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Abstract Scouring refers to the process by which bed sediment in a river is eroded around the periphery of a bridge abutment or pier. Many empirical models are available to estimate the scour depth for different flow, geometry, and bed roughness condition. However, none of them provide a better estimation of scour depth for a wide range of input parameters. Thus, in this paper, the scour depth around bridge piers has been modelled using M5 tree and hybrid artificial neural network (ANN)-particle swarm optimisation (PSO) techniques by considering the wide range of datasets. The clear-water scouring (CWS) datasets are collected from the literature and five different non-dimensional influencing parameters are selected as input parameters to model the scour depth. A Gamma test (GT) was performed to choose the best input parameter combinations. Based on the lowest gamma value and V-ratio, 4 out of 26 distinct input combinations for CWS depth modelling were chosen in the GT. According to statistical measures, the proposed M5 tree model predicts scour depth better than empirical approaches. Additionally, the developed ANN-PSO model is suitable for determining scour depth in both rectangular and circular shapes of piers. The results of both developed models are compared with other existing models and found to be satisfactory.
Kumar et al. (Tue,) studied this question.