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”
by Prof. Huan Nguyen, Director, and Dr. Ramona Trestian, LDTRC
with thanks to our external collaborators, Dr. Duc To, Viavi Solutions and Dr. Mallik Tatipamula, CTO, Ericsson Silicon Valley
Although many countries have started the initial phase of rolling out 5G, it is still in its infancy with researchers from both academia and industry facing the challenges of developing to its full potential. With the support of Artificial Intelligence (AI), development of digital transformation through the notion of a ‘Digital Twin’ has been taking off in many industries such as smart manufacturing, oil & gas, constructions, bio-engineering, and automotive. However, Digital Twins remain relatively new for 5G networks, despite the obvious potential in helping develop and deploy the complex 5G environment. At London Digital Twin Research Centre, we investigate these topics and discover how Digital Twin could be a powerful tool to fulfil the potentials of 5G networks and beyond. Some market challenges with open questions exist: (1) how to speed up the deployment of new (but complex) 5G technologies? (2) how to provide flexible testbed facilities with high availability? and (3) who is willing to invest in the expensive 5G deployment with uncertain returns.
Our findings and discussions are to appear soon in IEEE Communications Magazine.
Prof. Nguyen said “It is amazing to find out that the development of a DT framework at our LDTRC to unify different twins is very much aligning with the ‘connecting the Twins’ work task at the National Digital Twin programme. We see great opportunities to work together to achieve common goals set out at both Centres towards developing a national Digital Twin.”
by Dr. Hrishikesh Venkataraman, External Collaborator and Project Partner, Indian Institute of Information Technology (IIIT)
The Indian Institute of Information Technology (IIIT), represented by Dr. Hrishikesh Venkataraman and Dr. Raja Vara Prasad, is an international collaborator of the London Digital Twin Research Centre (LDTRC). The research conducted in Sri City, India, aims to support and drive forward the advancements in our Digital Twin for Industry 4.0 project, with an extended focus on anomaly detection mechanisms that can be integrated in the Digital Twin for manufacturing processes.
As part of the project activity funded by the UK-India Education and Research Initiative (UKIERI) and the Department of Science and Technology (DST), India, the research paper “CATS: Cluster-Aided Two-Step Approach for Anomaly Detection in Smart Manufacturing” was accepted for publication in The Fourth International Conference on Computing and Network Communications (CoCoNet’20).