Physics-guided ensemble machine learning framework for slope failure prediction

Abstract

Purpose. This research aims to provide a physics-based ensemble machine learning framework that can reliably predict slope stability and distinguish stable from unstable slopes in static and seismic conditions. Standard analytical and numerical methods have significant processing costs and oversimplified assumptions that limit their usefulness. Methods. The study analyzed 700 slope stability samples, including geotechnical and seismic factors such as slope height, slope angle, cohesion, internal friction angle, and peak ground acceleration. The proposed model now includes physics-based engineering elements, such as tan ϕ, c/H, and PGA/g, to account for geotechnical interactions. A linear meta-learner and Random Forest and Gradient Boosting regressors were used to develop a stacked ensemble framework. We assessed model strength and reliability. Findings. The devised framework showcased exceptional prediction performance with an (R2) of 0.982, a mean absolute error of 0.02 and a root mean square error of nearly 0.03. Cross-validation showed consistent generalization. Random Forests classified slope stability conditions with 97.4% accuracy. The most important parameters for slope stability predictions were the internal friction angle and cohesiveness. Furthermore, the inclusion of carefully crafted physics-based features demonstrably improved robustness, accuracy and consistency. Originality. This work introduces a pioneering, combined framework that fuses the mechanics of geotechnical soil with ensemble machine learning techniques, enhancing both interpretability and the certainty of slope stability predictions. Practical implications. The framework may support rapid, cost-effective, and interpretable preliminary geotechnical risk assessment and design screening. However, its use in slope monitoring or early-warning applications requires further field validation and integration with monitoring data.