Developing Digital Twins for Real-Time Bridge Structural Health Monitoring by Integrating Scan-to-BrIM Remote Sensing Technologies, Photogrammetry-Based 3D Scanning, Finite Element Modeling, and IoT Sensing

Bridge structural health monitoring (SHM) is a crucial task for ensuring the safety, reliability, and longevity of bridges. While plenty of digital twin (DT)-based bridge management systems have been developed, they commonly suffer from low efficiency with digital bridge model generation and insufficiency to visualize the structural responses globally. Thus, this paper proposes a framework integrating scan-to-BrIM (bridge information model)-based finite element analysis with the Internet of Things (IoT) to develop numerical simulation-enabled DT for addressing the two aforementioned limitations. The proposed framework was tested on a prototype 3D-printed bridge through a case study to dynamically visualize structural deformation induced by thermal loads for the entire bridge model. Experimental results revealed that the photogrammetry-based 3D-scanned bridge model could efficiently support finite element model generation, and the real-time structural response visualization had high representativeness. This paper ultimately contributes to the body of knowledge by improving the existing practices of DT-based bridge SHM for facilitating intelligent bridge infrastructure management.