Zitieren Sie bitte immer diesen URN: urn:nbn:de:kobv:b43-586803
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.…
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): | Creative Commons - CC BY - Namensnennung 4.0 International |
Datum der Freischaltung: | 26.10.2023 |
Referierte Publikation: | Nein |
Schriftenreihen ohne Nummerierung: | Wissenschaftliche Artikel der BAM |