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Combining Signal Features of Ground-Penetrating Radar to Classify Moisture Damage in Layered Building Floors

  • To date, the destructive extraction and analysis of drilling cores is the main possibility to obtain depth information about damaging water ingress in building floors. The time- and costintensive procedure constitutes an additional burden for building insurances that already list piped water damage as their largest item. With its high sensitivity for water, a ground-penetrating radar (GPR) could provide important support to approach this problem in a non-destructive way. In this research, we study the influence of moisture damage on GPR signals at different floor constructions. For this purpose, a modular specimen with interchangeable layers is developed to vary the screed and insulation material, as well as the respective layer thickness. The obtained data set is then used to investigate suitable signal features to classify three scenarios: dry, damaged insulation, and damaged screed. It was found that analyzing statistical distributions of A-scan features inside one B-scan allows forTo date, the destructive extraction and analysis of drilling cores is the main possibility to obtain depth information about damaging water ingress in building floors. The time- and costintensive procedure constitutes an additional burden for building insurances that already list piped water damage as their largest item. With its high sensitivity for water, a ground-penetrating radar (GPR) could provide important support to approach this problem in a non-destructive way. In this research, we study the influence of moisture damage on GPR signals at different floor constructions. For this purpose, a modular specimen with interchangeable layers is developed to vary the screed and insulation material, as well as the respective layer thickness. The obtained data set is then used to investigate suitable signal features to classify three scenarios: dry, damaged insulation, and damaged screed. It was found that analyzing statistical distributions of A-scan features inside one B-scan allows for accurate classification on unknown floor constructions. Combining the features with multivariate data analysis and machine learning was the key to achieve satisfying results. The developed method provides a basis for upcoming validations on real damage cases.zeige mehrzeige weniger

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Metadaten
Autor*innen:Tim KleweORCiD, Christoph StrangfeldORCiD, Tobias Ritzer, Sabine KruschwitzORCiD
Dokumenttyp:Zeitschriftenartikel
Veröffentlichungsform:Verlagsliteratur
Sprache:Englisch
Titel des übergeordneten Werkes (Englisch):Applied Sciences
Jahr der Erstveröffentlichung:2021
Organisationseinheit der BAM:8 Zerstörungsfreie Prüfung
8 Zerstörungsfreie Prüfung / 8.0 Abteilungsleitung und andere
Veröffentlichende Institution:Bundesanstalt für Materialforschung und -prüfung (BAM)
Verlag:MDPI
Jahrgang/Band:11
Ausgabe/Heft:19
Erste Seite:8820
DDC-Klassifikation:Naturwissenschaften und Mathematik / Chemie / Analytische Chemie
Freie Schlagwörter:Civil Engineering; Machine Learning; Material Moisture; Non-destructive testing; Radar; Signal Features
Themenfelder/Aktivitätsfelder der BAM:Chemie und Prozesstechnik
DOI:10.3390/app11198820
URN:urn:nbn:de:kobv:b43-533606
Zugehöriger Identifikator:https://opus4.kobv.de/opus4-bam/frontdoor/index/index/docId/59609
Verfügbarkeit des Dokuments:Datei für die Öffentlichkeit verfügbar ("Open Access")
Lizenz (Deutsch):License LogoCreative Commons - CC BY - Namensnennung 4.0 International
Datum der Freischaltung:23.09.2021
Referierte Publikation:Ja
Datum der Eintragung als referierte Publikation:28.10.2021
Schriftenreihen ohne Nummerierung:Wissenschaftliche Artikel der BAM
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