@Article{cmes.2026.086451,
AUTHOR = {Xiaoyuan Li, Ming Xu, Shibin Yao, Su Wang, Jian Zhou},
TITLE = {SSA-Optimized Ensemble Learning Models for Accurate and Interpretable Crest Settlement Prediction of Concrete-Faced Rockfill Dams},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/CMES/online/detail/28336},
ISSN = {1526-1506},
ABSTRACT = {Accurate prediction of crest settlement in Concrete-Faced Rockfill Dam (CFRD) is of great significance for safety during its construction and operational phases. In this study, the Squirrel Search Algorithm (SSA) was used to optimize Random Forest (RF), EXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LGBM) to improve model performance. The original dataset, containing 74 cases, was augmented to train and test the models. The final results showed that among all the developed models, the XGBoost model optimized by SSA with a population size of 25 achieved the best performance, with an R<sup>2</sup> of 0.936, RMSE of 0.0210, MAE of 0.0152, VAF of 93.62%, and an A-20 index of 0.830 on the test set. Furthermore, several different model interpretation techniques were employed to analyze the influence of different input variables on the predictions. The analysis results indicated that dam height (H) is the most influential parameter on crest settlement. In conclusion, the predictive model developed in this study effectively predicts crest settlement and demonstrates interpretability.},
DOI = {10.32604/cmes.2026.086451}
}