Vibration-based structural state identification by a 1-dimensional convolutional neural network

Deep learning has ushered in many breakthroughs in vision-based detection via convolutional neural networks (CNNs), but the vibration-based structural damage detection by CNN remains being refined. Thus, this study proposes a simple one-dimensional CNN that detects tiny local structural stiffness and mass changes, and validates the proposed CNN on actual structures. Three independent acceleration databases are established based on a T-shaped steel beam, a short steel girder bridge (in test field), and a long steel girder bridge (in service). The raw acceleration data are not pre-processed and are directly used as the training and validation data. The well-trained CNN almost perfectly identifies the locations of small local changes in the structural mass and stiffness, demonstrating the high sensitivity of the proposed simple CNN to tiny structural state changes in actual structures. The convolutional kernels and outputs of the convolutional and max pooling layers are visualized and discussed as well.