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Geometric accuracy of digital twins for structural health monitoring

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posted on 14.05.2020, 09:15 by Ruodan Lu, Chris Rausch, Marzia Bolpagni, Ioannis Brilakis, Carl T Haas
We present an exploratory analysis of the geometric accuracy of digital twins generated for existing infrastructure using point clouds. A fundamental feature of digital twins is their 3D geometry. The Level of Geometric Accuracy is a vital specification to measure the twinning quality of the resulting twins. However, there is a lack of a clear definition of the Level of Geometric Accuracy for geometric twins generated in the operation and maintenance stage, especially for structural health monitoring purposes. We critically review existing industry applications and twinning methods. To highlight the technical challenges with creating high-fidelity digital replicas, we present a case study of twinning a bridge using real-world point clouds. We do not provide conclusive methods or results, but we envisage potential twinning strategies to achieve the desired geometry accuracy. We also suggest evaluation metrics to assess twinning accuracy. This chapter aims to inform the future development of a geometric-accuracy-based evaluation system for use in twinning and updating processes. Since a major barrier for a fully automated twinning workflow is the lack of rigorous interpretation of “geometric accuracy” outside design environments, it is imperative to develop comprehensive standards to guide practitioners and researchers in order to achieve model certainty. As such, this chapter also aims to educate all stakeholders in order to minimize risk when drafting contracts and exchanging digital deliverables.


This research work is supported by the National Sciences and Engineering Research Council (NSERC), Mitacs and Edge Architects Ltd and Cambridge Trimble Fund.



  • Architecture, Building and Civil Engineering

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Structural Integrity and Failure




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© The Authors

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This chapter is distributed under the terms of the Creative Commons Attribution 3.0 License (https://creativecommons.org/licenses/by/3.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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9781838819811; 9781838819804; 9781838819828




Resat Oyguc; Faham Tahmasebinia


Dr Ruodan Lu. Deposit date: 11 May 2020