A Digital Twin Assisted and Embedded Strain Gauge Monitoring System

In this work, a strain monitoring system (strip) for assessing the road pavement distress under vehicle loads was developed. The system consists of the sensing element, the data processing and storage unit, and a graphical user interface with post- processing features. The sensing elements were designed to be adhesively bonded on the pavement and are protected by an encapsulating plastic strip. Strain data are sent to a digital reconstruction (the Digital Twin) of a real-life asset (the pavement model) that is frequently and automatically updated through data sampling. This tool provides functionalities to monitor and optimize assets and make informed and data-based decisions, in the context of day-to-day operative conditions and after extreme events. These data not only include sensor data, but also regularly revalidated structural reliability indices formulated on the grounds of the frequently updated Digital Twin model.

• Record URL:
  • https://doi.org/10.1016/j.trpro.2023.11.686
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S2352146523009845
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/23521465
• Supplemental Notes:
  • © 2023 The Author(s). Published by Elsevier B.V. Abstract reprinted with permission of Elsevier.
• Authors:
  • Sotiriadis, George
  • Kotzakolios, Thanasis
  • Kostopoulos, Vassilis
  • Gemou, Maria
• Conference:
  • Transport Research Arena Conference (TRA Lisbon 2022)
  • Location: Lisbon , Portugal
  • Date: 2022-11-14 to 2022-11-17
• Publication Date: 2023

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References;
• Pagination: pp 2038-2045
• Serial:
  • Transportation Research Procedia
  • Volume: 72
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 2352-1465
  • Serial URL: http://www.sciencedirect.com/science/journal/23521465/
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Embedded systems; Machine learning; Monitoring; Optimization; Pavement maintenance; Service life
• Subject Areas: Data and Information Technology; Highways; Maintenance and Preservation; Pavements;

Filing Info

• Accession Number: 01911897
• Record Type: Publication
• Files: TRIS
• Created Date: Mar 15 2024 4:34PM