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

Authors:
Engin Pinar;Kamil Paydas;Galip Seckin;Huseyin Akilli;Besir Sahin;Murat Cobaner;Selahattin Kocaman;M. Atakan Akar
Affiliations:
Department of Mechanical Engineering, Cukurova University, 01330 Balcali/Adana, Turkey;Department of Mechanical Engineering, Cukurova University, 01330 Balcali/Adana, Turkey;Department of Civil Engineering, Cukurova University, 01330 Balcali/Adana, Turkey;Department of Mechanical Engineering, Cukurova University, 01330 Balcali/Adana, Turkey;Department of Mechanical Engineering, Cukurova University, 01330 Balcali/Adana, Turkey;Department of Civil Engineering, Erciyes University, 38039 Kayseri, Turkey;Department of Civil Engineering, Mustafa Kemal University, 31024 Antakya/Hatay, Turkey;Department of Mechanical Engineering, Cukurova University, 01330 Balcali/Adana, Turkey
Venue:
Advances in Engineering Software
Year:
2010

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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 (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 (@f=0), and five different skew angles (@f=10^o, 20^o, 30^o, 40^o, and 50^o) 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, MLR, and MNLR approaches. The MLP produced more accurate predictions than those of the others.