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- 2014 (5) (entfernen)
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- Data fusion (2)
- Radar (2)
- Ultrasonics (2)
- Bridge components (1)
- Cluster analysis (1)
- Concrete (1)
- Corrosion (1)
- Dangerous goods (1)
- Defect detection (1)
- Defects (1)
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.
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.