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Design of Jetty Piles Using Artificial Neural Networks

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 an...

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Detalles Bibliográficos
Autores principales: Lee, Yongjei, Lee, Sungchil, Bae, Hun-Kyun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4142307/
https://www.ncbi.nlm.nih.gov/pubmed/25177724
http://dx.doi.org/10.1155/2014/405401
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author Lee, Yongjei
Lee, Sungchil
Bae, Hun-Kyun
author_facet Lee, Yongjei
Lee, Sungchil
Bae, Hun-Kyun
author_sort Lee, Yongjei
collection PubMed
description 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.
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spelling pubmed-41423072014-08-31 Design of Jetty Piles Using Artificial Neural Networks Lee, Yongjei Lee, Sungchil Bae, Hun-Kyun ScientificWorldJournal Research Article 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. Hindawi Publishing Corporation 2014 2014-08-07 /pmc/articles/PMC4142307/ /pubmed/25177724 http://dx.doi.org/10.1155/2014/405401 Text en Copyright © 2014 Yongjei Lee et al. https://creativecommons.org/licenses/by/3.0/ 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.
spellingShingle Research Article
Lee, Yongjei
Lee, Sungchil
Bae, Hun-Kyun
Design of Jetty Piles Using Artificial Neural Networks
title Design of Jetty Piles Using Artificial Neural Networks
title_full Design of Jetty Piles Using Artificial Neural Networks
title_fullStr Design of Jetty Piles Using Artificial Neural Networks
title_full_unstemmed Design of Jetty Piles Using Artificial Neural Networks
title_short Design of Jetty Piles Using Artificial Neural Networks
title_sort design of jetty piles using artificial neural networks
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4142307/
https://www.ncbi.nlm.nih.gov/pubmed/25177724
http://dx.doi.org/10.1155/2014/405401
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