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dc.contributor.authorMercan, DE
dc.contributor.authorKabdasli, MS
dc.contributor.authorCigizoglu, HK
dc.contributor.authorYagci, O
dc.date.accessioned2021-03-03T08:24:21Z
dc.date.available2021-03-03T08:24:21Z
dc.identifier.citationYagci O., Mercan D., Cigizoglu H., Kabdasli M., "Artificial intelligence methods in breakwater damage ratio estimation", OCEAN ENGINEERING, cilt.32, ss.2088-2106, 2005
dc.identifier.issn0029-8018
dc.identifier.othervv_1032021
dc.identifier.otherav_17593ef4-e8d0-4109-9371-17a22c482f47
dc.identifier.urihttp://hdl.handle.net/20.500.12627/21064
dc.identifier.urihttps://doi.org/10.1016/j.oceaneng.2005.03.004
dc.description.abstractThe anticipation of damage ratio with an acceptable accuracy is a vital issue in breakwater design. The presented study covers the employment of three different artificial neural network methods and a fuzzy model for this problem. Inputs like mean wave period, wave steepness, significant wave height and the breakwater slope are used as input to estimate the corresponding damage ratio value. All artificial neural network methods and fuzzy logic model provided quite close estimations for the experimental values. The testing stage results were significantly superior to the conventional multi-linear regression method in terms of the selected performance criteria. (c) 2005 Elsevier Ltd. All rights reserved.
dc.language.isoeng
dc.subjectTemel Bilimler (SCI)
dc.subjectDeniz Bilimleri ve Teknolojisi
dc.subjectOşinografi
dc.subjectBiyolojik Oşinografi (Deniz Biyolojisi
dc.subjectFiziksel Oşinografi
dc.subjectİnşaat Mühendisliği
dc.subjectMühendislik ve Teknoloji
dc.subjectOŞİNOGRAFİ
dc.subjectYerbilimleri
dc.subjectMÜHENDİSLİK, OCEAN
dc.subjectMÜHENDİSLİK, SİVİL
dc.subjectMühendislik, Bilişim ve Teknoloji (ENG)
dc.subjectMühendislik
dc.subjectMÜHENDİSLİK, DENİZ
dc.titleArtificial intelligence methods in breakwater damage ratio estimation
dc.typeMakale
dc.relation.journalOCEAN ENGINEERING
dc.contributor.department, ,
dc.identifier.volume32
dc.identifier.startpage2088
dc.identifier.endpage2106
dc.contributor.firstauthorID95278


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