Artificial neural network approaches for prediction of backwater through arched bridge constrictions

PINAR E., Paydas K., SEÇKİN G., AKILLI H., ŞAHİN B., Cobaner M., ...More

ADVANCES IN ENGINEERING SOFTWARE, vol.41, no.4, pp.627-635, 2010 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 41 Issue: 4
  • Publication Date: 2010
  • Doi Number: 10.1016/j.advengsoft.2009.12.003
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.627-635
  • Keywords: Artificial neural network methods, Backwater, Bridges, Flood control, CHANNEL FLOW, SCOUR DEPTH, FUZZY, AFFLUX, FEED
  • Çukurova University Affiliated: Yes


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 (SOSC), multiple opening semi-circular arch (MOSC), single opening elliptic arch (SOE), and multiple opening elliptic arch (MOE), were used in the testing program. The normal crossing (phi = 0), and five different skew angles (phi = 10 degrees, 20 degrees, 30 degrees, 40 degrees, and 50 degrees) were tested for each type of arched bridge model. The main aim of this study is to develop a suitable model for estimating backwater through arched bridge constrictions with normal and skewed crossings. Therefore, different artificial neural network 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. Results of these experimental studies were compared with those obtained by the MLP, RBNN, GRNN, MILK and MNLR approaches. The MLP produced more accurate predictions than those of the others. Crown Copyright (C) 2009 Published by Elsevier Ltd. All rights reserved.