Classification of Thermally Degraded Concrete by Acoustic Resonance Method and Image Analysis via Machine Learning

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dc.contributor.authorDvořák, Richardcs
dc.contributor.authorChobola, Zdeněkcs
dc.contributor.authorPlšková, Ivetacs
dc.contributor.authorHela, Rudolfcs
dc.contributor.authorBodnárová, Lenkacs
dc.coverage.issue3cs
dc.coverage.volume16cs
dc.date.accessioned2023-01-26T15:52:22Z
dc.date.available2023-01-26T15:52:22Z
dc.date.issued2023-01-22cs
dc.description.abstractThe study of the resistance of plain concrete to high temperatures is a current topic across the field of civil engineering diagnostics. It is a type of damage that affects all components in a complex way, and there are many ways to describe and diagnose this degradation process and the resulting condition of the concrete. With regard to resistance to high temperatures, phenomena such as explosive spalling or partial creep of the material may occur. The resulting condition of thermally degraded concrete can be assessed by a number of destructive and nondestructive methods based on either physical or chemical principles. The aim of this paper is to present a comparison of nondestructive testing of selected concrete mixtures and the subsequent classification of the condition after thermal degradation. In this sense, a classification model based on supervised machine learning principles is proposed, in which the thermal degradation of the selected test specimens are known classes. The whole test set was divided into five mixtures, each with seven temperature classes in 200 °C steps from 200 °C up to 1200 °C. The output of the paper is a comparison of the different settings of the classification model and validation algorithm in relation to the observed parameters and the resulting model accuracy. The classification is done by using parameters obtained by the acoustic NDT Impact-Echo method and image-processing tools.en
dc.formattextcs
dc.format.extent25cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationMaterials . 2023, vol. 16, issue 3, 25 p.en
dc.identifier.doi10.3390/ma16031010cs
dc.identifier.issn1996-1944cs
dc.identifier.orcid0000-0003-0024-2344cs
dc.identifier.orcid0000-0002-8604-7162cs
dc.identifier.orcid0000-0003-1527-8219cs
dc.identifier.orcid0000-0001-6200-1728cs
dc.identifier.orcid0000-0002-4169-7735cs
dc.identifier.other181536cs
dc.identifier.researcheridAAL-7858-2020cs
dc.identifier.researcheridH-2733-2016cs
dc.identifier.scopus57195216023cs
dc.identifier.scopus23003716600cs
dc.identifier.scopus25637043800cs
dc.identifier.urihttp://hdl.handle.net/11012/208773
dc.language.isoencs
dc.publisherMDPIcs
dc.relation.ispartofMaterialscs
dc.relation.urihttps://www.mdpi.com/1996-1944/16/3/1010cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/1996-1944/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectconcreteen
dc.subjecthigh temperaturesen
dc.subjectnondestructive testingen
dc.subjectmachine learningen
dc.subjectimage analysisen
dc.subjectImpact-Echoen
dc.subjectresonance methoden
dc.titleClassification of Thermally Degraded Concrete by Acoustic Resonance Method and Image Analysis via Machine Learningen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-181536en
sync.item.dbtypeVAVen
sync.item.insts2023.08.14 16:59:39en
sync.item.modts2023.08.14 16:19:33en
thesis.grantorVysoké učení technické v Brně. Fakulta stavební. Ústav technologie stavebních hmot a dílcůcs
thesis.grantorVysoké učení technické v Brně. Fakulta stavební. Fyzika AdMaScs
thesis.grantorVysoké učení technické v Brně. Fakulta stavební. Ústav fyzikycs
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