Machine learning based evaluation of concrete strength from saturated to dry by non-destructive methods

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dc.authorid0000-0001-7559-5684en_US
dc.authorid0000-0002-2724-190Xen_US
dc.authorid0000-0001-7617-198Xen_US
dc.contributor.authorGünaydın, Osman
dc.contributor.authorAkbaş, Ergün
dc.contributor.authorÖzbeyaz, Abdurrahman
dc.contributor.authorGüçlüer, Kadir
dc.date.accessioned2023-08-16T06:27:39Z
dc.date.available2023-08-16T06:27:39Z
dc.date.issued2023en_US
dc.departmentDicle Üniversitesi, Mühendislik Fakültesi, İnşaat Mühendisliği Bölümüen_US
dc.description.abstractMachine learning (ML) techniques have been increasingly applied in various scientific fields, including non-destructive testing (NDT), to enhance efficiency and speed up data analysis. In this study, an approach that aims to predict the compressive strength and quality of concrete with NDT and ML algorithms is presented to provide a great advantage in terms of both time and cost and to shorten the long laboratory periods. In the study, we aimed to set up a laboratory environment for determining the strengths of concrete samples by using Schmidt hardness values from NDT, curing times, water contents, and ultrasonic velocities. The data obtained in the laboratory environment was subjected to machine learning algorithms in the next process. In the laboratory environment, which is the first stage of the study, because concrete can be found at a variety of humidity levels depending on where it is used, 63 concrete samples were exposed to curing for 7, 28, and 90 days. Then these samples were put through a pressure test and subsequently exposed to various moisture conditions, from saturated to dry. Thus, the moisture state of concrete samples was evaluated based on their dryness or saturation in tests conducted on concrete samples. Afterwards, the ultrasonic velocities and Schmidt hardness values of these samples were measured. In the second stage, the strengths of concrete samples were classified with LR, MLP, SVM, and k-NN algorithms, and high R values were achieved. As a result of the studies carried out, the best R value (0.89) was achieved in the k-NN algorithm. This study has demonstrated that concrete strength values may be approximated with the k-NN method at high levels using sparse data collected in a laboratory setting.en_US
dc.identifier.citationGünaydın, O., Akbaş, E., Özbeyaz, A. ve Güçlüer, K. (2023). Machine learning based evaluation of concrete strength from saturated to dry by non-destructive methods. Journal of Building Engineering, (76), 1-13.en_US
dc.identifier.doi10.1016/j.jobe.2023.107174en_US
dc.identifier.endpage13en_US
dc.identifier.issn2352-7102
dc.identifier.issue76en_US
dc.identifier.scopus2-s2.0-85163330735en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S2352710223013542?via%3Dihub
dc.identifier.urihttps://hdl.handle.net/11468/12494
dc.identifier.wosWOS:001040724800001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorAkbaş, Ergün
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.relation.ispartofJournal of Building Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectNon-destructive testen_US
dc.subjectCompressive strengthen_US
dc.subjectWater contenten_US
dc.subjectMachine learningen_US
dc.subjectRegressionen_US
dc.titleMachine learning based evaluation of concrete strength from saturated to dry by non-destructive methodsen_US
dc.typeArticleen_US

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