By Lan Nguyen and Dr Thanh Bui (UTC), Newton Fund IL project partner in Vietnam
Since its launch in February 2019, the Newton-funded project “Digital twin model for structural health monitoring (SHM) of lifeline infrastructures in Vietnam” has achieved major milestones, including the data collection and vibration measurement of structures. In the last few months, the Vietnamese partner has conducted vibration measurement of two of the most heavily used bridges in Vietnam- Chuong Duong bridge in the Northern capital Hanoi and Can Tho bridge in the Southern Mekong Delta region. Let us look back at how it was done.
Our Newton Fund project “Digital twin model for structural health monitoring of lifeline infrastructures in Vietnam” has now advanced into the development stage. The prototype concept of the Digital Twin has been trialled in the lab with a basic bridge model.
The partner in Vietnam, University of Transport and Communications, has organised the Third International Conference on Sustainability in Civil Engineering (ICSCE 2020) in Hanoi on 26th/27th November as one of the planned dissemination events. The Conference Chair, Dr Thanh T. Bui (the project’s PI in Vietnam) has led the committee team and dedicated one session for project topic of “Digital Twin for Structural Health Monitoring,” contributing to the general conference theme “Building a Green Infrastructure for Living.” Continue reading “ICSCE 2020: Digital Twin for Infrastructures in Vietnam”
As part of the project activity funded by the Newton Fund Institutional Links through the U.K. Department of Business, Energy, and Industrial Strategy and managed by the British Council under Grant 429715093, Research paper ” Data-Driven Structural Health Monitoring using feature Fusion and Hybrid Deep Learning” was accepted for publication in Q1 journal “IEEE Transactions on Automation Science and Engineering” in October 2020.
Toward a Digital Twin model automatically monitoring the operational state of large-scale structures in a near-real-time fashion, it requires a Structural Damage Detection (SDD) approach, both time-efficient, and data-efficient, while still maintaining accurate detection results. Conventional methods either resort to time-consuming sophisticated preprocessing techniques such as experimental modal analysis or data-eager image-based deep learning duplicating multiple times raw measured data. Continue reading “Data-driven structural damage detection approach for Digital twin-Structural health monitoring”