Structural damage identification based on convolution neural network

Here, a convolution neural network was used to extract structural features, identify damage and solve problems of structural damage identification. The effectiveness of this method was verified with IASC-ASCE SHM Benchmark Phase 1 simulation data. Then, comparing the same classifier accuracies for energy characteristics of the convolution neural network, the wavelet packet and the first 5 IMFs obtained by EMD, advantages of the convolution neural network in automatically extracting features were proved. In analyzing the robustness of features' automatic extraction of the convolution neural network, it was found that the characteristic anti-noise ability of a single noise data training mode is limited. In order to acquire the better characteristic anti-noise ability, a mixed noise training mode was proposed. The validity of this training mode was verified using the sample data with noise of 0%-50% to obtain good recognition results. At the same time, it was found in visualization of the convolution's kernel features that the convolution kernel of the mixed noise training mode can identify more orders of frequency information. © 2019, Editorial Office of Journal of Vibration and Shock. All right reserved.