scholarly journals PREDICTION OF SCOUR DEPTH AROUND BRIDGE PILES USING ARTIFICIAL NEURAL NETWORKS

2009 ◽  
Vol 37 (2) ◽  
pp. 257-268
Author(s):  
K. A. Amen ◽  
Yasser M. R
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Scour Depth ◽  
Artificial Neural

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

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.

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