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Unsupervised fusion of scattered data collected by a multi-sensor robot on concrete

  • At BAM a multi-sensor robot system BetoScan is used for the investigation of reinforced concrete floors affected by corrosion in parking garages. Potential maps, as well as the distribution of concrete cover and moisture can be assessed simultaneously and data can be collected contactlessly. In order to evaluate the extent of degradation adequately and to divide the investigated structure into zones with defined damage classes, large data sets have to be collected and interpreted manually. Thus, to promote an efficient data evaluation framework, which could speed up and simplify the evaluation of large data sets, an unsupervised data fusion is of major interest. However, taking into account that collected data do not certainly coincide in space, a scattered data interpolation method should be applied prior data fusion. In the paper, a case study involving a BetoScan data set acquired from a reinforced concrete floor of a parking garage in Germany is presented. The data set includesAt BAM a multi-sensor robot system BetoScan is used for the investigation of reinforced concrete floors affected by corrosion in parking garages. Potential maps, as well as the distribution of concrete cover and moisture can be assessed simultaneously and data can be collected contactlessly. In order to evaluate the extent of degradation adequately and to divide the investigated structure into zones with defined damage classes, large data sets have to be collected and interpreted manually. Thus, to promote an efficient data evaluation framework, which could speed up and simplify the evaluation of large data sets, an unsupervised data fusion is of major interest. However, taking into account that collected data do not certainly coincide in space, a scattered data interpolation method should be applied prior data fusion. In the paper, a case study involving a BetoScan data set acquired from a reinforced concrete floor of a parking garage in Germany is presented. The data set includes potential mapping, covermeter based on eddy current, as well as microwave moisture measurements. Among the examined methods for interpolation of scattered data, kriging shows to yield smooth interpolated data plots even in the case of very sparse data. In the post-processing step, the investigated structure is efficiently segmented into zones using clustering based data fusion methods, which prove to be robust enough also for handling noisy data. Based on the minimization of the XB validity index, an unsupervised selection of optimal segmentation into damage classes is derived.zeige mehrzeige weniger

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Metadaten
Autor*innen:P. Cotic, Ernst NiederleithingerORCiD, Markus Stoppel
Dokumenttyp:Beitrag zu einem Tagungsband
Veröffentlichungsform:Verlagsliteratur
Sprache:Englisch
Titel des übergeordneten Werkes (Englisch):DGZfP-Jahrestagung 2014 (Proceedings)
Jahr der Erstveröffentlichung:2014
Veröffentlichende Institution:Bundesanstalt für Materialforschung und -prüfung (BAM)
Verlag:Deutsche Gesellschaft für Zerstörungsfreie Prüfung (DGZfP)
Ausgabe/Heft:DGZfP-BB 148
Erste Seite:Di.2.C.1, 1
Letzte Seite:8
Freie Schlagwörter:Data fusion; Potential method; Radar; Robot; Ultrasonics
Veranstaltung:DGZfP-Jahrestagung 2014
Veranstaltungsort:Potsdam, Germany
Beginndatum der Veranstaltung:26.05.2014
Enddatum der Veranstaltung:28.05.2014
URN:urn:nbn:de:kobv:b43-337943
URL:http://www.ndt.net/article/dgzfp2014/papers/di2c1.pdf
ISBN:978-3-940283-61-0
Verfügbarkeit des Dokuments:Datei für die Öffentlichkeit verfügbar ("Open Access")
Datum der Freischaltung:31.12.2015
Referierte Publikation:Nein
Schriftenreihen ohne Nummerierung:Wissenschaftliche Artikel der BAM
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