Artificial neural network approaches for prediction of backwater through arched bridge constrictions

Basit Öğe Kaydı
dc.authoridSahin, Besir/0000-0003-0671-0890
dc.authoridCobaner, Murat/0000-0002-3476-7512
dc.authoridKocaman, Selahattin/0000-0001-8918-0324
dc.authoridakar, mustafa/0000-0002-0192-0605
dc.contributor.authorPinar, Engin
dc.contributor.authorPaydas, Kamil
dc.contributor.authorSeckin, Galip
dc.contributor.authorAkilli, Huseyin
dc.contributor.authorSahin, Besir
dc.contributor.authorCobaner, Murat
dc.contributor.authorKocaman, Selahattin
dc.date.accessioned2024-09-18T20:02:40Z
dc.date.available2024-09-18T20:02:40Z
dc.date.issued2010
dc.departmentHatay Mustafa Kemal Üniversitesien_US
dc.description.abstractThis paper presents the findings of laboratory model testing of arched bridge constrictions in a rectangular open channel flume whose bed slope was fixed at zero. Four different types of arched bridge models, namely single opening semi-circular arch (SOSC), multiple opening semi-circular arch (MOSC), single opening elliptic arch (SOE), and multiple opening elliptic arch (MOE), were used in the testing program. The normal crossing (phi = 0), and five different skew angles (phi = 10 degrees, 20 degrees, 30 degrees, 40 degrees, and 50 degrees) were tested for each type of arched bridge model. The main aim of this study is to develop a suitable model for estimating backwater through arched bridge constrictions with normal and skewed crossings. Therefore, different artificial neural network approaches, namely multi-layer perceptron (MLP), radial basis neural network (RBNN), generalized regression neural network (GRNN), and multi-linear and multi-nonlinear regression models, MLR and MNLR, respectively were used. Results of these experimental studies were compared with those obtained by the MLP, RBNN, GRNN, MILK and MNLR approaches. The MLP produced more accurate predictions than those of the others. Crown Copyright (C) 2009 Published by Elsevier Ltd. All rights reserved.en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) [CAYDAG 106Y308]en_US
dc.description.sponsorshipThe authors acknowledge the financial support of the Scientific and Technological Research Council of Turkey (TUBITAK) under the Project No. CAYDAG 106Y308.en_US
dc.identifier.doi10.1016/j.advengsoft.2009.12.003
dc.identifier.endpage635en_US
dc.identifier.issn0965-9978
dc.identifier.issn1873-5339
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-74449091089en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage627en_US
dc.identifier.urihttps://doi.org/10.1016/j.advengsoft.2009.12.003
dc.identifier.urihttps://hdl.handle.net/20.500.12483/7951
dc.identifier.volume41en_US
dc.identifier.wosWOS:000275763700013en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofAdvances in Engineering Softwareen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial neural network methodsen_US
dc.subjectBackwateren_US
dc.subjectBridgesen_US
dc.subjectFlood controlen_US
dc.titleArtificial neural network approaches for prediction of backwater through arched bridge constrictionsen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
[ N/A ]
İsim:
Tam Metin / Full text
Boyut:
850.98 KB
Biçim:
Adobe Portable Document Format
İndir

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu