Digital Twins for Bridge Management through the Integrating of Computer Vision Techniques and Finite Element Models
Through this project, we have proposed the development of an innovative integrated solution for damage identification (detection, localization, quantification) of bridge structures using a non-contact and non-interruptive image-based measurement scheme. Computer vision techniques will be used to extract information from camera images, which will be used for joint finite element (FE) model updating and vehicular load estimation. The information collected from raw images includes the location of vehicles passing on the bridge and the resulting bridge response time histories at sparse measurement locations. Through the model updating process, the component-level mechanics-based material parameters of the bridge are estimated and used to assess the location and extent of damage. The updated model can be maintained as an evolving digital twin of the real-world asset and utilized by the stakeholders for maintenance, preservation, and management of highway assets including load rating, retrofit design, and post-disaster assessment.