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.

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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