Working Paper Article Version 2 This version is not peer-reviewed

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

Version 1 : Received: 14 June 2019 / Approved: 17 June 2019 / Online: 17 June 2019 (11:03:43 CEST)
Version 2 : Received: 31 August 2019 / Approved: 2 September 2019 / Online: 2 September 2019 (10:29:42 CEST)

How to cite: Amini, A.; Hamidi, S.; Shirzadi, A.; Behmanesh, J.; Akib, S. Efficiency of Artificial Neural Networks in Determining Scour Depth at Composite Bridge Piers. Preprints 2019, 2019060164 Amini, A.; Hamidi, S.; Shirzadi, A.; Behmanesh, J.; Akib, S. Efficiency of Artificial Neural Networks in Determining Scour Depth at Composite Bridge Piers. Preprints 2019, 2019060164

Abstract

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.

Subject Areas

Local Scour; Sediment; Bridge Design; Pier Geometry; ANN

Comments (1)

Comment 1
Received: 2 September 2019
Commenter: Ata Amini
Commenter's Conflict of Interests: Author
Comment: While the paper is under review in a journal, the name of an author has been changed and one is removed. The whole author who contributed in the paper are listed now.
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