Estimation of Equilibrium Scour Depth Around Abutments Using Artificial Intelligence

This study compares various machine learning models to determine the method with the highest accuracy rate to predict the equilibrium scour depth of bridge abutments. Unlike in previous studies, hyperparameter optimization is focused on increasing model performance in small datasets, and feature importance ranking is analyzed. This study employs Multiple Linear Regression (MLR), Support Vector Regression (SVR), Decision Tree Regressor (DTR), Random Forest Regressor (RFR), XGBoost, and Artificial Neural Networks (ANNs) to predict the Dse. The dataset consists of 150 records with the following key hydraulic parameters: the flow depth (Y), abutment length (L), channel width (B), flow velocity (V), and median grain size (d50). The results show that DTR achieved the highest accuracy (R2 = 0.992, accuracy = 99.28%), followed by XGBoost (R2 = 0.990, accuracy = 99.21%) and ANNs (R2 = 0.981, accuracy = 98.77%). Traditional MLR exhibited lower accuracy (R2 = 0.806, accuracy = 81.14%), confirming the superiority of AI-based models. These findings highlight the effectiveness of machine learning in scour prediction, providing a reliable alternative to conventional methods. This study underscores the potential of AI in hydraulic engineering, facilitating efficient bridge design and maintenance strategies. Future research should explore real-time data integration and hybrid AI models for improved interpretability and robustness.