Neural Network assessment for scour depth around bridge piers (No. R855)

Abstract

The mechanism of flow around a pier structure is so complicated that, it is difficult to establish a general empirical model to provide accurate estimation for scour. Interestingly, each of the proposed empirical formula yields good results for a particular data set. In this study, an alternative approach, artificial neural networks (ANN), is proposed to estimate the equilibrium and timedependent scour depth with numerous reliable data base. Numerous ANN models, multi-layer perceptron using back propagation algorithm (MLP/BP) and radial basis using orthogonal least-squares algorithm (RBF/OLS), Bayesian neural Network (BNN) and single artificial Neural Network (SANN) were used. The equilibrium scour depth was modeled as a function of five variables; flow depth, mean velocity, critical flow velocity, mean grain diameter and pier diameter. The time variation of scour depth was also modeled in terms of equilibrium scour depth, equilibrium scour time, scour time, mean flow velocity and critical flow velocity. The training and testing data are selected from the experimental data of several valuable references.