Prediction of Concrete Abrasion Depth in Hydraulic Structures Using an Interpretable Hybrid Ensemble Model Based on Meta-Heuristic Algorithms

The concrete protective layer in hydraulic tunnels is prone to abrasion by high-velocity sand-laden water, reducing structural durability. Accurate prediction of abrasion depth is key to rational hydraulic structure design. Existing studies have limitations: classical empirical models consider only a single factor, while early machine learning models fail to cover two core abrasion mechanisms (friction and impact) and lack meta-heuristic algorithm-based parameter optimization, leading to insufficient generalization and stability. This study aims to (1) establish a multi-source database with 690 cases (463 friction-dominated, 227 impact-dominated) covering multiple test standards (ASTM C944, ASTM C779, BIS: 1237-1980, ASTM C1138); (2) optimize hyperparameters of LightGBM, XGBoost, and CatBoost using Genghis Khan Shark Optimizer (GKSO) to build a hybrid ensemble model; (3) verify model performance and identify key factors via SHAP analysis. After preprocessing, input features were simplified to five: water–cement ratio, FA/CA (fine aggregate/coarse aggregate), age, T/V (test duration/velocity), and WRA content. Results show that GKSO-CatBoost performed best (test set R2 = 0.982, RMSE = 0.1231 mm). SHAP analysis identified T/V and the water–cement ratio as key influencing features, providing clear directions for optimizing concrete mix proportions under different standard scenarios. This study provides a new method for hydraulic concrete abrasion prediction and a scientific basis for durability design oriented to specific test standards.