TY - JOUR A1 - Andrés Arcones, Daniel A1 - Weiser, M. A1 - Koutsourelakis, F. A1 - Unger, Jörg F. T1 - A Bayesian Framework for Simulation-based Digital Twins of Bridges JF - ce/papers Proceedings in civil engineering N2 - 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 tructural 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. T2 - Eurostruct 2023 CY - Vienna, Austria DA - 25.09.2023 KW - Digital Twins KW - Bayesian Inference KW - Bridge Monitoring KW - Uncertainty Quantification PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-586803 UR - https://eurostruct.org/eurostruct-2023/ DO - https://doi.org/10.1002/cepa.2177 SN - 2509-7075 VL - 6 IS - 5 SP - 734 EP - 740 PB - Ernst & Sohn CY - Berlin AN - OPUS4-58680 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -