The Development of Digital Twins for Bridges to Assist with Maintenance and Monitoring

Research Team: Ertugrul Taciroglu (lead) and Farid Ghahari

University: UCLA

Problem Statement: The U.S. has a total of 614,387 bridges, nearly 40% of which are 50 years or older. The average age of bridges in the U.S. continues to climb and many bridges are approaching the end of their nominally 50-year-long service lives. In 2016, one in eleven of bridges were structurally deficient and more than one in eight were functionally obsolete. Replacement or rehabilitation of such a large number of bridges cannot be achieved overnight. Since structurally deficient and obsolete bridges will likely remain in service for a significant time period, ensuring public safety necessitates close monitoring of the bridges system to expand the service life, guide the inspection process, and prioritize maintenance and rehabilitation decisions. Current monitoring and maintenance is mainly based on visual inspections, which is labor-intensive, costly, time-consuming, and subjective (i.e., prone to human errors). There is a clear need for developing and implementing low-cost solutions for bridge assessment to facilitate the inspection and management process, and to reduce associated costs and risks.

Project Description: This project will develop an innovative, integrated solution for damage identification (i.e., detection, localization, quantification) of bridge structures using a non-contact, image-based measurement scheme. Computer vision techniques will be used to extract information from raw images, which will be used for joint finite element (FE) model updating and vehicular load estimation. The updated model can be maintained as a digital twin for damage diagnosis of the structural system. The digital twin can be utilized for global load rating and operational condition and post-disaster assessments through the life-cycle of the bridge, so as to help with bridge management decision strategies.

Status: In Progress

Budget: $87,602