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.
The capabilities of non-destructive testing (NDT) methods for defect detection in civil engineering are characterized by their different penetration depth, resolution and sensitivity to material properties. Therefore, in many cases multi-sensor NDT has to be performed, producing large data sets that require an efficient data evaluation framework. In this work an image fusion methodology is proposed based on unsupervised clustering methods. Their performance is evaluated on ground penetrating radar and infrared thermography data from laboratory concrete specimens with different simulated near-surface defects. It is shown that clustering could effectively partition the data for further feature level-based data fusion by improving the detectability of defects simulating delamination, voids and localized water. A comparison with supervised symbol level fusion shows that clustering-based fusion outperforms this, especially in situations with very limited knowledge about the material properties and depths of the defects. Additionally, clustering is successfully applied in a case study where a multi-sensor NDT data set was automatically collected by a self-navigating mobile robot system.
Eine Vielzahl zerstörungsfreier Prüfverfahren hat in den letzten Jahren Einzug in die Bauwerksuntersuchung gehalten. Um die Aussagekraft der Einzelverfahren zu erhöhen und den wirtschaftlichen Einsatz weiter voranzubringen, lag es nahe, die Verfahren kombiniert auf einer automatisierten Plattform zu montieren. Zu diesem Zweck wurde das BetoScan-System entwickelt. Um weitere Erfahrungen mit dem BetoScan-System zu erhalten, wurde ein langjährig genutztes Parkhaus aus Stahlbeton mit Gussasphaltfahrbahn als Untersuchungsobjekt ausgewählt. Bei der experimentellen Bauwerksuntersuchung wurden die Verfahren Wirbelstrom, Radar, Ultraschall und Mikrowelle zum Einsatz gebracht. Von der Anwendung des Systems und der Auswertung der Ergebnisse wird in diesem Beitrag berichtet.-----------------------------------------------------------------------------------------
In recent years a variety of non-destructive evaluation methods are more and more used for the inspection of constructions. Increasing the information value of inspections and the economic aspects of applications leads to a combination of methods on an automated system. Therefore, the BetoScan robot was developed. Gaining more experience in applications was realized with the inspection of a car park which was unused for several years. The building was made of reinforced concrete with melted asphalt as floor coating. Within the frame of the inspections eddy current, radar, ultrasound and microwaves were used. The application of the BetoScan system and the results are presented in this article.
Control and data acquisition of automated multi-sensor systems - two examples from civil engineering
(2010)
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.