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A Bayesian Framework for Simulation-based Digital Twins of Bridges

  • Simulation-based digital twins have emerged as a powerful tool for evaluating the mechanical response of bridges. As virtual representations of physical systems, digital twins can provide a wealth of information that complements traditional inspection and monitoring data. By incorporating virtual sensors and predictive maintenance strategies, they have the potential to improve our understanding of the behavior and performance of bridges over time. However, as bridges age and undergo regular loading and extreme events, their structural characteristics change, often differing from the predictions of their initial design. Digital twins must be continuously adapted to reflect these changes. In this article, we present a Bayesian framework for updating simulation-based digital twins in the context of bridges. Our approach integrates information from measurements to account for inaccuracies in the simulation model and quantify uncertainties. Through its implementation and assessment, this work demonstrates the potential for digital twins to provide a reliable and up-to-date representation of bridge behavior, helping to inform decision-making for maintenance and management.
Metadaten
Author:Daniel Andrés Arcones, Martin WeiserORCiD, Faidon-Stelios Koutsourelakis, Jörg F. Unger
Document Type:Article
Parent Title (English):EUROSTRUCT 2023:European Association on Quality Control of Bridges and Structures: Digital Transformation in Sustainability
Volume:6
Issue:5
First Page:734
Last Page:740
Year of first publication:2023
DOI:https://doi.org/10.1002/cepa.2177
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