  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.
