STATISTICAL FEATURE EXTRACTION BASED ON AN ANN APPROACH FOR ESTIMATING THE COMPRESSIVE STRENGTH OF CONCRETE

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dc.contributor.authorDogan, G.
dc.contributor.authorArslan, M. H.
dc.contributor.authorCeylan, M.
dc.date.accessioned2020-03-26T19:07:11Z
dc.date.available2020-03-26T19:07:11Z
dc.date.issued2015
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractApplications of artificial intelligence in engineering disciplines have become widespread and have provided alternative solutions to engineering problems. Image processing technology (IPT) and artificial neural networks (ANNs) are types of artificial intelligence methods. However, IPT and ANN have been used together in extremely few studies. In this study, these two methods were used to determine the compressive strength of concrete, a complex material whose mechanical features are difficult to predict. Sixty cube-shaped specimens were manufactured, and images of specific features of the specimens were taken before they were tested to determine their compressive strengths. An ANN model was constituted as a result of the process of digitizing the images. In this way, the two different artificial intelligence methods were used together to carry out the analysis. The compressive strength values of the concrete obtained via analytical modeling were compared with the test results. The results of the comparison (R-2 = 0.9837-0.9961) indicate that the combination of these two artificial intelligence methods is highly capable of predicting the compressive strengths of the specimens. The model's predictive capability was also evaluated in terms of several statistical parameters using a set of statistical methods during the digitization of the images constituting the artificial neural network.en_US
dc.description.sponsorshipSelcuk University Unit of Scientific Research Projects CoordinationSelcuk University [13101007]en_US
dc.description.sponsorshipThis study was carried out within the framework of the Thesis Project (Gamze DOGAN) No 13101007 supported by the Selcuk University Unit of Scientific Research Projects Coordination. The authors are thankful to SU Unit of Scientific Research Project.en_US
dc.identifier.doi10.14311/NNW.2015.25.016en_US
dc.identifier.endpage318en_US
dc.identifier.issn1210-0552en_US
dc.identifier.issue3en_US
dc.identifier.scopusqualityQ4en_US
dc.identifier.startpage301en_US
dc.identifier.urihttps://dx.doi.org/10.14311/NNW.2015.25.016
dc.identifier.urihttps://hdl.handle.net/20.500.12395/32573
dc.identifier.volume25en_US
dc.identifier.wosWOS:000358101800005en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherACAD SCIENCES CZECH REPUBLIC, INST COMPUTER SCIENCEen_US
dc.relation.ispartofNEURAL NETWORK WORLDen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectStrengthen_US
dc.subjectconcreteen_US
dc.subjectimage processingen_US
dc.subjectneural networken_US
dc.subjectnondestructive testingen_US
dc.subjectstatistical properties/methodsen_US
dc.subjectcross validationen_US
dc.subjectanalytical modellingen_US
dc.titleSTATISTICAL FEATURE EXTRACTION BASED ON AN ANN APPROACH FOR ESTIMATING THE COMPRESSIVE STRENGTH OF CONCRETEen_US
dc.typeArticleen_US

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