Abstract Summary
Structural damage detection is an inverse problem to identify and quantify structural damage from measurement data by discovering the change of structural mechanical parameters such as stiffness and damping coefficients. Recently, a novel deep learning framework named physics-informed neural networks (PINNs) has been proposed and successfully applied to solve inverse problems of various linear/nonlinear partial differential equations (PDEs) by integrating physical information such as constraints and governing equations as prior information. In this study, we proposed a PINNs framework to exploit a new direction of structural damage detection. Specifically, a multi-output neural network model is built to predict the dynamic response such as displacement, velocity, and acceleration of the structure. The mechanical parameters to be discovered are initialized and updated together with the neural network model parameters. Then, the structural physical model and boundary conditions are embedded by calculating the residuals of governing equations and boundary conditions as parts of the loss function to constrain the relationships between the dynamic responses. The residual between the predicted dynamic response and measurement data is also used as another part of the loss function. The total loss function is minimized by an optimizer so the predicted dynamic response from the model can satisfy the constraints of the governing equations and boundary conditions and represent the measured response simultaneously. Through numerical experiments of a single-degree-of-freedom system, we demonstrate that the proposed method can successfully identify potential structural mechanical parameters and quantitatively detect structural damage. The influence of noise in the measurement data on the detection results is also analyzed. Through numerical experiments of a 6-DOF system, we demonstrate that the proposed method can be utilized to detect the structural damage of the complex structural system.
