Damage detection of 3D structures using nearest neighbor search method

An innovative damage identification method using the nearest neighbor search method to assess 3D structures is presented. The frequency response function was employed as the input parameters to detect the severity and place of damage in 3D spaces since it includes the most dynamic characteristics of the structures. Two-dimensional principal component analysis was utilized to reduce the size of the frequency response function data. The nearest neighbor search method was employed to detect the severity and location of damage in different damage scenarios. The accuracy of the approach was verified using measured data from an experimental test; moreover, two asymmetric 3D numerical examples were considered as the numerical study. The superiority of the method was demonstrated through comparison with the results of damage identification by using artificial neural network. Different levels of white Gaussian noise were used for polluting the frequency response function data to investigate the robustness of the methods against noise-polluted data. The results indicate that both methods can efficiently detect the damage properties including its severity and location with high accuracy in the absence of noise, but the nearest neighbor search method is more robust against noisy data than the artificial neural network.