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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 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, thisSimulation-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.zeige mehrzeige weniger

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Metadaten
Autor*innen:Daniel Andrés Arcones, M. Weiser, F. Koutsourelakis, Jörg F. UngerORCiD
Dokumenttyp:Zeitschriftenartikel
Veröffentlichungsform:Graue Literatur
Sprache:Englisch
Titel des übergeordneten Werkes (Englisch):ce/papers Proceedings in civil engineering
Jahr der Erstveröffentlichung:2023
Organisationseinheit der BAM:7 Bauwerkssicherheit
7 Bauwerkssicherheit / 7.7 Modellierung und Simulation
Veröffentlichende Institution:Bundesanstalt für Materialforschung und -prüfung (BAM)
Verlag:Ernst & Sohn
Verlagsort:Berlin
Jahrgang/Band:6
Ausgabe/Heft:5
Erste Seite:734
Letzte Seite:740
DDC-Klassifikation:Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Ingenieurbau
Freie Schlagwörter:Bayesian Inference; Bridge Monitoring; Digital Twins; Uncertainty Quantification
Themenfelder/Aktivitätsfelder der BAM:Infrastruktur
Veranstaltung:Eurostruct 2023
Veranstaltungsort:Vienna, Austria
Beginndatum der Veranstaltung:25.09.2023
Enddatum der Veranstaltung:29.09.2023
DOI:10.1002/cepa.2177
URN:urn:nbn:de:kobv:b43-586803
URL:https://eurostruct.org/eurostruct-2023/
ISSN:2509-7075
Verfügbarkeit des Dokuments:Datei für die Öffentlichkeit verfügbar ("Open Access")
Lizenz (Deutsch):License LogoCreative Commons - CC BY - Namensnennung 4.0 International
Datum der Freischaltung:26.10.2023
Referierte Publikation:Nein
Schriftenreihen ohne Nummerierung:Wissenschaftliche Artikel der BAM
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