Engin Pinar

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

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.

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

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.

The Passive Control of Unsteady Flow Structure Downstream of a Circular Cylinder in Shallow Water

In the present study, it was aimed to suppress the vortex shedding occurred in the near wake of a circular cylinder (inner cylinder) by perforated cylinder (outer cylinder) in shallow water flow. The inner cylinder (Di) and outer cylinder (Do) have fixed diameters, such as Di = 50 mm and Do = 100 mm, respectively. The effect of porosity, β, was examined using four different porosity ratios, 0.3, 0.5, 0.6 and 0.8. In order to investigate the effect of arc angle of outer cylinder, α, four different arc angles, α = 360°, 180°, 150° and 120° were used. The experiments were implemented in a recirculating water channel using the particle image velocimetry, PIV technique. The depth-averaged free-stream velocity was kept constant as U∞ = 100 mm/s which corresponded to a Reynolds number of Re = 5000 based on the inner cylinder diameter. The results demonstrated that the suppression of vortex shedding is substantially achieved by perforated outer cylinder for arc angle of α = 360° at β = 0.6. Turbulence Kinetic Energy statistics show that porosity, β, is highly effective on the flow structure. In comparison with the values obtained from the case of the bare cylinder, at porosity β = 0.6, turbulence characteristics are reduced by %80. Also, the point, which the values of maximum TKE, shift to a farther downstream compared to the case of bare cylinder.Copyright