On 21 August 2021, Prof. Huan Nguyen delivered a keynote talk on “Digital Twins for Infrastructure and Smart Manufacturing” at the virtual 3rd International Conference on Automation, Mechatronics, and Robotics (ICAMEROB 2021) to discuss the digital twin technology and showcase the recent results at the London Digital Twin Research Centre on the development of Digital Twin for infrastructure and smart manufacturing. The talk fits well into the conference theme “Breakthrough Innovation, Creativity and Resilience by Embracing Change & Transformation through Research,” and attracted great attention from audience from Far East in this new Digital Twin technology.
Structural Health Monitoring with Digital Twin: State of Play
By Prof Guido De Roeck (KU Leuven), external advisor and project partner at LDTRC
With more than 500 scientific journal and conference papers, Prof. De Roeck is a world-famous expert in vibration based damage detection, dynamic system identification and soil structure interaction and vehicle bridge interaction. In this short talk, Prof. De Roeck addresses relevant topics in our Newton Fund project on digital twin for structural health monitoring (SHM) between Middlesex Uni (UK) and Uni. of Transport and Communications (Vietnam), in which KU Leven is an associated partner.
He outlines recent development in sensoring techniques in SHM, non-model and model-based methods for vibration-based damage detection. For example, the non-model based methods can be the alarm/alerting just based on the pure or preprocessed measurement data, or the stochastic treatment to distinguish temperature influence from real structural damage. The model based approach can be the digital twin structure that is adapted according to measurement results. The adaptation, updating, calibration can use global optimization methods like Particle Swarm Optimization (PSO) , Genetic Algorithm (GA). Prof. De Roeck also discusses the digital transformation (with references to relevant projects in Europe) for predictive maintenance of important infrastructures in case of extreme events, such us, earthquakes, collapse of Morandi Bridge in Genova, Italy in 2018.
2021 Annual Workshop: Transforming Industry and Society with Digital Twins
The London Digital Twin Research Centre would like to extend an invitation to all the Digital Twin researchers and enthusiasts from industry and academia to attend our annual 2021 workshop which is held online on June 4th, 2021. This virtual “Workshop on Transforming Industry and Society with Digital Twins” brings together experts from industry and academia to share their valuable insights regarding the adoption of the Digital Twin technology across different industries, from structural health monitoring, pandemic management, smart campuses through to health and wellbeing. The domains covered in this event provide opportunities and research challenges that the future maturation of digital twin technology demands. The virtual workshop represents an excellent opportunity for networking for Digital Twin enthusiasts to share ideas for future developments in digital twins.
Programme: DT workshop_June2021
Eventbrite registration (free): Link
Time/date: 10am-4pm on 4th June 2021
Update after the workshop [9 Jan 2021]: news on workshop
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Research Grant on Restoration of Heritage using Digital Twin
In a continuation of a series of Middlesex-Egypt collaborations, London Digital Twin Research Centre (Middlesex University) has won a new Institutional Links grant under the Newton Fund Impact Scheme. The grant is led by Dr. Noha Saleeb, Associate Professor in Digital Technologies and Construction in partnership with Prof. Mohamed Marzouk, Professor of Construction Engineering and Management, Cairo University Egypt. The funding delivered by the British Council supports activities that aid in the development of Egypt’s economy using new digital twin technologies in alignment with the UN’s sustainability goals.
The project will be conducted simultaneously with the case study on Heritage assets in the UK and Egypt for mutual national benefits with the collaboration of industry and government parties namely Historic Environment Scotland, BurroHappold and Ministry of Housing Egypt.
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Digital Twin for SHM – Demonstrations and Results
Newton Fund Institutional Links project “Digital Twin Models for Structural Health Monitoring”
On Wednesday 3rd March 2021, the project team will organise a workshop in Hanoi to continue the sharing of the outcomes to the relevant stakeholders and partners in the country.
Several talks and demonstrations on Cloud-based Digital Twin for bridges (both scaled down models in the lab and the actual bridges) will be presented during the event.
Agenda: here
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DT Workshop in Hanoi
On 21 January 2021, in a series of dissemination activities of the Newton Fund International Links project ‘Digital Twin for Structural Health Monitoring,’ the Vietnamese partner, University of Transport and Communication (UTC), organised a workshop to present the project outcomes to the local and national science/industrial communities in civil engineering, computer science, geology, and mechanics.
Digital Twin launched for Structural Health Monitoring
At London Digital Twin Research Centre, we have engineered a Digital Twin (DT) model for Structural Health Monitoring, which is able to collect, analyse, and visualise data in a near real-time fashion. The cloud-based DT was trialed at this initial stage on Amazon Cloud services (AWS) (continuously deployed at the following IP address: https://52.14.81.171:5000/ and currently being developed over the Siemens’ Mindsphere platform. For more detail of work flow and main components, read on.
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Data Collection for SHM Digital Twin: Vibration Measurement of Bridge Structures
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.
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ICSCE 2020: Digital Twin for Infrastructures in Vietnam
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”
Data-driven structural damage detection approach for Digital twin-Structural health monitoring
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”