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    3D model for prediction of flow profiles around bridges
    (Taylor & Francis Ltd, 2010) Kocaman, Selahattin; Seckin, Galip; Erduran, Kutsi S.
    Computational fluid dynamics models have become well established as tools for simulating free surface flow over a wide range of structures. This study is an assessment and comparison of the performance of a commercially available three-dimensional numerical software, which solves the Reynolds-averaged Navier-Stokes equations, to predict the free surface profiles from up-to downstream of four different bridge types with and without piers in a compound channel. The model results were compared with the available experimental data. Comparisons indicate that the model provides a reasonably good description of free surface profiles under both gradually and rapidly varied flow conditions in the bridge vicinity, respectively.
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    3D NUMERICAL MODELLING OF FLOW AROUND SKEWED BRIDGE CROSSING
    (Hong Kong Polytechnic Univ, Dept Civil & Structural Eng, 2012) Erduran, Kutsi S.; Seckin, Galip; Kocaman, Selahattin; Atabay, Serter
    This study investigates the performance of commercially available three-Dimensional (3D) numerical software, FLOW-3D, on the prediction of the water surface profiles using a series of experimental data obtained in a two stage channel with skewed bridge crossing. The experiments were carried out for four different types of bridge models with two different skew angles of phi = 30 degrees and phi = 45 degrees. FLOW-3D, which solves the Reynolds-averaged Navier - Stokes equations, was applied to experimental data for the prediction of water surface profiles along the compound channel from upstream to downstream. The comparison of free surface profiles of 3D model showed good agreement with the experimental data. Notably, the measured and computed afflux values are found to be almost identical.
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    ANN approaches for the prediction of bridge backwater using both field and experimental data
    (Taylor & Francis Ltd, 2011) Pinar, Engin; Seckin, Galip; Sahin, Besir; Akilli, Huseyin; Cobaner, Murat; Canpolat, Cetin; Atabay, Serter
    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 (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. 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.
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    Artificial neural network approaches for prediction of backwater through arched bridge constrictions
    (Elsevier Sci Ltd, 2010) Pinar, Engin; Paydas, Kamil; Seckin, Galip; Akilli, Huseyin; Sahin, Besir; Cobaner, Murat; Kocaman, Selahattin
    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.