A convolutional neural network deep learning method for model class selection
This provides a tool for structural health monitoring applications, though it appears incremental as it applies existing deep learning methods to a specific domain problem.
The paper tackles model class selection for structural health monitoring by developing a convolutional neural network that uses only response signals (without input information) to classify new signals, achieving successful selection even with slight signal variations in both linear and nonlinear dynamic systems and a 3D building model.
The response-only model class selection capability of a novel deep convolutional neural network method is examined herein in a simple, yet effective, manner. Specifically, the responses from a unique degree of freedom along with their class information train and validate a one-dimensional convolutional neural network. In doing so, the network selects the model class of new and unlabeled signals without the need of the system input information, or full system identification. An optional physics-based algorithm enhancement is also examined using the Kalman filter to fuse the system response signals using the kinematics constraints of the acceleration and displacement data. Importantly, the method is shown to select the model class in slight signal variations attributed to the damping behavior or hysteresis behavior on both linear and nonlinear dynamic systems, as well as on a 3D building finite element model, providing a powerful tool for structural health monitoring applications.