Machine learning-driven modeling of soil plasticity and strength parameters with interpretability insights

Abstract

This study proposes advanced stacking ensemble machine learning approaches to predict soil Liquidity Index ( LI ) and Undrained Shear Strength ( Su ). Over 1,550 LI and 950 Su measurements from global research were employed. Individual regressors—Multilayer Perceptron (MLP), Random Forest (RF), and AdaBoost—were evaluated alongside ensemble strategies, including Simple Averaging, Weighted Averaging, and stacking using a Support Vector Regression (SVR) meta-learner. Hyperparameter tuning was performed using both Bayesian Optimization (BO) and Particle Swarm Optimization (PSO). AdaBoost achieved the best results for LI prediction, while RF yielded the highest accuracy for Su . Weighted Averaging ensemble methods produced outstanding predictive performances, achieving R 2 values of 0.9913 (BO) and 0.9961 (PSO) for Su , and 0.9624 (BO) and 0.9338 (PSO) for LI. BO proved superior for LI models, while PSO excelled for Su models. The results highlight the robust potential of ensemble modeling and tailored optimization in the field of geotechnical engineering.