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. The signals from such sounding methods are often contaminated by ambient traffic noise and delamination 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 delamination detection and the delamination is detected by a radial basis function (RBF) neural network. Results from both laboratory and field data suggest that the proposed method is noise robust and has satisfactory performance. The method can also detect the debonding of repair patches and concrete delamination below the repair patches. The algorithms were incorporated into an automatic impact-based delamination detection (AIDD) system for field application.