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The Scientific World Journal
Volume 2014, Article ID 405401, 12 pages
Research Article

Design of Jetty Piles Using Artificial Neural Networks

1Port and Harbor Team, Seoyeong Engineering, Republic of Korea
2Department of Computer Design, School of Engineering and Agriculture, Ulaanbaatar University, Mongolia
3Department of Global Environment, School of Environment, Keimyung University, 203 Osan Hall, Dalgubul-Daero, Dalsegu, Daegu 1095, Republic of Korea

Received 30 April 2014; Accepted 19 July 2014; Published 7 August 2014

Academic Editor: Jie Zhou

Copyright © 2014 Yongjei Lee et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


To overcome the complication of jetty pile design process, artificial neural networks (ANN) are adopted. To generate the training samples for training ANN, finite element (FE) analysis was performed 50 times for 50 different design cases. The trained ANN was verified with another FE analysis case and then used as a structural analyzer. The multilayer neural network (MBPNN) with two hidden layers was used for ANN. The framework of MBPNN was defined as the input with the lateral forces on the jetty structure and the type of piles and the output with the stress ratio of the piles. The results from the MBPNN agree well with those from FE analysis. Particularly for more complex modes with hundreds of different design cases, the MBPNN would possibly substitute parametric studies with FE analysis saving design time and cost.