are presented and discussed.