Title:
Damage Identification in Concrete Structures Using Physics-Informed Neural Networks (PINNs)
Author(s):
Zhang
Publication:
Web Session
Volume:
ws_F23_Zhang.pdf
Issue:
Appears on pages(s):
Keywords:
DOI:
Date:
10/29/2023
Abstract:
In this research, a physics-informed neural networks (PINNs) framework is developed for the damage detection of continuous structural systems based on limited and noisy sensor data. The PINNs framework integrates sensor data and knowledge of the physics of the system by embedding the sensor data, governing partial differential equations, and boundary conditions into the loss function of the neural network (NN) architecture. Through minimizing the physics-informed loss function, the NNs parameters and unknown structural parameters can be estimated, and hence the full state of the system is then predicted by the trained PINNs. The PINNs framework is demonstrated through application for the damage detection of a three-span continuous concrete beam subject to a dynamic moving load. The goal of the application is to detect the damage, which was modeled as the reduction of the flexural rigidity of each span of the concrete beam, as well as to predict the full state of the concrete beam. It is observed that the PINNs framework can accurately detect the damage and estimate the structural state from limited and noisy sensor data.