Impact sounding has been recognized as an effective technique to detect delamination in concrete structures, such as concrete decks. An enormous amount of sounding data can be generated/collected by the autonomous inspection systems equipped with impactors and microphones. However, the main challenge in the practical application of this technology is the development of advanced data analysis approaches for identifying defects from impact sounding data. In this study, the empirical mode decomposition (EMD) analysis and power-spectral density (PSD) analysis are combined to extract useful features of sounding data generated by the impact hammer. It has been found that the EMD method can effectively eliminate noise from the captured data during the identification of features such as the fundamental frequency. Based on extracted features, a defect contour of the inspected structure can be generated for fast decision making and reliable inspection ratings of concrete structures.
Agrawal, Anil K., "Quantitative Bridge Inspection Ratings Using Autonomous Robotic Systems" (2022). Project IM-2. 1.
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31 Mar 2020