A Bayesian Framework for Simulation‐based Digital Twins of Bridges
نویسندگان
چکیده
Abstract Simulation‐based digital twins have emerged as a powerful tool for evaluating the mechanical response of bridges. As virtual representations physical systems, can provide wealth information that complements traditional inspection and monitoring data. By incorporating sensors predictive maintenance strategies, they potential to improve our understanding behavior performance bridges over time. However, age undergo regular loading extreme events, their structural characteristics change, often differing from predictions initial design. Digital must be continuously adapted reflect these changes. In this article, we present Bayesian framework updating simulation‐based in context Our approach integrates measurements account inaccuracies simulation model quantify uncertainties. Through its implementation assessment, work demonstrates reliable up‐to‐date representation bridge behavior, helping inform decision‐making management.
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ژورنال
عنوان ژورنال: ce/papers
سال: 2023
ISSN: ['2509-7075']
DOI: https://doi.org/10.1002/cepa.2177