Damage Detection Using Wavelet Packet Decomposition and Random Forests Algorithm in Experimental Structure at the UBC (University of British Columbia)

Damage Detection methods based on signal are principal and widely used methods that contain wavelet packet decomposition, which is one of new methods in this field. On the other hand there are lots of methods and implements for evaluate models which classify data and regression.Random Forests ,which is newly used method has attracted researchers attention. In this paper a experimental structure was designed and analyzed. drifts from time history response were decomposed to energy rate indexes by wavelet packet decomposition. energy rate indexes in each damage conditions were classified in 3 class of damage conditions and they made data base. finally by training the algorithm, R-F tested other conditions with comparing data base and other near damage conditions, and classified in one of 3 classes. Random Forests precision in this research was 83% which is admissible for classifying. this algorithm can be used on other researches in the future time.