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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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    Bridge afflux estimation using artificial intelligence systems
    (ICE PUBL, 2011) Seckin, Galip; Cobaner, Murat; Ozmen-Cagatay, Hatice; Atabay, Serter; Erduran, Kutsi S.
    Most of the methods developed for the prediction of bridge afflux are generally based on either energy or momentum equations. Recent studies have shown that the energy method, which is one of the four bridge subroutines within the commonly used program HEC-RAS for computing water surface profiles along rivers, produced more accurate results than three other methods (momentum, WSPRO and Yarnell's methods) when applied to bridge afflux data obtained from experiments conducted in a two-stage channel. This work developed three artificial intelligence models (the radial basis neural network, the multi-layer perceptron and the adaptive neuro-fuzzy inference system) as alternatives to the energy method. Multiple linear and multiple non-linear regression models were also used in the analysis. The results showed that the performance of the adaptive neuro fuzzy inference system in predicting bridge afflux was superior to the other models.
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    Prediction of backwater level of bridge constriction using an artificial neural network
    (ICE PUBLISHING, 2013) Atabay, Serter; Abdalla, Jamal A.; Erduran, Kutsi S.; Mortula, Maruf; Seckin, Galip
    Bridge constriction in channels usually increases the water level well above the normal depth and may result in overflow on the surrounding floodplain. In this paper, the experimental backwater level at which the maximum afflux value was observed due to bridge constriction was investigated. An artificial neural network (ANN) was used to predict the backwater level based on Manning's roughness coefficient of the main channel (n(mc)) and of the floodplain (n(fp)), bridge width (b) and flow discharge (Q). A multi-layer perceptron (MLP) ANN was used to predict the backwater level using these parameters. Multiple linear (MLR) regression and multiple non-linear regression (MNLR) were used as benchmarks for comparison of ANN results. It is concluded that an ANN can very accurately predict the backwater level. The developed ANN model was then used to conduct a parametric study to investigate the influence of n(mc), n(fp), b and Q on the backwater level due to a bridge constriction without piers. It is concluded that n(mc) and Q have a more profound effect on the backwater level than does n(fp), while b has very little effect on the backwater level within this range of parameters. Other observations and conclusions are also drawn.