Non-destructive Testing of CIPP Defects Using Machine Learning Approach

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dc.contributor.authorDvořák, Richardcs
dc.contributor.authorPazdera, Lubošcs
dc.contributor.authorTopolář, Liborcs
dc.contributor.authorJakubka, Lubošcs
dc.contributor.authorPuchýř, Jancs
dc.coverage.issue5cs
dc.coverage.volume58cs
dc.date.accessioned2025-04-04T11:56:36Z
dc.date.available2025-04-04T11:56:36Z
dc.date.issued2024-09-15cs
dc.description.abstractIn civil engineering, retrofitting actions involving repairs to pipes inside buildings and in extravehicular locations present complex and challenging tasks. Traditional repair procedures typically involve disassembling the surrounding structure, leading to technological pauses and potential work environment disruptions. An alternative approach to these procedures uses the cured-in-place-pipe (CIPP) technology for repairs. Unlike standard repairs, CIPP repairs do not require a disassembly of the surrounding structures; only the access points at the beginning and end of the pipe need to be accessible. However, this method introduces the possibility of different types of defects.1 1 This research aims to observe the defects between the host and newly cured pipes. The presence of holes, cracks, or obstacles prevents achieving a desired close-fit state, ultimately reducing the life expectancy of the retrofitting. This paper focuses on the non-destructive observation of these defects using the non-destructive testing (NDT) impact-echo (IE) method. The study explicitly applies this method to the composite segments inside concrete host pipes, forming a testing polygon. Previous results have indicated that the mechanical behaviour of cured composite pipes can vary in stiffness depending on factors such as the curing procedure and environmental conditions.2 2 The change in acoustic parameters such as resonance frequency, attenuation and other features of typical IE signals can describe the stiffness evolution. This study compares different sensors used for the proposed IE testing, namely piezoceramic and microphone sensors. It evaluates their ability to distinguish between the defects present in the body of a CIPP via a machine-learning approach using random tree classifiers.en
dc.formattextcs
dc.format.extent561-565cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationMateriali in tehnologije. 2024, vol. 58, issue 5, p. 561-565.en
dc.identifier.doi10.17222/mit.2023.1000cs
dc.identifier.issn1580-2949cs
dc.identifier.orcid0000-0003-0024-2344cs
dc.identifier.orcid0000-0002-9416-5644cs
dc.identifier.orcid0000-0001-9437-473Xcs
dc.identifier.other189650cs
dc.identifier.researcheridAAL-7858-2020cs
dc.identifier.researcheridE-3469-2018cs
dc.identifier.researcheridE-9341-2018cs
dc.identifier.scopus57195216023cs
dc.identifier.scopus6602266656cs
dc.identifier.scopus37051376500cs
dc.identifier.urihttps://hdl.handle.net/11012/250760
dc.language.isoencs
dc.publisherInstitute of Metals and Technologycs
dc.relation.ispartofMateriali in tehnologijecs
dc.relation.urihttps://mater-tehnol.si/index.php/MatTech/article/view/1000cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/1580-2949/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectRetrofittingen
dc.subjectCured-in-Place Pipesen
dc.subjectNon-Destructive Testingen
dc.subjectImpact-Echo Methoden
dc.subjectPipe Defectsen
dc.subjectAcoustic Parametersen
dc.subjectMachine Learningen
dc.subjectClassification.en
dc.titleNon-destructive Testing of CIPP Defects Using Machine Learning Approachen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-189650en
sync.item.dbtypeVAVen
sync.item.insts2025.04.04 13:56:36en
sync.item.modts2025.03.31 11:32:05en
thesis.grantorVysoké učení technické v Brně. Fakulta stavební. Ústav fyzikycs
thesis.grantorVysoké učení technické v Brně. Fakulta stavební. Ústav stavebního zkušebnictvícs
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