Prediction of structural damage by the wavelet-based neural network

In this paper, the application of wavelet-based neural network ART-2 for the damage detection of structure is discussed. A method combining dyadic wavelet with neural network of ART2 is presented and the damage location can be well identified with this method. The basic theories of artificial neural network and wavelet transform are given and their features and the principle of detecting damage are analyzed. The wavelet-based neural network is constructed by making wavelet transform the pre-processor of neural network. Then the wavelet de-noise and detection of changes of a signal and the ability of damage detection of wavelet-based neural network are tested by numerical samples. At the end, the effectiveness of this method is attested further by a model frame structure. The results show the method presented in this article is feasible and it has the advantages of few requirements of historical data, automatic increase of identification category, and the ability of anti-noise.