For a dam curtain-grouting project

accurate prediction of its unit grout injection volume is crucial to engineering cost control and effective mitigation of its seepage-related risks. To address the complicated nonlinear relationship between its evaluation indicators and unit cement consumption

this paper presents a novel prediction model based on stacking ensemble learning

and compares it with the models of Random Forest (RF)

XGBoost

and Support Vector Machine (SVM). We optimize the hyper-parameters of these models using a Newton-Raphson-based optimizer (NRBO)

and test their predictive performance based on both training sets and validation sets. Results show the stacking model markedly outperforms the single-method models

with R2 = 0.998 and RMSE = 0.350 achieved on the training sets

and R2 = 0.971 and RMSE = 1.224 on the validation sets. By contrast

validation-set R2 values of the three single-method models are 0.905

0.930 and 0.728 respectively

and RMSEs are 2.219

1.896 and 3.748 respectively. Ensemble-learning models (Stacking

RF

and XGBoost) feature a stronger fitting capacity and robustness in the case of high-dimensional nonlinear data

while the stacking model

by leveraging the strengths of multiple base learners

further enhances predictive accuracy and robustness to outliers. Thus

our NRBO-Stacking model offers high accuracy

effective solutions

and better generalization performance for dam curtain-grouting projects.