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Time reversal techniques are used in ocean acoustics, medical imaging and non-destructive evaluation to backpropagate recorded signals to the source of origin. We demonstrate experimentally a technique which improves the temporal focus achieved at the source location. The experiment consists of propagating a signal from a transducer within a concrete block to a single receiver on the surface, and then applying time reversal or deconvolution to focus the energy back at the source location. The results show that we are able to generate a focus in time at the correct location. The proposed method is simple and proven to be robust. Additionally, its costs are negligible due to deconvolution being a preprocessing step to the recorded data. The technique can be applied for detailed investigation of the source mechanisms (e. g. cracks) but also for monitoring purposes.
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.