신경망을 활용한 사석식 방파제의 파괴확률예측 SCOPUS KCI

DC Field Value Language
dc.contributor.author 김동현 -
dc.contributor.author 박우선 -
dc.contributor.author 한상훈 -
dc.date.accessioned 2020-04-21T06:40:13Z -
dc.date.available 2020-04-21T06:40:13Z -
dc.date.created 2020-02-10 -
dc.date.issued 2003 -
dc.identifier.issn 1598-141X -
dc.identifier.uri https://sciwatch.kiost.ac.kr/handle/2020.kiost/5566 -
dc.description.abstract A new approach to reliability analysis of rubble mound breakwater using neural network is proposed. At first, a neural network model which can estimate the stability number of any breakwaters for some design conditions is trained. Then, the neural network model is integrated with Monte Carlo simulation technique in order to calculate probability of failure for the breakwater. The proposed technique is compared with conventional approach using empirical formula. -
dc.description.uri 3 -
dc.title 신경망을 활용한 사석식 방파제의 파괴확률예측 -
dc.title.alternative Prediction of Failure Probability of Breakwater using Neural Network -
dc.type Article -
dc.citation.endPage 351 -
dc.citation.startPage 347 -
dc.citation.title Ocean and Polar Research -
dc.citation.volume 25 -
dc.citation.number 3s -
dc.contributor.alternativeName 김동현 -
dc.contributor.alternativeName 박우선 -
dc.contributor.alternativeName 한상훈 -
dc.identifier.bibliographicCitation Ocean and Polar Research, v.25, no.3s, pp.347 - 351 -
dc.identifier.kciid ART000993155 -
dc.description.journalClass 3 -
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.subject.keywordAuthor 방파제 -
dc.subject.keywordAuthor 안정성 -
dc.subject.keywordAuthor 신경망 -
dc.subject.keywordAuthor 신뢰성설계 -
dc.subject.keywordAuthor 몬테카를로 모사기법 -
dc.description.journalRegisteredClass scopus -
dc.description.journalRegisteredClass kci -
Appears in Collections:
Marine Industry Research Division > Ocean Space Development & Energy Research Department > 1. Journal Articles
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