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”
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).
London Digital Twin Research Centre (LDTRC) has joined 130+ leading industry and academic organisations from all over the world as a full member of the Digital Twin Consortium (DTC).
Digital Twin Consortium drives the adoption, use, interoperability and development of digital twin technology. It propels the innovation of digital twin technology through consistent approaches and open source development. It is committed to accelerating the market and guiding outcomes for users.
The goal of the consortium is to be The Authority in Digital Twin as it relates to policy, security, interoperability and overall development of digital twins. The consortium will define the ecosystem, standards requirements, architectures, open source code, identify gaps, and publish statements and opinions. This will be done in partnership between industry, academia and government in a collaborative open environment.
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 “Deep Learning-Based Detection of Structural Damage Using Time-Series Data” was accepted for publication in Q1 journal “Structure and Infrastructure Engineering” in July 2020.
Digital Twins embody the driving force behind the Fourth Industrial Revolution, that is the promise of bridging the physical world and its virtual counterpart in a way that enables full-duplex, real-time, reliable communication between the two entities. With the advent of Big Data, IIoT, Cyber Physical Factories, and Artificial Intelligence, this no longer looks like a far-fetched idea, becoming instead an increasingly relevant objective for researchers to achieve. However, building such a complex system requires a strong grasp of the technologies involved and good foresight into risks and issues that might pose a challenge along the way. In this context, this week’s meeting of the London Digital Twin Research Centre focused on discussing one of the most prominent challenges in Machine Learning: the Bias/Variance Trade-Off. Continue reading “Insight of the Week: Supervised Machine Learning and the Bias/Variance Trade-Off”
Prof Balbir Barn gave his thoughts on the risks and opportunities for universities under the COVID-19 situation and how digital twin technology can have an impact
“This year signals UK higher education’s very own anthropocene – a sector-defining point at which universities pivoted en masse to deliver emergency online teaching and new modes of working.” read more