Automatic Delamination Detection of Concrete Bridge Decks Using Impact Signals

Abstract

Delamination of the concrete cover above the upper reinforcing bars is a common problem in concrete bridge decks. Acoustic nondestructive evaluation is widely used to detect such delamination because of its low cost, fast speed, and ease of implementation. The accuracy of traditional acoustic approaches is dependent on the level of ambient noise, and the detection process is highly subjective. An automatic impact-based delamination detection (AIDD) system is described in this paper. In this system, the traffic noise is eliminated by a modified version of independent component analysis. Mel-frequency cepstral coefficients are then used as features for detection to eliminate subjectivity. The delamination detection is performed by a radial basis function neural network. The AIDD system was developed using mixed-language programming in MATLAB, LabVIEW, and C++. The performance of the system was evaluated using data from two bridges, and the results were satisfactory.