Factor of safety prediction for high road embankments using mixed effects random forest and bee colony optimization

Abstract This study presents a novel hybrid machine learning framework integrating Random Forest (RF), Mixed Effects Random Forest (MERF), and Artificial Bee Colony optimized Mixed Effects Random Forest (ABC-MERF) for predicting Factor of Safety (FOS) in high road embankments. The methodology addresses critical limitations in traditional geotechnical analysis by incorporating hierarchical group effects and bio-inspired optimization techniques to handle complex, non-linear relationships inherent in slope stability problems. Model performance was evaluated using a comprehensive dataset derived from validated finite element analysis, encompassing key influencing factors including embankment height, slope geometries, and diverse embankment soil properties representative of realistic field conditions. The predictive accuracy of all models was assessed using standard regression metrics (Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R2)) to provide a comprehensive evaluation of prediction errors and explained variance. Results demonstrate that ABC-MERF outperforms conventional approaches, achieving high predictive accuracy (R2 = 0.991, RMSE = 0.025) that represents statistically significant improvement over baseline methods. The feature importance analysis reveals friction angle and embankment height as the most critical design parameters, validating the physical relevance of model predictions and aligning with fundamental soil mechanics principles. This framework successfully bridges machine learning accuracy with geotechnical engineering interpretability, offering practitioners a robust, physics-based tool for optimizing embankment stability assessment and reducing infrastructure risks in critical transportation projects.