Interpretable Ensemble Learning with Lévy Flight-Enhanced Heuristic Technique for Strength Prediction of MICP-Treated Sands

Microbially-induced calcite precipitation (MICP) has emerged as a promising bio-geotechnical technique for sustainable soil improvement, yet accurate prediction of treatment effectiveness remains challenging due to complex multi-factor interactions. This study develops an ensemble learning framework (LARO-EnML) for predicting the unconfined compressive strength (UCS) of MICP-treated sand. A comprehensive database containing 402 experimental datasets was utilised in the study, consisting of unconfined compression test results from bio-cemented sands with eight key input parameters considered. The performance evaluation demonstrates that LARO-EnML achieves superior predictive accuracy, with RMSE of 0.5449, MAE of 0.2853, R2 of 0.9570, and OI of 0.9597 on the test data, significantly outperforming other models. Model interpretability analysis reveals that calcite content serves as the most influential factor, with a strong positive correlation to strength enhancement, while urease activity exhibits complex, staged influence characteristics. This research contributes to advancing the practical implementation of MICP technology in geotechnical engineering by offering both accurate predictive capability and enhanced process understanding through interpretable ML approaches.