Efficiency of artificial neural networks in determining scour depth at composite bridge piers

2020 ◽  
pp. 1-7 ◽  
Author(s):  
Ata Amini ◽  
Shahriar Hamidi ◽  
Ataollah Shirzadi ◽  
Javad Behmanesh ◽  
Shatirah Akib
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Bridge Piers ◽  
Scour Depth ◽  
Composite Bridge ◽  
Artificial Neural

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.

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 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.

scholarly journals Prediction of non-breaking wave induced scour depth at the trunk section of breakwaters using Genetic Programming and Artificial Neural Networks

2017 ◽  
Vol 121 ◽  
pp. 107-118 ◽  
Author(s):  
Ali Pourzangbar ◽  
Miguel A. Losada ◽  
Aniseh Saber ◽  
Lida Rasoul Ahari ◽  
Philippe Larroudé ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Genetic Programming ◽  
Scour Depth ◽  
Breaking Wave ◽  
Artificial Neural ◽  
Wave Induced
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