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BETOSCAN - An instrumented mobile robot system for the diagnosis of reinforced concrete floors
(2009)
Control and data acquisition of automated multi-sensor systems - two examples from civil engineering
(2010)
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 combination of radio-frequency identification (RFID) tags with different types of sensors offers excellent potential for applications with regard to identification, diagnosis, and monitoring. This should be demonstrated by means of two examples of actual developments carried out by the Federal Institute for Materials Research and Testing (BAM). The Identification and diagnosis of concrete components is a major task in the maintenance of critical infrastructure, for instance concrete bridges with heavy traffic volume. A feasibility study investigates the application of RFID sensor systems for this task. The second example reviews the transportation of dangerous goods. Using modern technologies enables promising possibilities to reduce accidents and to avoid non-conformity with transportation regulations. Project results demonstrate an innovative technical solution for monitoring of dangerous goods transports with RFID sensor systems.
Infrastructure is subject to continuous ageing. This has given life cycle management of infrastructure an increasing role. Reliable inspection and monitoring tools are therefore an increasing demand. A combination of different non-destructive test methods is often necessary to receive reliable results for material characterization,
flaw detection and the determination of component specific geometry parameters.
Regarding concrete structures thickness measurements are combined with flaw detection and additional information about reinforcement and tendon ducts is required.
Therefore, a multi-sensor measurement approach is necessary with a high degree of automation. Otherwise a time consuming succession of manual measurements has to be performed which would prevent practical applications. A modular control and data acquisition approach is described and the application of two different automated measurement devices is shown. The BetoScan system consists of a self-navigating
mobile robot. The system is especially designed for the investigation of reinforced concrete floors exposed to de-icing salts. The data acquisition of the OSSCAR system a multi-sensor scanner is similar to the robot approach. These different applications are based on a similar kernel allowing the modular use of different contact and noncontact sensors. The described general concept of multi-sensor data acquisition and data analysis presented here is not limited to the field of civil engineering applications.