Keywords: neural networks, bridge pier, back propagation algorithm, orthogonal least square algorithm, scour depth.

Placing a hydraulic structure in either a river or marine environment will alter the flow patterns in the vicinity of the structure. The changes to the flow pattern cause an increase in sediment movement leading to the phenomenon of scour. Understanding the phenomenon of bridge pier scour is of paramount concern to the hydraulics engineering profession as without this detailed knowledge bridge failures can occur, resulting in loss of life and devastating destruction. From a purely economic standpoint, businesses of all sizes depend on major interstates, city streets and rural roads to move products and services. Therefore, where roads and bridges are temporarily or permanently closed due to damage sustained because of scour, the economy will suffer.

Given the importance of understanding the stability of hydraulic structures exposed to scour, extensive research has been conducted on the mechanisms and dynamics of scour and scour patterns around different objects [1,2]. Blodgett [3] studied 383 bridge failures caused by catastrophic floods. Approximately half of these failures were caused by local scour. Although some of the scour was attributed to the increased local and contraction scour, due to accumulation of ice and debris, a large portion resulted from erroneous prediction of scour depth during engineering design. Among these, 86% of the 577,000 bridges in the National Bridge Registry (NBI) of America are built over waterways. More than 26,000 of these bridges have been found to be scour critical, meaning that the stability of the bridge foundation has been, or could be, affected by the removal of bed materials.

The depth of scour is an important parameter for determining the minimum depth of foundations as it reduces the lateral capacity of the foundation. It is for this reason that extensive experimental investigation has been conducted in an attempt to understand the complex process of scour and to determine a method of predicting scour depth for various pier situations. To date, no generic formula has been developed that can be applied to all pier cases to determine the extent of the scour that will develop. Numerous empirical formulae have been presented to estimate equilibrium scour depth at bridge piers. Each approach varies significantly, highlighting the fact that there is a lack of knowledge in predicting scour depth and that a more universal solution would be beneficial. It is the lack of knowledge in predicting scour depth for all pier conditions that has led to the undertaking of this research.

In this study, an alternative approach, using an artificial neural network (ANN), is proposed for the estimation of local scour around a bridge pier. Three different ANN models were outlined in this paper. They are: the back propagation algorithm (MLP/BP), the radial basis using orthogonal least-squares algorithm (RBF/OLS) and the Bayesian neural Network (BNN) were used. The equilibrium scour depth was modelled as a function of five variables; flow depth, mean velocity, critical flow velocity, mean grain diameter and pier diameter. The time variation of the scour depth was also modelled in terms of the equilibrium scour depth, equilibrium scour time, scour time, mean flow velocity and critical flow velocity. This study includes the manipulation of the collected laboratory data to train and to validate the networks. It shows that the neural network approach predicts scour depth much more accurately than existing methods.

1

J.B. Herbich, "Seafloor Scour: Design Guidelines For Ocean-Founded Structures", Marine Technology Society, New York, 1984.

2

B.M. Sumer, J. Fredsoe, "The Mechanics of Scour in the Marine Environment", World Scientific, 2002.

3

B.A. Blodgett, "Countermeasures for Hydraulic Problems at Bridges", Federal HWY Administration US Department of Transportation, 1, 10-23, 1978.

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