Artificial Intelligence and Physics-Informed Modeling for Rock Slope Engineering: Progress, Challenges, and Future Directions

Recent advances in deep learning and artificial intelligence (AI) have significantly transformed the analysis of rock slopes and geotechnical structures. Rock slope stability is governed by complex interactions among rock mass discontinuity networks, mechanical properties, environmental loading conditions, and stress redistribution. Traditional analytical and numerical methods, including discrete element methods, finite element simulations, and limit equilibrium approaches, provide valuable insights; however, they often have limitations in capturing complex failure mechanisms and handling heterogeneous datasets. This review systematically synthesizes recent developments in AI-driven approaches for rock slope engineering, with particular emphasis on their integration with physical and numerical modeling frameworks and their role in improving the performance assessment of geotechnical systems. Key applications include machine learning-based slope stability prediction, automated discontinuity detection, surrogate modeling for numerical simulations, and spatiotemporal forecasting of slope deformation using monitoring data. The review further discusses emerging approaches such as physics-informed machine learning, digital twin systems, and hybrid AI–numerical frameworks, which combine data-driven learning with established rock mechanics principles. In addition, the potential of AI technologies to support sustainable rock slope management is evaluated, including early warning systems, optimal stabilization design, and resilient infrastructure monitoring. Finally, major challenges related to data quality, model interpretability, uncertainty, and integration with physical models are identified. The review suggests that future research should focus on integrating AI with physics-based modeling and uncertainty quantification, supported by rigorous validation strategies and high-quality datasets, to improve reliability and practical applicability in rock slope engineering. This paper provides a comprehensive perspective on how AI and deep learning can improve the understanding, prediction, and long-term management of rock slopes in modern geotechnical engineering practice.