An In - Depth Study of Artificial Intelligence Applications in Slope Stability Assessment of Mining Sites

Abstract

The stability of mine slopes is a critical aspect of open - pit mining operations, as slope failure can result in severe economic, environmental, and human losses. In recent years, Artificial Intelligence (AI) has emerged as a powerful tool for enhancing the accuracy and efficiency of slope stability assessments. This study presents a comprehensive review and deep analysis of AI - based approaches applied to slope stability assessment in mining areas. This study presents the first comprehensive and in - depth review focusing specifically on AI - based approaches applied to slope stability assessment in mining areas, highlighting their strengths and weaknesses. An in - depth analysis was adopted, focusing on peer - reviewed publications from 2010 to 2025, retrieved from databases such as Scopus, Google Scholar, and Web of Science. The selected studies were categorized into two main groups based on the type of AI techniques used: machine learning models and deep learning and optimization algorithms. The study reveals that AI models, particularly hybrid and deep learning approaches, outperform traditional deterministic methods in predicting slope failures, especially when dealing with complex, nonlinear geotechnical data. The integration of geospatial technologies with AI further enhances real - time monitoring and decision - making capabilities. These findings provide valuable insights for mining engineers and researchers, supporting the development of intelligent, data - driven systems for safer and more sustainable slope management in mining operations.