Scour depth prediction using machine learning and explainable AI: assessment of bridge vulnerability

Introduction One of the major factors contributing to the collapse of bridges around the world is bridge scour, whereby sediment is washed away from the bridge foundations by flowing water. Hence, the proper prediction of scour depth is of utmost importance for the proper functioning of the structure and the maintenance schedule. Method Traditional empirical equations, although they found a wide range of applications, often suffer from their inability to represent the nonlinear interactions between the hydraulic, sediment, and geometric factors. To get through these shortcomings, the present study explores the use of the latest machine learning (ML) models for scour depth prediction based on a combined dataset of 569 experimental cases from the U.S. Geological Survey. A very thorough and considered study was done on Random Forest, Gradient Boosting, XGBoost, CatBoost, Support Vector Regression (SVR), Artificial Neural Networks (ANN), and metaheuristically optimized neural networks (ANN-PSO and ANN-GA). The performance of the models was determined by very comprehensive k-fold cross-validation and evaluated through various statistical indices, such as RRMSE, NMAE, NNSE, KGE, WI, and R 2 . In addition, SHapley Additive exPlanations (SHAP) analysis was used to interpret feature contributions, thereby linking model output to physically consistent hydraulic principles. Results and Discussion This work discusses the ensemble learning models, significantly Gradient Boosting, provide an accurate and interpretable framework for scour depth prediction. Specifically, Gradient Boosting (five features) achieved an R 2 of 0.9365, RRMSE of 0.2297, NNSE of 0.9407, and WI of 0.9825. By integrating advanced machine learning techniques with explainability tools, the research bridges the gap between black-box modeling and engineering practice. These findings offer a pathway toward the development of reliable decision-support systems for bridge safety assessment, with potential extensions to field-scale validation, temporal scour evolution modeling, and hybrid physics–data-driven approaches. This work analyses the data and interpret the feature contributions using explainable AI, thereby linking model output to physically consistent hydraulic principles. Such prediction of scour depth supports the engineers to take care of the design and safety assessment frameworks for bridges.