Curtain grouting volume prediction using a Bayesian-optimized stacking ensemble model with SHAP analysis

) of 0.92, a mean absolute error (MAE) of 70.19 L, and a root mean square error (RMSE) of 187.07 L. Scatter analysis further indicated strong agreement between predicted and measured values. SHAP analysis quantified the relative contributions of geological conditions, construction parameters, and slurry properties to grouting volume. Overall, the proposed approach improves predictive performance of grouting volume under complex geological conditions and provides support for construction planning and quality management.