Please use this identifier to cite or link to this item: https://hdl.handle.net/11499/6724
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dc.contributor.authorFirat, Mahmut-
dc.date.accessioned2019-08-16T12:10:02Z-
dc.date.available2019-08-16T12:10:02Z-
dc.date.issued2009-
dc.identifier.issn1741-7589-
dc.identifier.urihttps://hdl.handle.net/11499/6724-
dc.identifier.urihttps://doi.org/10.1680/wama.2009.00061-
dc.description.abstractAccurate and reliable prediction of scour depth around bridge piers is important due to the complexity of the scour process. In this study, an adaptive neuro-fuzzy inference system (Anfis) approach is used to predict the scour depth around circular bridge piers. In particular, the applicability of the Anfis method as a prediction model for scour depth is investigated. A total of 165 data records are used to predict equilibrium scour depth from various experimental studies. Two different models are constructed for the prediction. The first comprises a combination of dimensional data, whereas the second includes non-dimensional input variables. The performance of the Anfis models in training and testing sets is compared with observations. The models are also compared with a radial basis neural network (RBNN), existing scour depth equations and multiple linear regression (MLR). The results of the Anfis models, RBNN, MLR and existing scour depth equations are all compared to yield a more reliable evaluation. The results show that the Anfis method can provide high accuracy and reliability for the prediction of scour depth around circular bridge piers. © 2009 Thomas Telford.en_US
dc.language.isoenen_US
dc.relation.ispartofProceedings of the Institution of Civil Engineers: Water Managementen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBridgesen_US
dc.subjectMathematical modellingen_US
dc.subjectAdaptive neuro-fuzzy inference systemen_US
dc.subjectANFIS methoden_US
dc.subjectANFIS modelen_US
dc.subjectCircular bridgesen_US
dc.subjectData recordsen_US
dc.subjectExperimental studiesen_US
dc.subjectInput variablesen_US
dc.subjectMultiple linear regressionsen_US
dc.subjectPrediction modelen_US
dc.subjectRadial basis neural networksen_US
dc.subjectScour depthen_US
dc.subjectScour processen_US
dc.subjectTraining and testingen_US
dc.subjectBridge piersen_US
dc.subjectComputer simulationen_US
dc.subjectErosionen_US
dc.subjectFuzzy inferenceen_US
dc.subjectFuzzy systemsen_US
dc.subjectMathematical modelsen_US
dc.subjectPiersen_US
dc.subjectScouren_US
dc.subjectbridgeen_US
dc.subjectdepthen_US
dc.subjectequationen_US
dc.subjectexperimental studyen_US
dc.subjectmodel testen_US
dc.subjectnumerical modelen_US
dc.subjectpieren_US
dc.subjectpredictionen_US
dc.subjectregression analysisen_US
dc.subjectscouren_US
dc.subjectsediment yielden_US
dc.titleScour depth prediction at bridge piers by Anfis approachen_US
dc.typeArticleen_US
dc.identifier.volume162en_US
dc.identifier.issue4en_US
dc.identifier.startpage279-
dc.identifier.startpage279en_US
dc.identifier.endpage288en_US
dc.authorid0000-0002-8010-9289-
dc.identifier.doi10.1680/wama.2009.00061-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopus2-s2.0-69749124125en_US
dc.identifier.wosWOS:000272397900006en_US
dc.identifier.scopusqualityQ2-
dc.ownerPamukkale University-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.openairetypeArticle-
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.grantfulltextnone-
crisitem.author.dept10.02. Civil Engineering-
Appears in Collections:Mühendislik Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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