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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2014
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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. |
format | Online Article Text |
id | pubmed-4142307 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
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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