An Artificial Intelligence-Driven Evaluation of Scour Depth Around Bridge Piers

Accurate estimation of scour depth around bridge piers remains a challenging task due to the complex interaction between flow hydraulics and sediment dynamics; however, it is vital to safeguard bridge stability and reduce economic and human losses. Empirical relations are insufficient to satisfactorily simulate this very complex phenomenon. This study proposes intelligent models for the estimation of scour depth around bridge piers using three different modelling approaches, namely Multilayer Perceptron Artificial Neural Networks (MLP-ANN), Radial Basis Function Networks (RBFN), and Multigene Genetic Programming (MGGP). In addition, dimensional analysis was used to reach dimensionless quantities, simplifying the complex relations and also improving the accuracy of the models. The ANN model achieved the highest accuracy, with an R² value of 0.94, an RMSE of 0.032, and a WI of 0.97, indicating an excellent alignment with the observed data. The MGGP model yielded an R² of 0.91, demonstrating balanced performance in multiple statistical metrics. In contrast, the Basis Radial Function (BRF) model, although robust, employed a more conservative estimation approach, with an R² of 0.86, and exhibited limited sensitivity to extreme values. The results of the sensitivity analysis revealed that the bedload transport rate, dimensionless time, and depth-to-width ratio are critical parameters in scour depth calculations, which tangibly confirms the ability of ANNs and dimensional analysis to improve anti-scouring in designs and maintenance, and reduce the failure risk of structures. The findings highlight the effectiveness of machine learning in enhancing hydraulic prediction and improving the resilience of bridge design.