A hybrid ANN–FEM framework for high accuracy slope stability prediction

Abstract

Purpose. To develop a hybrid methodology that integrates Artificial Neural Networks (ANN) with Finite Element Method (FEM) simulations for the rapid and accurate prediction of slope stability. Methodology. A dataset of 1,000 FEM simulations was generated by systematically varying seven key input parameters: slope geometry (height and angle) and soil properties (cohesion, friction angle, unit weight, pore water pressure ratio, and reinforcement type). An ANN model with a (7-10-1) feedforward architecture was trained on this data. Findings. The model demonstrated exceptional predictive performance, achieving a near-perfect correlation coefficient (R  0.999997) and an extremely low mean squared error (MSE = 3.6828  10-6), showing close agreement with the FEM-computed factors of safety (FOS). A comprehensive sensitivity analysis based on analysis of variance identified the pore water pressure ratio as the dominant controlling parameter, contributing approximately 77 % to the variability of FOS, followed by cohesion with a contribution of about 13 %. Complementary correlation analysis revealed that cohesion exhibits the strongest linear correlation with FOS (r = 0.83), whereas the pore water pressure ratio shows a relatively weak linear correlation, highlighting its pronounced nonlinear and interaction-driven influence on slope stability. These results demonstrate that the proposed ANN–FEM hybrid framework provides a powerful, efficient, and reliable tool for slope stability assessment and parametric analysis. The methodology is particularly well suited for engineering applications requiring rapid decision-making, large-scale evaluations, and uncertainty analysis. Originality. The core originality of this research is its development of a robust ANN–FEM hybrid framework applied to a large, systematically generated dataset of 1,000 slope simulations. Unlike many studies, it comprehensively incorporates seven critical input variables, including the often underrepresented pore water pressure. Furthermore, its scientific rigor is enhanced by a dual interpretability strategy that combines analysis of variance for quantifying parameter contribution and correlation heatmaps for distinguishing linear effects from nonlinear ones, providing deeper insight into slope stability mechanisms. Practical value. This study provides engineers with a fast and reliable tool to predict slope safety in seconds instead of running time-consuming FEM simulations, making it highly valuable for real-time decision-making and large parametric studies. Practically, it also shows that controlling pore water pressure (through drainage) is the most effective risk-reduction strategy, while cohesion offers a predictable way to improve slope stability in design.