Enhanced Predictive Ensemble Learning Models for Compressive Strength of Concrete Modified with Bentonite Clay

This study addresses the logistical challenges of testing bentonite plastic concrete in the field by developing predictive models for compressive strength (CS) using Python-based machine learning techniques. The models, including random forest, adaptive boosting, extreme gradient boosting, and gradient boosting regression tree, were enhanced with forensic-based investigation optimization. A dataset of 285 CS records was used to train and validate these models. SHAP analysis highlighted the impact of various inputs like gravel, bentonite, curing time, and cement on CS. Results show that the GBRT-FBIO model outperformed the others in CS prediction, indicating its superior effectiveness for material science applications.