ANN approaches for the prediction of bridge backwater using both field and experimental data

Abstract

This paper presents the findings of laboratory model testing of arched bridge constrictions in a rectangular open-channel flume whose bed slope was fixed at zero. Four different types of arched bridge models, namely single-opening semi-circular arch, multiple-opening semi-circular arch, single-opening elliptic arch, and multiple-opening elliptic arch, were used in the testing program. The normal crossing (φ  =  0) and five different skew angles (φ  =  10°, 20°, 30°, 40°, and 50°) were tested for each type of arched bridge model. Recently, a major coverage of backwater field data obtained from the medieval arched bridge constrictions was published by the Hydraulic Research Wallingford in the UK (Brown, P.M., 1985. Hydraulics of bridge waterways: Interium report. Wallingford, UK: Hydraulic Research Wallingford, Report SR 60; Brown, P.M., 1987. Afflux at arch bridges: second interium report. Wallingford, UK: Hydraulic Research Wallingford, Report SR 115; Brown, P.M., 1988. Afflux at arch bridges. Wallingford, UK: Hydraulic Research Wallingford, Report SR 182). These data were also used in the analysis. The main aim of this study is to develop a suitable model for estimating backwater through arched bridge constrictions with normal and skewed crossings using both experimental and field data. Therefore, different artificial intelligence approaches, namely multi-layer perceptron (MLP), radial basis neural network (RBNN), generalized regression neural network (GRNN), and multi-linear and multi-nonlinear regression models, MLR and MNLR, respectively were used. The comparison between these developed models and one of the most commonly used traditional methods (Biery, P.F. and Delleur, J.W., 1962. Hydraulics of single span arch bridge constrictions. ASCE Journal of the Hydraulics Division, 88, 75–108) has been made. The test results showed that the MLP model gave highly accurate results than those of Biery and Delleur, MLR, MNLR, and GRNN and gave similar results with the RBNN model when applied to both field and experimental data.

Keywords:

• Bridge backwater
• arched bridges
• flood control
• artificial neural networks

Acknowledgement

The authors acknowledge the financial support of the Scientific and Technological Research Council of Turkey (TUBITAK) under the project no CAYDAG 106Y308.