Analysis of experimental data sets for local scour depth around bridge abutments using artificial neural networks

N Şarlak,Ş Tiğrek

doi:10.4314/wsa.v37i4.19

Abstract

The performance of soft computing techniques to analyse and interpret the experimental data of local scour depth around bridge abutment, measured at different laboratory conditions and environment, is presented. The scour around bridge piers and abutments is, in the majority of cases, the main reason for bridge failures. Therefore, many experimental and theoretical studies have been conducted on this topic. This study sought to answer the following questions: Firstly, can data collected by different researchers at different times be combined in one data set? Secondly, can we determine any unquantified effects such as data differences, laboratory conditions and measurement devices? Artificial neural networks (ANN) are used and a basic ANN model is selected to observe the application problems, in order to avoid any misleading conclusion arising due to the model parameters selected and the compilation of different subsets of experimental data into one set. At the first stage, seven experimental data sets are compiled to address the first question and an ANN model is used to discovery any existing discrepancies between available data groups. The importance of selected model parameters for the model’s performance was demonstrated by increasing the number of parameters. Then, each data subset was inspected to expose the importance of the homogeneity of data groups in order to obtain a best-fit ANN model. Finally, a sensitivity analysis was carried out to obtain the dominant parameters of the problem. It was concluded that the use of ‘soft’ computational techniques such as ANN can be beneficial, provided the user is aware of the heterogeneity of the data set and the physical context of the subject or problem being addressed. However, as with other data analysis techniques, elaborate inspection of data and results is required. Keywords: Scour prediction methods, bridge abutment scour, soft computing techniques, ANN

Highlights

Over the past few decades, statistical studies have shown that the most common cause of bridge failures is the removal of bed material around bridge foundations (Yanmaz, 2002)
An Artificial neural networks (ANN) model consists of 2 main components, the first is the structure of the model and the second is the selection of the learning algorithm
Some of the learning algorithms presented in the literature include: back-propagation, feed forward back-propagation (FFBP), feed forward cascade correlation (FFCC), radial basis function (RBF), Levenberg-Marquardt, quasi-Newton, conjugate gradients, Powel-Beale, etc

Summary

Introduction

Over the past few decades, statistical studies have shown that the most common cause of bridge failures is the removal of bed material around bridge foundations (Yanmaz, 2002). General scour involves the removal of material from the bed and banks across all or most of the width of a channel This type of scour, natural or man-induced, needs both sediment and geomorphologic analysis. Flow pattern and mechanism of local scour around a pier and abutment are complex phenomena resulting from the strong interaction of the 3-dimensional turbulent flow field around the bridge foundations and the erodible sediment bed. For instance Federal Highway Administration (FHWA) of the USA studied about 383 bridge failures resulting from catastrophic floods (FHWA, 2001) and showed that 25% of the failures involved pier damage while 75% included abutment scour.

Methods

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Discussion

Conclusion