Piers and abutments cause local contraction in the rivers. As a result of the contraction, the water level increases in the upstream section of the bridge named as afflux. Afflux is one of the key concerns in the design of a bridge construction and the afflux estimation is important when the specific regions in the upstream of the bridge is to be protected against the flood. Hence, estimation of the afflux is required for river training of the upstream sections, before bridge construction. In this research 54 experiments were conducted to investigate the effects of different parameters such as the pier angle with respect to vertical, the Froude number and the contraction ratio on afflux. Design expert software is used to design the experiments and to analyze the results. According to the results, for a downstream inclined and laterally inclined piers, the afflux increased. However, the afflux decreases due to an upstream inclined pier. Also, the effect of inclination angle on the afflux is smaller for inclined piers towards upstream than inclined pier to the downstream or laterally inclined piers. By increasing the pier angle from zero to 24 degrees, toward channel side walls and toward downstream the afflux increases by 20 and 15 percent, respectively. While in the same situation by increasing the pier angle from zero to 24 degrees toward upstream, the afflux decreases by about 5 percent. The higher affluxes were observed in larger Froude numbers and contraction ratios. The analysis showed that the Froude number and contraction ratio have more effects on the afflux as compare to other parameters. Finally, appropriate equations are presented to estimate the afflux.

Depth of water at the upstream of pier will increase when a bridge is constructed. The difference between water depth before and after the bridge construction is usually termed as afflux.   The purpose of this paper is to apply and evaluate the abilities of Artificial Neural Networks (ANN) to predict this phenomena. Mapping between input data (discharge, normal depth, length to width ratio of bridge pier, contraction ratio and angle of pier axis) and output data (afflux) has been provided by a Radial Basis Function (RBF) ANN based on imperical data produced by experimental measurements. The results show that a RBF artificial neural network including two hidden layers can predict intelligently the afflux and its performance is much better than other conventional approaches.

One of the most significant hydraulic issues in determining the opening of river bridges is the lack of flow choking due to a reduction in the width of the flood passage. In this paper, determining the required opening for flow passage at a bridge location has been investigated using the concept of specific energy, one-dimensional, and three-dimensional flow modeling. First, the maximum encroachment of the embankments on the sides of the bridge in the river has been determined in such a way that it does not change the flow situation upstream of the bridge, using the concept of specific energy. The dimensions obtained for the bridge opening have been simulated numerically in two one-dimensional and three-dimensional models, and the flow condition at the bridge site and upstream has been evaluated and compared. The results showed that the one-dimensional numerical model predicts, on average, 67 percent higher amount of afflux than the three-dimensional model, while the maximum shear stress obtained from the one-dimensional model is, on average, 33 percent lower than that of the three-dimensional model. The effect of the bridge skewness on the amount of afflux and other hydraulic parameters of the flow, including bed shear stress and maximum velocity, has also been investigated using a three-dimensional model. The afflux was obtained at a 19.2 percent of normal depth at a skew of 40 degrees.

The river morphometric has been reviewed by using topographic maps, digital model and field data for determining the role of lithology and tectonic changing resistance on longitudinal profile of Mahabad river catchment area in south part of Urmia Lake. For this purpose, the basin was divided in to 5 (five) parts. Streams were ranked based on Astraler method. In order to determine the fractures, the longitudinal profile of each class was adjusted with geology and tectonic formations from 1: 100000 geology maps. The obtained results showed that in Daras and Agri limits, changing from one resistant lithology to a nonresistant lithology has caused a clear and distinguished fracture in longitudinal profile and the breaks can only be seen in the formation borders which indicate the high effect of lithology on longitudinal profile. In Kootar and Bytas areas, the fractures can be seen inside the lithology and there in no fracture at the formation border. Based on geo tectonic index, Kootar and Bytas limits has more tectonic activities in comparing with the other areas, so that it is understood that the relationship between the afflux due to tectonic and the effect of bed rock on the longitudinal profile is reversed. Therefore tectonic afflux reduces the effect of bed rock on longitudinal profile.

Weirs are one of the common structures for discharge and flow measurement. Therefore, these types of hydraulic structures depending on the purpose of usage, have different shapes. Weirs have been widely used for the purpose of flow measurement and flow control in open channels. Generally they are used as normal weirs. For the purpose of flow diversion, they can also be used as side weirs or skew weirs. Various weirs of modified plan form have been suggested in the past to enhance their discharging capacity with minimum head over the weirs and to restrict the afflux. The aim of this study is to apply different methods to investigate the discharging capacity of a sharp-crested curved plan-form weirs under free flow conditions using original experimental dataset through the Artificial Neural Networks (ANNs) and Genetic Expression Programming (GEP) techniques. Subsequently, for training and testing of the proposed equation, experimental data of Kumar et al. have been used. A preliminary investigation on various GEP operators is also carried out for selecting the proper operators. The obtained results indicate that applied machine learning techniques have reliable performance in predicting discharging capacity of a sharp-crested curved plan-form weirs. Comparison of results obtained from this equation with the experimental data reveals high accuracy of the new equation of genetic programming and result of the ANNs. Determination coefficient of the proposed equation for discharge coefficient have been calculated as 0. 956 and 0. 924 for the model with best functions (F2 and F4), also this parameter calculated for ANNs as 0. 962 for testing phase.

This study is an independent and a comparative research concerning the accuracy, capability and suitability of three well known packages of ISIS, MIKEI 1 and HEC-RAS as hydraulic river modeling software packages for modeling the flow through bridges. The research project was designed to assess the ability of each software package to model the flow through bridge structures. It was carried out using the data taken From experiments completed by a 22-meter laboratory flume at the University of Birmingham. The flume has a compound cross section containing a main channel and two flood plains on either side. For this study a smooth main channel and a smooth floodplain have been assumed. Two types of bridges are modeled in this research; a multiple opening semi-circular arch bridge and a single opening straight deck bridge. For each bridge, two different simulations were carried out using two different upstream boundaries as low flow and high. flow simulations. According to the results, all three packages were able to model arch and US BPR bridges but in some cases they presented different results. The highest water elevation upstream the bridge (maximum afflux) was the main parameter to be compared to the measured values. ISIS and HEC-RAS (especially HEC-RAS) seem to be more efficient to model arch bridge. However, in some cases, MIKE 11 produced considerably higher results than those of the other two packages. To model USBPR bridge, all three packages produced reasonable results. However, the results by HEC-RAS are the best when the outputs are compared to the experimental data.