Neural Network Modeling for Estimation of Scour Depth Around Bridge Piers

It is essential to predict the scour depth around bridge piers for hydraulic engineers involved in the economical design of bridge pier foundation. Conventional investigations have long been of the opinion that empirical scour prediction equations based on laboratory data over predict scour depths. In this article, the Back-Propagation Neural Network (BPN) was applied to predict the scour depth in order to overcome the problem of exclusive and the nonlinear relationships. The observations obtained from thirteen states in USA was verified by the present model. From the comparison with conventional experimental methods, it can be found that the scour depth around bridge piers can be efficiently predicted using the BPN.