Digital Twins for civil engineering: the Clifton Suspension Bridge (UK)

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)


Society is dependent on aging infrastructure, which usually operates outside its expected life. Replacing this infrastructure is often an unviable option due to its cost and disruption. A structure’s operational life might be extended if the features of its aging are better understood, enabling preventive maintenance to compensate. Digital Twins (the continuous comparison between sensor measurements and a mathematical model) are one way of enabling this sort of data-driven decision making. However, despite the possibilities for this technology, its take up amongst industry has been slow, in part because infrastructure managers are unsure of how the technology will support them.

This work develops a methodological framework to enhance this uptake in the field of systems engineering and the system development life cycle, using the developed knowledge to inform how an operational Digital Twin should be created. The requirements capture is the most important part of any system design development process. We present a Digital Twin development method, grounded firmly in a thorough requirements capture, and illustrate how those requirements inform many of the later design decisions. We then present our method through a case study of the Clifton Suspension Bridge, UK.

Our method provides a series of actionable steps, the completion of which will facilitate the creation of a Digital Twin able to support operational decisions. By fulfilling the requirements of infrastructure managers, we hope to encourage the uptake of the technology
Original languageEnglish
Title of host publicationProceedings of the 10th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 10
Place of PublicationPorto
Number of pages6
Publication statusPublished - 30 Jun 2021


  • Digital Twin
  • structural health monitoring
  • finite element model
  • decision support
  • bridge management
  • resilience


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