Concrete compressive strength detection using image processing based new test method

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dc.contributor.authorDogan, Gamze
dc.contributor.authorArslan, Musa Hakan
dc.contributor.authorCeylan, Murat
dc.date.accessioned2020-03-26T19:34:35Z
dc.date.available2020-03-26T19:34:35Z
dc.date.issued2017
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractToday, Artificial Neural Networks (ANN) and Image Processing (IP) are particularly used to solve engineering problems. This study uses ANN and IP together to determine the mechanical properties of concrete, such as the compressive strength, modulus of elasticity and maximum deformation, at a certain success rate. In other words, the primary objective of study is to predict the mechanical properties of concrete without causing destruction, using a new alternative method. In this context, using five distinctive parameters (water/cement ratio, curing, amount of cement, compression and additive), 96 cylindrical concrete samples were produced; images of the samples were taken before they were examined at the compression testing, and the training and testing procedures for ANN and IP were realized using the obtained pressure readings at the laboratory. In addition to 96 cylindrical concrete samples, 48 were randomly selected to verify ANN and IP. From both the training/test samples and the verification samples, there is a notably high correlation between the outcomes of ANN and IP and the actual results, which varies between 97.18% and 99.87%. When ANN and IP were used together, the described method is a good alternative to the traditional destructive and nondestructive methods that are currently used to identify the mechanical properties of concrete. (C) 2017 Elsevier Ltd. All rights reserved.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 No 13101007 supported by the Selcuk University Unit of Scientific Research Projects Coordination.en_US
dc.identifier.doi10.1016/j.measurement.2017.05.051en_US
dc.identifier.endpage148en_US
dc.identifier.issn0263-2241en_US
dc.identifier.issn1873-412Xen_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage137en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.measurement.2017.05.051
dc.identifier.urihttps://hdl.handle.net/20.500.12395/34920
dc.identifier.volume109en_US
dc.identifier.wosWOS:000405973100017en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherELSEVIER SCI LTDen_US
dc.relation.ispartofMEASUREMENTen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectConcreteen_US
dc.subjectMechanical propertiesen_US
dc.subjectImage processingen_US
dc.subjectArtificial neural networken_US
dc.titleConcrete compressive strength detection using image processing based new test methoden_US
dc.typeArticleen_US

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