Deep neural network for pier scour prediction

With the advancement in computing power over last decades, deep neural networks (DNN), consisting of two or more hidden layers with large number of nodes, are being suggested as an alternate to commonly used single-hidden-layer neural networks (ANN). DNN are found to be flexible models with a very large number of parameters, thus making them capable of modelling very complex and highly nonlinear relationships existing between inputs and outputs. This paper investigates the potential of a DNN consisting of 3 hidden layers (100, 80 and 50 nodes) to predict the local scour around bridge piers using field data. To update the weights and bias of DNN, an adaptive learning rate optimization algorithm was used. The dataset consists of 232 pier scour measurements, out of which a total of 154 data were used to train whereas remaining 78 data to test the created model. A correlation coefficient value of 0.957 (root mean square error = 0.306m) was achieved by DNN in comparison to 0.938 (0.388m) by ANN, indicating an improved performance by DNN for scour depth perdition. Encouraging performance on the used dataset in the work suggests the need of more studies on the use of DNN for various civil engineering applications.