Flow mechanism around a bridge pier is complicated and difficult to present a general model to provide a good prediction of scour depth. Geotechnical and economical parameters govern design of complex bridge pier design. The interaction between flow parameters and complex bridge pier is necessary to study to accurately predict the performance of system. In this study, an artificial neural network (ANN) has been developed to predict scour depth around complex bridge pier. ANN model, feed forward back propagation, FFBP, was utilized to estimate the depth of scour hole. 82 experiments have been carried out to collect experimental data. The training and testing experimental data on local scour depth around complex piers are selected from several references. Three categories of input data were used for network training: the first input combination includes cases that pile cap was above the original bed; the second combinations refers to semi buried pile cap, both combinations contains 15 dimensional parameters; and the third combination consists of cases that pile cap was below the original bed level which contains 8 dimensional parameters. ANN results have been compared with the results of empirical methods. Sensitivity analysis showed that pile spacing width, longitudinal extension of pile group from column, and column length have the least influence on scour depth. While the number of piles in line with flow, transversal extension of pile cap from column, and pile cap length are the most effective parameters on complex pier scour in each combination respectively. Based upon an errors and sensitivity analyses it may be said, the collected data and method of analysis could be reliable to use.

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