Scour modeling using deep neural networks based on hyperparameter optimization

Abstract

Design of bridge piers and abutments is significantly impacted by hydrodynamic processes that cause scouring of the foundation. Although, many empirical formulae are available in the literature to estimate the depth of scouring, but they suffer from several limitations. A major limitation of empirical formulae is that they are largely applicable to the hydraulic conditions for which they have been derived. In this research, a deep neural network (DNN) has been developed and applied to predict the depth of scour around bridge piers and abutments. The practicality of the proposed model has been demonstrated using the experimental data sets consisting of 211 data points. The novelty of the DNN model applied herein lies in the use of Adam Optimizer for optimizing the parameters of the DNN model. The performance of the DNN model was evaluated for each parameter set using statistical indicators such as the coefficient of determination, root mean square error, and mean absolute error. A regression equation based upon the available data set has also been proposed. Based upon the values of the statistical parameters, the DNN model has been found to be significantly better than the regression model. The model proposed herein performs better than the regression model. A distinct practical advantage of the model proposed herein is that it eliminates the need of hit and trial procedure to determine the optimal parameter set for the model.