Assessment of artificial intelligence‐ based techniques for the estimation of pile group scour depth

The scour phenomenon around piles is regarded as one of the main causes of serious damages to the pile‐supported structures such as bridges, jetties, wind turbines, and offshore platforms threatening their stability and sustainability in the long term. Thus, accurate forecast of scouring is vital for the design and operation of these structures. In this paper, three artificial intelligence‐based techniques including support vector regression, artificial neural network and random forest were applied to predict the local scour depth around pile groups. An experimental dataset is collected and used to construct the machine learning‐based models. The sediment number, shields parameter spacing, Keulegan‐Carpenter number and pile Reynolds number were used as input variables for the model development. Results assessment indicate that the artificial neural network model anticipated the highest performance among the three machine learning based models, with coefficient of determination of 0.97, and root mean square error of 0.15.