Prediction of Time Variation of Local Scour Depth at Bridge Abutments: Comparative Analysis of Machine Learning

Computing the temporal variation in clearwater scour depth around abutments is important for bridge foundation design. To reach the equilibrium scour depth at bridge abutments takes a very long time. However, the corresponding times under prototype conditions can yield values significantly greater than the time to reach the design flood peak. Therefore, estimating the temporal variation in scour depth is necessary. This study evaluates multiple machine learning (ML) models to identify the most accurate method for predicting scour depth (Ds) over time using experimental data. The dataset of 3275 records, including flow depth (Y), abutment length (L), channel width (B), velocity (V), time (t), sediment size (d50), and Ds, was used to train and test Linear Regression (LR), Random Forest Regressor (RFR), Support Vector Regression (SVR), Gradient Boosting (GBR), XGBoost, LightGBM, and KNN models. Results demonstrated the superior performance of AI-based models over conventional regression. The RFR model achieved the highest accuracy (R2 = 0.9956, Accuracy = 99.73%), followed by KNN and GBR. In contrast, the conventional LR model performed poorly (R2 = 0.4547, Accuracy = 57.39%). This study confirms the significant potential of ML, particularly ensemble methods, to provide highly reliable scour predictions, offering a robust tool for enhancing bridge design and safety.