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Data aggregation for improved honeycomb detection in concrete using machine learning-based algorithms
- We present the results of several machine learning (ML)- inspired data fusion algorithms applied to multi-sensory nondestructive testing (NDT) data. Our dataset consists of Impact-Echo (IE), Ultrasonic Pulse Echo (US) and Ground Penetrating Radar (GPR) data collected on large-scale concrete specimens with built–in simulated honeycombing defects. The main objective is to improve the detectability of honeycombs by fusing the information from the three different sensors. We describe normalization, feature detection and optimal feature selection. We have used unsupervised and supervised ML, i.e., classification and clustering, for data fusion. We demonstrate the advantage of data fusion in reducing the false positives up to 10% compared to the best single sensor, thus, improving the detectability of the defects. The methods were evaluated on a concrete specimen. The effectiveness of the proposed approach was demonstrated on a separate full-scale concrete specimen. The results indicate theWe present the results of several machine learning (ML)- inspired data fusion algorithms applied to multi-sensory nondestructive testing (NDT) data. Our dataset consists of Impact-Echo (IE), Ultrasonic Pulse Echo (US) and Ground Penetrating Radar (GPR) data collected on large-scale concrete specimens with built–in simulated honeycombing defects. The main objective is to improve the detectability of honeycombs by fusing the information from the three different sensors. We describe normalization, feature detection and optimal feature selection. We have used unsupervised and supervised ML, i.e., classification and clustering, for data fusion. We demonstrate the advantage of data fusion in reducing the false positives up to 10% compared to the best single sensor, thus, improving the detectability of the defects. The methods were evaluated on a concrete specimen. The effectiveness of the proposed approach was demonstrated on a separate full-scale concrete specimen. The results indicate the transportability of the conclusions from one specimen to the other.…
Autor*innen: | Christoph VölkerORCiD, P. Shokouhi |
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Dokumenttyp: | Zeitschriftenartikel |
Veröffentlichungsform: | Graue Literatur |
Sprache: | Englisch |
Titel des übergeordneten Werkes (Englisch): | The e-journal of nondestructive testing & ultrasonics |
Jahr der Erstveröffentlichung: | 2015 |
Veröffentlichende Institution: | Bundesanstalt für Materialforschung und -prüfung (BAM) |
Verlag: | NDT.net |
Verlagsort: | Kirchwald |
Jahrgang/Band: | 20 |
Ausgabe/Heft: | 11 |
Aufsatznummer: | 18364 |
Erste Seite: | 1 |
Letzte Seite: | 8 |
DDC-Klassifikation: | Naturwissenschaften und Mathematik / Chemie / Analytische Chemie |
Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Ingenieurbau | |
Freie Schlagwörter: | Clustering; Concrete evaluation; Data fusion; Honeycombing; Machine learning |
Veranstaltung: | NDT-CE 2015 - International symposium non-destructive testing in civil engineering |
Veranstaltungsort: | Berlin, Germany |
Beginndatum der Veranstaltung: | 15.09.2015 |
Enddatum der Veranstaltung: | 17.09.2015 |
URN: | urn:nbn:de:kobv:b43-350968 |
URL: | https://www.ndt.net/?id=18364 |
ISSN: | 1435-4934 |
Verfügbarkeit des Dokuments: | Datei für die Öffentlichkeit verfügbar ("Open Access") |
Lizenz (Deutsch): | Creative Commons - Namensnennung-Keine Bearbeitung |
Datum der Freischaltung: | 20.02.2016 |
Referierte Publikation: | Nein |
Schriftenreihen ohne Nummerierung: | Wissenschaftliche Artikel der BAM |