An Automatic Impact-based Delamination Detection System for Concrete Bridge Decks
Delamination of concrete bridge decks is a commonly observed distress in corrosive environments. In traditional acoustic inspection methods, delamination is assessed by the "hollowness" of the sound created by impacting the bridge deck with a hammer or bar or by dragging a chain where the signals are often contaminated by ambient traffic noise and the detection is highly subjective. In the proposed method, a modified version of independent component analysis (ICA) is used to filter the traffic noise. To eliminate subjectivity, Mel-frequency cepstral coefficients (MFCC) are used as features for detection and the delamination is detected by a radial basis function (RBF) neural network. Results from both experimental and field data suggest that the proposed methods id noise robust and has satisfactory performance. The methods can also detect the delamination of repair patches and concrete below the repair patches. The algorithms were incorporated into an automatic impact-bases delamination detection (AIDD) system for field application.
- Research Organization:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC05-76RL01830
- OSTI ID:
- 1076715
- Report Number(s):
- PNNL-SA-77729
- Journal Information:
- NDT & E International, 45(1):120-127, Journal Name: NDT & E International, 45(1):120-127
- Country of Publication:
- United States
- Language:
- English
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