Automatic delamination detection of concrete bridge decks using acoustic signatures

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 nondestructive 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 by impacting 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 detection is highly subjective.;The performance of acoustic NDE methods can be improved by employing a suitable noise-cancelling algorithm and a reliable detection algorithm that eliminates subjectivity. Since the noise is non-stationary and unpredictable, the algorithms should be adaptive. After evaluating different noise cancelling algorithms based on a numerical performance criterion and through visual inspection, a noise cancelling algorithm using a modified independent component analysis (ICA) was used to separate the sounding signals from recordings in a noisy environment. After the noise signals and the impact signals were successfully separated, the features of filtered signal were extracted. Different feature extraction algorithms were used to extract features of the filtered signals. The performance of different feature extraction algorithms were evaluated against repeatability, separability and mutual information which measures the information about the condition of the concrete bridge deck. Mel-frequency cepstral coefficients (MFCC) were used as features for detection. The extracted features were further selected based on the value of the mutual information to reduce the negative effect of features with poor separability. The features selected were used to train the classifiers and the trained classifiers were used to classify new signals. The error rate was used to evaluate the performance of different classifiers. The radial basis function neural network had the lowest error rate and was selected as the classifier for field application.;The proposed noise-cancelling and delamination detection algorithms were then implemented using mixed-language programming in MATLAB, Lab VIEW and C/C++. The performance of the system was verified using both experimental and field data. The proposed system showed good noise robustness. The performance of the system was satisfactory when there was sufficient available data for training and the selection of the training data was representative.