Application of Noise Cancelling and Damage Detection Algorithms in NDE of Concrete Bridge Decks Using Impact Signals
Delamination is a commonly observed distress in concrete bridge decks. Among all the delamination detection methods, acoustic methods have the advantages of being fast and inexpensive. In traditional acoustic inspection methods, the inspector drags a chain along or hammers on the bridge deck and detects delamination from the 'hollowness' of the sounds. The signals are often contaminated by ambient traffic noise and the detection of delamination is highly subjective. This paper describes the performance of an impact-bases acoustic NDE method where the traffic noise was filtered by employing a noise cancelling algorithm and where subjectivity was eliminated by introducing feature extraction and pattern recognition algorithms. Different algorithms were compared and the best one was selected in each category. The comparison showed that the modified independent component analysis (ICA) algorithm was most effective in cancelling the traffic noise and features consisting of mel-frequency cepstral coefficients (MFCCs) had the best performance in terms of repeatability and separabillty. The condition of the bridge deck was then detected by a radial basis function (RBF) neural network. The performance of the system was evaluated using both experimental and field data. The results show that the selected algorithms increase the noise robustness of acoustic methods and perform satisfactorily if the training data is representative.
- Research Organization:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC05-76RL01830
- OSTI ID:
- 1029084
- Report Number(s):
- PNNL-SA-77730; JNOED5; TRN: US201122%%715
- Journal Information:
- Journal of Nondestructive Evaluation, Vol. 30, Issue 4; ISSN 0195-9298
- Country of Publication:
- United States
- Language:
- English
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