Structural damage assessment based on wavelet packet transform

Wavelet transform (WT) is a mathematical tool that can decompose a temporal signal into a summation of time-domain basis functions of various frequency resolutions. This simultaneous time-frequency decomposition gives the WT a special advantage over the traditional Fourier transform in analyzing nonstationary signals. One drawback of the WT is that its resolution is rather poor in the high-frequency region. Since structural damage is typically a local phenomenon captured most likely by high frequency modes, this potential drawback can affect the application of the wavelet-based damage assessment techniques. The wavelet packet transform (WPT) adopts redundant basis functions and hence can provide an arbitrary time-frequency resolution. In this study, a WPT based method is proposed for the damage assessment of structures. Dynamic signals measured from a structure are first decomposed into wavelet packet components. Component energies are then calculated and used as inputs into neural network models for damage assessment. Numerical simulations are performed on a three-span continuous bridge under impact excitation. The results show that the WPT-based component energies are good candidate indices that are sensitive to structural damage. These component energies can be used for various levels of damage assessment including identifying damage occurrence, location, and severity.