Detection and classification of flaws in concrete structure using bispectra and neural networks

The problem addressed in this paper is the detection and classification of flaws in concrete structure. It is known that higher-order spectra contain information not present in the power spectrum and can suppress Gaussian noise. Thus estimates of higher-order spectra have been shown to be useful in certain signal processing problems. This paper is concerned with the feature extraction from bispectra for concrete flaw detection. Impact-echo experiments are carried out for three different types of flaw in concrete structure. For each monitoring signal, after bispectral estimation, features are selected from the modules of bispectra in the primary region. For automatic interpretation, a multilayer back-propagation neural network is used as a classifier. Both clean data and data with additive white Gaussian noise are used for training and testing. The classification results obtained experimentally demonstrate that this method has good detection rates in varying environments.