A decision-support framework for slope stability analysis: Prediction and Reinforcement Optimization Using Machine Learning

Slope stability analysis is an important task in geotechnical engineering but predicting the Factor of Safety (FS) and the optimal remediation strategy for unstable slopes is a complex and resource-consuming challenge. This study introduces a novel, two-stage artificial intelligence (AI) framework to be used as a complete decision support tool for engineers. First, comparative analysis of multiple machine learning (ML) models, which includes Linear Regression, Random Forest, XGBoost and LightGBM, was performed on a large synthetic data of 10,000 slope simulations. The LightGBM model for the prediction of the Factor of Safety showed good results in its prediction accuracy. Second, this optimized prediction model was incorporated in a "Smart Optimizer" tool. If a predicted slope is determined to be unstable (FS < 1.0), this tool automatically simulates the geotechnical effects of four common reinforcement techniques including: Retaining Walls, Soil Nailing, Geosynthetics, and Drainage. By comparing the predicted FS for all the different scenarios, the tool gives a clear indication of which stabilization method is the best. This framework goes beyond simply predicting stability to provide actionable and data-driven optimization to deliver a fast and reliable system for improving the safety and design efficiency of geotechnical projects.