PREDICTION OF STRUCTURAL DAMAGE BY THE WAVELET-BASED NEURAL NETWORK

The application of wavelet-based neural network ART2 to 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 damage detection are analyzed. Wavelet-based neural network is constructed by taking wavelet transform as the pre-processor of neural network. Then the wavelet de-noise, the detection of changes of a signal and the ability of damage detection of wavelet-based neural network are tested by numerical samples. The effectiveness of this method is attested further by a model frame structure. The results show that the present method is feasible and it has advantages of few requirements of historical data, automatic increase of identification category, and the noiseproof ability.