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<p>The stability of foundations near slopes is a key challenge in geotechnical engineering, influenced by both foundation and slope failure mechanisms. Traditional methods like Limit Equilibrium (LEM) and Limit Analysis (LAM) have been widely used, with LAM providing more accurate predictions by eliminating predefined failure surfaces. Recent advancements, including the Finite Element Method (FEM), Finite Difference Method (FDM), and Discrete Element Method (DEM), offer improved simulations of soil-structure interactions. Artificial Intelligence (AI)-based models, such as Artificial Neural Networks (ANNs), and probabilistic approaches, including Monte Carlo simulations, enhance prediction accuracy and risk assessment. Studies show that increasing soil friction angle and foundation depth on the downslope side improves bearing capacity, particularly on gentler slopes. Advanced computational methods provide more reliable estimates than traditional techniques, emphasizing the need for their integration in foundation design near slopes to ensure safety and stability. This comparative analysis reveals that the future of reliable design rests not on choosing between methods, but on strategically integrating physics-based simulations with explainable AI to create trustworthy, next-generation predictive tools.</p> Graphical Abstract <p></p>

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Advancements in predicting bearing capacity of foundations near slopes: a comparative review of LEM/LAM/FEM to AI-driven models

  • 期刊论文

  • 发表日期：

    2026年3月24日

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  • Muhammad Rizwan,
  • Sobia Naseem,
  • Muhammad Akhtar Tarar

摘要

The stability of foundations near slopes is a key challenge in geotechnical engineering, influenced by both foundation and slope failure mechanisms. Traditional methods like Limit Equilibrium (LEM) and Limit Analysis (LAM) have been widely used, with LAM providing more accurate predictions by eliminating predefined failure surfaces. Recent advancements, including the Finite Element Method (FEM), Finite Difference Method (FDM), and Discrete Element Method (DEM), offer improved simulations of soil-structure interactions. Artificial Intelligence (AI)-based models, such as Artificial Neural Networks (ANNs), and probabilistic approaches, including Monte Carlo simulations, enhance prediction accuracy and risk assessment. Studies show that increasing soil friction angle and foundation depth on the downslope side improves bearing capacity, particularly on gentler slopes. Advanced computational methods provide more reliable estimates than traditional techniques, emphasizing the need for their integration in foundation design near slopes to ensure safety and stability. This comparative analysis reveals that the future of reliable design rests not on choosing between methods, but on strategically integrating physics-based simulations with explainable AI to create trustworthy, next-generation predictive tools.

Graphical Abstract