archimedes-Artificial Intelligence, Data Science, Algorithms-greece

 
Artificial Intelligence
 
Data Science
 
Algorithms

[Archimedes Talks Series]Machine-learning for structural dynamics and structural health monitoring

Dates
2024-07-23 11:00 - 13:00
Venue
Artemidos 1 - Amphitheater
Title: Machine-learning for structural dynamics and structural health monitoring

Presenter: Dr. George Tsialiamanis (Dynamics Research Group, University of Sheffield, UK)


Abstract: In recent years, the field of structural dynamics has transitioned from physics-based approaches to the inclusion of data in the process of modelling systems. Data-driven techniques, including machine learning, have offered solutions to structural-dynamics problems which would be extremely challenging or even impossible to solve with traditional approaches, e.g. non-linear modal analysis. A major subfield of structural dynamics which is inherently connected to the acquisition of data, that of structural health monitoring (SHM), is founded on the use of machine-learning techniques. The field of SHM aims at maintaining the operational condition of structures to ensure safety and minimise potential economic and societal costs. Data acquired from sensors placed on structures and a wide range of algorithms are exploited to gain insights in the condition of the structure, to identify and localise potential damage, and to perform prognosis regarding the remaining useful life of the structure.

Bio: Dr. Tsialiamanis is a lecturer (Assistant professor) in the Dynamics Research Group (DRG) of the Mechanical Engineering Department of the University of Sheffield.

Dr. Tsialiamanis completed his MEng at the Civil Engineering School of the National Technical University of Athens. He then joined the DRG of the University of Sheffield as a PhD student and a Marie Skłodowska-Curie early stage researcher of the Dynamic Virtualisation (DyVirt) project. He has been a visiting researcher at ETH Zurich and has acquired funding from the non-destructive evaluation division of the Engineering Institute of the Los Alamos National Laboratory and for an extended research visit. Dr. Tsialiamanis is also a member of the steering board committee of the Welsh Digital Twin Network, which aims at developing a functional digital twin of Wales to improve the everyday life of people in the region.

The research of the DRG is focused on modelling structural dynamics. A great part of the research is conducted on the structural health monitoring of structures which is inseparably connected to the acquisition and exploitation of data. The group employs machine-learning algorithms to deal with situations where physical knowledge is absent and the formalisation of the physics is challenging because of the presence of uncertainties. A great deal of research is also conducted on the Verification and Validation of models, which is based on performing experiments to confirm the accuracy and robustness of models. For this purpose, the Laboratory of Verification and Validation (LVV) is used, where equipment is available to test structures under varying environmental conditions, such as varying temperatures, wind loadings, wave loading, etc.


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Vision

To position Greece as a leading player in AI and Data Science

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Mission

To build an AI Excellence Hub in Greece where the international research community can connect, groundbreaking ideas can thrive, and the next generation of scientists emerges, shaping a brighter future for Greece and the world

 

ARCHIMEDES IMPACT

Figures as of October 2026

Highlights will be added soon.

 

NEWS

 
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The project “ARCHIMEDES Unit: Research in Artificial Intelligence, Data Science and Algorithms” with code OPS 5154714 is implemented by the National Recovery and Resilience Plan “Greece 2.0” and is funded by the European Union – NextGenerationEU.

Greece 2.0 – Funded by the European Union – NextGenerationEU