Assessment of unconfined compressive strength of nano-doped fly ash-treated clayey soil using machine learning tools

This study presents a comparative framework for evaluating the predictive performance of four machine learning models namely Gradient Boosting Machine (GBM), Random Decision Forest (RDF), Non-Parametric Regression (NP), and Decision Tree (TREE), in estimating the Unconfined Compressive Strength (UCS) of nano-doped fly ash reinforced clayey soil. The key innovation lies in combining ensemble learning with sensitivity, monotonicity, and SHAP (SHapley Additive exPlanations) analyses to enhance predictive accuracy and interpretability in geotechnical applications. Using a comprehensive dataset of key variables (Curing Days, Maximum Dry Density (MDD), Optimum Moisture Content (OMC), Fly Ash, Multi-Walled Carbon Nanotubes (MWCNT), and Sodium Hexametaphosphate (SHMP)) models were trained and validated using various statistical metrics (R², MAE, MSE etc.). GBM achieved the best performance (R²: 1.000, 0.955; MAE: 0.001, 0.022; MSE: 0.000, 0.001 in training and testing respectively), consistently outperforming RDF, NP, and TREE. Taylor diagrams, REC curves, and AOC analysis further confirmed GBM's superior generalization and minimal error rates. Sensitivity analysis identified Days (0.4556), MDD (0.2458), and SHMP (0.1558) as the most influential factors, trends that were corroborated by SHAP analysis. Monotonicity analysis validated positive relationships with Days (R² = 0.9845) and MDD (R² = 0.689), while OMC and SHMP showed inverse effects. These results establish GBM as a highly accurate and interpretable model for UCS prediction, offering a powerful tool for optimizing soil stabilization in construction and geotechnical engineering.