LDTRC becomes a member of the Digital Twin Consortium

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.

Structural Damage Detection Using Digital Twin Technology

by Hung Dang, Fellow Research , LDTRC

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.

As the construction process is becoming more and more digitalized with the help of digital design packages such as 3D computer aid design (CAD), building information modeling (BIM), Finite element analysis (FEA), the Digital Twin emerges as one of the most exciting technologies which create a high-fidelity digital entity evolving synchronously with the real structures throughout their entire life cycle. Thus, the monitoring service evolves from periodical, generic, and physical law-based models to real-time, personalized, and data-driven ones, thus optimizing maintenance strategy, increasing reliability and safety of the structure, and extending its remaining service life.

This study developed a modulated workflow flexible in switching different Deep Learning algorithms and fusing data from multiple sensors, thus enabling to 1) compare the practical effectiveness of various DL algorithms, 2) perform multiple structural damage detection tasks such as damage localization, damage severity, and 3) handle time-series data polluted by noises through the noise injection learning method.

Measured vibration signal

Deep neural network-based models are alternative and complementary methods directly using measured vibrational signals without requiring an additional step to extract structural characteristics such as modal identification. Moreover, it is flexible to conduct different damage detection tasks with the same neural network architecture but the last output layer to be fine-tuned per task. Once the models are trained with appropriate datasets and their parameters are stably determined, they could deliver monitoring assessment in a near real-time fashion due to the fast inference time.

Example of detection results

[1]  Vasco de Gama bridge, https://arab-trip.com/wp-content/uploads/2019/07

Insight of the Week: Supervised Machine Learning and the Bias/Variance Trade-Off

by Stefan Viorel Mihai, Research Assistant, LDTRC

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”

The digitalisation of the university

  • 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