Real-time bridge performance evaluation based on crowdsourcing and learning

Funding: 2024: $110,000
2025: $115,000
2023: $135,000

Project Member(s): Zhu, X., Wang, Y., Li, J.

Funding or Partner Organisation: Australian Research Council (ARC Discovery Projects)
Australian Research Council (ARC Discovery Projects)

Start year: 2023

Summary: This project aims to develop a novel strategy utilizing the real-time measurements from moving vehicles and bridges and integrating the bridge-moving load models with machine learning to evaluate the safety and operational performance of bridges. The proposed research is the first essential study on integrating the bridge moving load models with transfer learning in an innovative physics-based data analytics. The project will provide an engineer-friendly low cost monitoring system for its deployment, management and maintenance of existing transport infrastructure. This study will contribute significantly to the community in the form of safe and resilient transportation infrastructure in operation.

Publications:

Guo, X-Y, Fang, S-E, Zhu, X & Li, J 2025, 'A semi-Markov process based digital twin for safety evaluation of cable-stayed bridges with cable corrosion', Advanced Engineering Informatics, vol. 65, pp. 103270-103270.
View/Download from: Publisher's site

FOR Codes: Road infrastructure and networks, Expanding knowledge in engineering, Structural engineering , Structural dynamics

Acknowledgement of Country

UTS acknowledges the Gadigal people of the Eora Nation, the Boorooberongal people of the Dharug Nation, the Bidiagal people and the Gamaygal people, upon whose ancestral lands our university stands. We would also like to pay respect to the Elders both past and present, acknowledging them as the traditional custodians of knowledge for these lands.