Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/14830
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dc.contributor.authorKoroglu, F. B.-
dc.contributor.authorMaguire, M.-
dc.contributor.authorAkta, E.-
dc.date.accessioned2024-09-24T15:58:54Z-
dc.date.available2024-09-24T15:58:54Z-
dc.date.issued2024-
dc.identifier.isbn9783031602733-
dc.identifier.isbn9783031602719-
dc.identifier.isbn9783031602702-
dc.identifier.issn2366-2557-
dc.identifier.issn2366-2565-
dc.identifier.urihttps://doi.org/10.1007/978-3-031-60271-9_37-
dc.description.abstractThis study highlights two of the important details of the implementation of artificial neural networks to the structural reliability problems by pointing out the effect of training dataset, and the relationship between the performance parameters (coefficient of determination of train, validation, and test sets) of a network and its probability estimation capacity when it is used as a surrogate model in structural reliability problems. Four numerical examples are covered regarding these key aspects including one that is derived from a real-life reinforced concrete structure. Results have shown that the dataset can affect the probability estimation capacity for complex problems. Furthermore, it is also observed that having a neural network with good performance parameters does not mean that the network always has good probability estimation capacity. However, in order to have a network that can be used for probability estimate purposes, its performance parameters must be at a satisfactory level.en_US
dc.language.isoenen_US
dc.publisherSpringer international Publishing Agen_US
dc.relation.ispartof20th International Probabilistic Workshop (IPW) -- MAY 08-10, 2024 -- Guimaraes, PORTUGALen_US
dc.relation.ispartofseriesLecture Notes in Civil Engineering-
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectStructural Reliabilityen_US
dc.subjectTraining Dataseten_US
dc.subjectPerformance Parametersen_US
dc.titleInvestigation of the Effect of Artificial Neural Network Performance Parameters and Training Dataset on the Probability Estimate Capacity in Structural Reliability Problemsen_US
dc.typeConference Objecten_US
dc.departmentIzmir Institute of Technologyen_US
dc.identifier.volume494en_US
dc.identifier.startpage398en_US
dc.identifier.endpage407en_US
dc.identifier.wosWOS:001323733800039-
dc.identifier.scopus2-s2.0-85200352745-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1007/978-3-031-60271-9_37-
dc.authorscopusid59246226200-
dc.authorscopusid56478346400-
dc.authorscopusid7003508723-
dc.identifier.wosqualityN/A-
dc.identifier.scopusqualityQ4-
dc.description.woscitationindexConference Proceedings Citation Index - Science-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.languageiso639-1en-
item.openairetypeConference Object-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.cerifentitytypePublications-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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