Neural network for design and reliability analysis of rubble mound breakwaters SCIE SCOPUS

DC Field Value Language
dc.contributor.author Kim, DH -
dc.contributor.author Park, WS -
dc.date.accessioned 2020-04-20T13:55:38Z -
dc.date.available 2020-04-20T13:55:38Z -
dc.date.created 2020-01-28 -
dc.date.issued 2005-08 -
dc.identifier.issn 0029-8018 -
dc.identifier.uri https://sciwatch.kiost.ac.kr/handle/2020.kiost/5042 -
dc.description.abstract Artificial neural networks were applied to the design of rubble mound breakwater. Five neural networks with different network structures were trained with the same training data. Then they were compared with conventional empirical model and one another. It was found that the neural network technique gives more accurate results than conventional empirical model and the extent of accuracy can be affected by the structure of neural network. After that, how to integrate the trained neural network into reliability analysis technique is proposed. Since the neural network technique shows better performance than empirical model based approach in breakwater design, it is expected that the neural network integrated reliability analysis gives more improved results for probability of failure than it is done with empirical model. (c) 2005 Elsevier Ltd. All rights reserved. -
dc.description.uri 1 -
dc.language English -
dc.publisher PERGAMON-ELSEVIER SCIENCE LTD -
dc.title Neural network for design and reliability analysis of rubble mound breakwaters -
dc.type Article -
dc.citation.endPage 1349 -
dc.citation.startPage 1332 -
dc.citation.title OCEAN ENGINEERING -
dc.citation.volume 32 -
dc.citation.number 11-12 -
dc.contributor.alternativeName 박우선 -
dc.identifier.bibliographicCitation OCEAN ENGINEERING, v.32, no.11-12, pp.1332 - 1349 -
dc.identifier.doi 10.1016/j.oceaneng.2004.11.008 -
dc.identifier.scopusid 2-s2.0-18744398739 -
dc.identifier.wosid 000229661500004 -
dc.type.docType Article -
dc.description.journalClass 1 -
dc.subject.keywordAuthor breakwater -
dc.subject.keywordAuthor stability -
dc.subject.keywordAuthor neural network -
dc.subject.keywordAuthor reliability based design -
dc.subject.keywordAuthor Monte Carlo simulation -
dc.relation.journalWebOfScienceCategory Engineering, Marine -
dc.relation.journalWebOfScienceCategory Engineering, Civil -
dc.relation.journalWebOfScienceCategory Engineering, Ocean -
dc.relation.journalWebOfScienceCategory Oceanography -
dc.description.journalRegisteredClass scie -
dc.description.journalRegisteredClass scopus -
dc.relation.journalResearchArea Engineering -
dc.relation.journalResearchArea Oceanography -
Appears in Collections:
Marine Industry Research Division > Ocean Space Development & Energy Research Department > 1. Journal Articles
Files in This Item:
There are no files associated with this item.

qrcode

Items in ScienceWatch@KIOST are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse