Predicting Equilibrium Scour Depth at Bridge Piers Using Evolutionary Radial Basis Function Neural Network

Abstract

Scouring of bridge piers is a major cause of bridge failure worldwide. Thus, designing safe depths for new bridge foundations and assessing/monitoring the safety of existing bridge foundations are critical to reducing the risk of bridge collapse and the subsequent potential losses in terms of life and property. This paper develops and tests the evolutionary radial basis function neural network (ERBFNN) as a model to forecast scour depth at bridge piers. The ERBFNN is an artificial intelligence (AI) inference model that integrates the radial basis function neural network (RBFNN) and the artificial bee colony (ABC). In the ERBFNN, the RBFNN handles the learning and fitting curves and ABC uses optimization to search for the optimal hidden neuron number