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This paper summarizes the results of research aimed at assessing cracks in reinforced concrete structures using embedded ultrasonic sensors. The diffuse ultrasonic waves were considered to evaluate the health status of the tested structures. There are different algorithms used to detect cracks in the structure, but most studies have been performed on benchmark reinforced concrete (RC) structures and in laboratory conditions. Since there were difficulties with the validity of Damage detection in real structures in the presence of environmental changes and noises, the application of advanced signal processing methods was necessary. Therefore, the wavelet transform was applied to process ultrasonic signals acquired from multiple civil structures. It is shown that the ultrasonic sensors with an applied wavelet transform algorithm on collected signals can successfully detect cracks in the laboratory as well as in a real environment. Experimental results showed a perfect match for detecting damage and quasi-static load in the presence of environmental changes. The results were confirmed with other techniques. In addition, designing an extra filter for removing noises can be avoided by using the applied algorithms. The obtained results confirmed that diffuse ultrasonic sensor methodology with the proposed algorithm is useful and effective in Monitoring real RC structures, and it is better than traditional techniques.
In structural parts under vibrational loading fatigue cracks can initiate and grow, which can lead to structural failure. Conventional non-destructive testing methods for crack detection provide just a snapshot of fatigue crack evolution, whereas crack luminescence coating realizes clear visibility of the entire crack formation. Fatigue causing cyclic tensile tests and examinations on special test bodies allowing control of the crack opening width demonstrate a high sensitivity of the coating.
This study aims at improving the detection of near-surface defects in magnetizable and conductive specimens by combining the measurements of eddy current, magnetic flux leakage and thermography testing. Different signal processing methods for data normalization are proposed to enable data fusion at the pixel level. These methods are applied to a test specimen which contains 10 variably-sized defects. We quantitatively evaluate the performances of a total of 29 detection methods with respect to false alarm reduction at a fixed level of true positive rate. We report that false positive rate could be reduced from 1.65% down to 0.28% by the best multi-sensor method compared to the best single-sensor performance on the smallest defect, when 50% found flaw pixels are required for successful detection.