Damage Detection in Concrete Slab Using Smart Sounding

Delamination is a serious problem in concrete bridge decks. Impact sounding has proved to be one of the most effective techniques for defect detection on concrete surfaces. However, the data collection technique is generally compromised due to the noise associated either with the surroundings or the equipment itself. The data collection is also affected due to human errors. This paper studies different data collection techniques using Hammer (flat and round), Robotic crawler, and Laser Doppler Vibrometer (LDV) on a concrete slab. The slab is embedded with different types of artificially constructed defects, such as shallow delamination, void, honeycomb, and deep delamination. Empirical Mode Decomposition (EMD) has been applied to filter the noise from the data collected. Different advanced signal processing techniques such as Power Spectral Density (PSD), Hilbert Huang Transform (HHT), and Hilbert Marginal Spectrum (HMS) have been applied for defect detection, and the results have been compared. The limitations of the different data collection techniques and signal processing techniques have also been discussed.