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Collecting different geophysical data sets at the same object and site offers the opportunity to reduce uncertainties and ambiguities in data analysis and interpretation. To be effective, the different available data sets should be linked during the model-generation process, e.g. by cooperative inversion. In this study, we apply a recently developed zonal cooperative inversion approach based on fuzzy c-means cluster analysis to a non-destructive testing experiment. After briefly reviewing the fundamentals of the inversion strategy, we present a synthetic study investigating the potential of the method to detect air-filled voids in masonry by using ultrasonic and georadar traveltime data. Then, we present and discuss laboratory experiments including the results of cooperatively inverted ultrasonic and georadar traveltimes collected at a masonry test specimen. The geometry of the specimen is known and is thus an ideal test object for a first-time real application of the novel zonal cooperative inversion procedure. Compared to the results of separate inversions of ultrasonic and georadar traveltimes, the zonal cooperative inversion allows for an improved delineation of the size and position of the cavities. The P-wave and georadar velocities determined for the model regions corresponding to the cavities are also improved.
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.