scholarly journals Prediction of local scour depth at bridge piers under clear-water and live-bed conditions: comparison of literature formulae and artificial neural networks

2011 ◽  
Vol 13 (4) ◽  
pp. 812-824 ◽  
Author(s):  
E. Toth ◽  
L. Brandimarte
Keyword(s):  
Neural Networks ◽  
Sediment Transport ◽  
Field Data ◽  
Local Scour ◽  
Bridge Piers ◽  
Validation Dataset ◽  
Scour Depth ◽  
Clear Water ◽  
Transport Mode ◽  
Artificial Neural

The scouring effect of the flowing water around bridge piers may undermine the stability of the structure, leading to extremely high direct and indirect costs and, in extreme cases, the loss of human lives. The use of Artificial Neural Network (ANN) models has been recently proposed in the literature for estimating the maximum scour depth around bridge piers: this study aims at further investigating the potentiality of the ANN approach and, in particular, at analysing the influence of the experimental setting (laboratory or field data) and of the sediment transport mode (clear water or live bed) on the prediction performances. A large database of both field and laboratory observations has been collected from the literature for predicting the maximum local scour depth as a function of a parsimonious set of variables characterizing the flow, the sediments and the pier. Neural networks with an increasing degree of specialization have been implemented – using different subsets of the calibration data in the training phase – and validated over an external validation dataset. The results confirm that the ANN scour depths' predictions outperform the estimates obtained by empirical formulae conventionally used in the literature and in the current engineering practice, and demonstrate the importance of taking into account the differences in the type of available data – laboratory or field data – and the sediment transport mode – clear water or live bed conditions.

Application of Artificial Neural Networks in Assessing the Equilibrium Depth of Local Scour Around Bridge Piers

2015 ◽  
Author(s):  
Ehsan Sarshari ◽  
Philippe Mullhaupt
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Empirical Models ◽  
Local Scour ◽  
Bridge Piers ◽  
Local Conditions ◽  
The Neural Network ◽  
Artificial Neural

Scour can have the effect of subsidence of the piers in bridges, which can ultimately lead to the total collapse of these systems. Effective bridge design needs appropriate information on the equilibrium depth of local scour. The flow field around bridge piers is complex so that deriving a theoretical model for predicting the exact equilibrium depth of local scour seems to be near impossible. On the other hand, the assessment of empirical models highly depends on local conditions, which is usually too conservative. In the present study, artificial neural networks are used to estimate the equilibrium depth of the local scour around bridge piers. Assuming such equilibrium depth is a function of five variables, and using experimental data, a neural network model is trained to predict this equilibrium depth. Multilayer neural networks with backpropagation algorithm with different learning rules are investigated and implemented. Different methods of data normalization besides the effect of initial weightings and overtraining phenomenon are addressed. The results show well adoption of the neural network predictions against experimental data in comparison with the estimation of empirical models.

scholarly journals Efficiency of Artificial Neural Networks in Determining Scour Depth at Composite Bridge Piers

2019 ◽  
Author(s):  
Ata Amini ◽  
Shahriar Hamidi ◽  
Marlinda Malek ◽  
Thamer Mohammad ◽  
Ataollah Shirzadi ◽  
...  
Keyword(s):  
Experimental Data ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Bridge Piers ◽  
Scour Depth ◽  
Ann Model ◽  
Composite Bridge ◽  
Artificial Neural ◽  
Bridge Failure ◽  
Propagation Network

Scouring is the most common cause of bridge failure. This study was conducted to evaluate the efficiency of the Artificial Neural Networks (ANN) in determining scour depth around composite bridge piers. The experimental data, attained in different conditions and various pile cap locations, were used to obtain the ANN model and to compare the results of the model with most well-known empirical, HEC-18 and FDOT, methods. The data were divided into training and evaluation sets. The ANN models were trained using the experimental data, and their efficiency was evaluated using statistical test. The results showed that to estimate scour at the composite piers, feedforward propagation network with three neurons in the hidden layer and hyperbolic sigmoid tangent transfer function was with the highest accuracy. The results also indicated a better estimation of the scour depth by the proposed ANN than the empirical methods.

Reduction of scouring depth by using inclined piers

2010 ◽  
Vol 37 (12) ◽  
pp. 1621-1630 ◽  
Author(s):  
Zafer Bozkus ◽  
Murat Çeşme
Keyword(s):  
Experimental Study ◽  
Dimensional Analysis ◽  
Local Scour ◽  
Bridge Piers ◽  
Scour Depth ◽  
Regression Analyses ◽  
Clear Water ◽  
Design Engineers ◽  
Water Conditions ◽  
Scouring Depth

The aim of this experimental study is to examine the effect of inclination of dual bridge piers on scour depth under clear-water conditions for various uniform flow depths. Duration of 4 h was used in the experiments for each run. Scour depths were measured at four different points around the piers. The depths of local scour around inclined piers were found to be substantially smaller than the scour depths around vertical piers. Dimensional and nondimensional curves were developed and presented to show the variation of scour depth with relevant parameters obtained in the dimensional analysis. Results of the study were compared to those obtained from a similar study performed with single inclined piers to see the effect of the second pier on scour depths. Useful equations for the design engineers were developed based on multiple regression analyses, to be used for predicting local scour depths around vertical and (or) inclined piers in uniform and (or) nonuniform sediments. Normalized scour depths measured around the vertical piers in the present study were compared with those computed by an equation suggested by Melville and Sutherland (1988), and also by an equation developed in the present study.

Evaluation of Selected Pier-Scour Equations Using Field Data

1996 ◽  
Vol 1523 (1) ◽  
pp. 186-195 ◽  
Author(s):  
Mark N. Landers ◽  
David S. Mueller
Keyword(s):  
Field Measurements ◽  
Bridge Piers ◽  
Critical Ratio ◽  
Scour Depth ◽  
Flow Depth ◽  
Clear Water ◽  
Pier Scour ◽  
Logarithmic Space ◽  
State Highway ◽  
Federal Highway

Field measurements of channel scour at bridges are needed to improve the understanding of scour processes and the ability to accurately predict scour depths. An extensive data base of pier-scour measurements has been developed over the last several years in cooperative studies between state highway departments, the Federal Highway Administration, and the U.S. Geological Survey. Selected scour processes and scour design equations are evaluated using 139 measurements of local scour in live-bed and clear-water conditions. Pier-scour measurements were made at 44 bridges around 90 bridge piers in 12 states. The influence of pier width on scour depth is linear in logarithmic space. The maximum observed ratio of pier width to scour depth is 2.1 for piers aligned to the flow. Flow depth and scour depth were found to have a relation that is linear in logarithmic space and that is not bounded by some critical ratio of flow depth to pier width. Comparisons of computed and observed scour depths indicate that none of the selected equations accurately estimate the depth of scour for all of the measured conditions. Some of the equations performed well as conservative design equations; however, they overpredict many observed scour depths by large amounts. Some equations fit the data well for observed scour depths less than about 3 m (9.8 ft), but significantly underpredict larger observed scour depths.

scholarly journals Nose-Angle Bridge Piers as Alternative Countermeasures for Local Scour Reduction

2018 ◽  
Vol 13 (2) ◽  
pp. 110-120 ◽  
Author(s):  
Ibtesam Abudallah Habib ◽  
Wan Hanna Melini Wan Mohtar ◽  
Atef Elsaiad ◽  
Ahmed El-Shafie
Keyword(s):  
Froude Number ◽  
Local Scour ◽  
Bridge Piers ◽  
Scour Depth ◽  
Skew Angle ◽  
Flow Angle ◽  
Sediment Size ◽  
Low Froude Number ◽  
Skew Angles ◽  
Depth Reduction

This study investigates the performance nose-angle piers as countermeasures for local scour reduction around piers. Four nose angles were studied, i.e., 90°, 70°, 60° and 45° and tested in a laboratory. The sediment size was fixed at 0.39 mm whereas the flow angle of attack (or skew angle) was varied at four angles, i.e., skew angles, i.e., 0°, 10°, 20° and 30°. Scour reduction was clear when decreasing nose angles and reached maximum when the nose angle is 45°. Increasing the flow velocity and skew angle was subsequently increasing the scour profile, both in vertical and transversal directions. However, the efficiency of nose angle piers was only high at low Froude number less than 0.40 where higher Froude number gives minimal changes in the maximum scour depth reduction. At a higher skew angle, although showed promising maximum scour depth reduction, the increasing pier projected width resulted in the increase of transversal lengths.

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