BETOSCAN - An instrumented mobile robot system for the diagnosis of reinforced concrete floors
(2009)
Bereits seit vielen Jahren stehen Anwendern der zerstörungsfreien Prüfung im Bauwesen (ZfPBau) zuverlässige Geräte für Radar-, Wirbelstrom- und Ultraschallmessungen an Betonbauteilen zur Verfügung. Doch erst die Kombination dieser drei Verfahren an einer Messfläche erlaubt den maximalen Informationsgewinn über die innere Konstruktion von Stahlbeton- und Spannbetonbauteilen. Mit dem OSSCAR-Bauwerkscanner wurden diese drei Verfahren erstmals zur kombinierten
und automatisierten Datenaufnahme an einem Scannerrahmen und unter einer Software vereint. Die Ergebnisse werden bildgebend in frei wählbaren Schnitten dargestellt, was eine Rekonstruktion von Bauteilen erlaubt, für die keine Planunterlagen vorliegen.
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 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.