A neuro-wavelet technique for seismic damage identification of cantilever structures

A method based on artificial neural networks and wavelet transform is proposed for identifying seismic-induced damage of cantilever structures. In the proposed method, response accelerations are measured at strategically selected locations. To extract damage-induced sharp transitions from the measured signals, they are decomposed by continuous wavelet transform. The size of the decomposed signals is reduced by principal component analysis (PCA). Principal components obtained from PCA are fed to a set of neural networks to identify damage. The proposed algorithm is applied to a tall airport traffic control tower by means of numerical simulations. The obtained results show that the proposed method effectively identifies seismic-induced damage, and the noise intensity has a negligible effect on the predicted results. Moreover, the trained neural network system is able to predict the seismic-induced damage of unseen samples well.