Delamination of the concrete cover above the upper reinforcing bars is a common problem in concrete bridge decks. The delamination is typically initiated by corrosion of the upper reinforcing bars and promoted by freeze-thaw cycling and traffic loading. The detection of delamination is important for bridge maintenance and acoustic non-destructive evaluation (NDE) is widely used due to its low cost, speed, and easy implementation. In traditional acoustic approaches, the inspector sounds the surface of the deck byimpacting it with a hammer or bar, or by dragging a chain, and assesses delamination by the hollowness of the sound. The acoustic signals are often contaminated by traffic and ambient noise at the site and the detectionis highly subjective. The performance of acoustic NDE methods can be improved by employing a suitable noise-reduction algorithm and a reliable detection algorithm that eliminates subjectivity. Since the noise is non-stationary and unpredictable, the algorithms should be adaptive. In this paper,a blind source separation algorithm using a modified independent component analysis (ICA) is used to separate the sounding signals from recordings in a noisy environment. The filtered signals are then fed into a detectionalgorithm where mel-frequency cepstral coefficients (MFCC) are used as features for detection. The performance of the detection algorithm was validated using recordings with different signal-to-noise ratios. The results show that the proposed algorithms can significantly improve the detection accuracy, especially under highly noisy conditions.
Abstract