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Bayesian model calibration and damage detection for a digital twin of a bridge demonstrator
- Using digital twins for decision making is a very promising concept which combines simulation models with corresponding experimental sensor data in order to support maintenance decisions or to investigate the reliability. The quality of the prognosis strongly depends on both the data quality and the quality of the digital twin. The latter comprises both the modeling assumptions as well as the correct parameters of these models. This article discusses the challenges when applying this concept to realmeasurement data for a demonstrator bridge in the lab, including the data management, the iterative development of the simulation model as well as the identification/updating procedure using Bayesian inference with a potentially large number of parameters. The investigated scenarios include both the iterative identification of the structural model parameters as well as scenarios related to a damage identification. In addition, the article aims at providing all models and data in aUsing digital twins for decision making is a very promising concept which combines simulation models with corresponding experimental sensor data in order to support maintenance decisions or to investigate the reliability. The quality of the prognosis strongly depends on both the data quality and the quality of the digital twin. The latter comprises both the modeling assumptions as well as the correct parameters of these models. This article discusses the challenges when applying this concept to realmeasurement data for a demonstrator bridge in the lab, including the data management, the iterative development of the simulation model as well as the identification/updating procedure using Bayesian inference with a potentially large number of parameters. The investigated scenarios include both the iterative identification of the structural model parameters as well as scenarios related to a damage identification. In addition, the article aims at providing all models and data in a reproducibleway such that other researcher can use this setup to validate their methodologies.…
Autor*innen: | Thomas Titscher, T. van Dijk, Daniel Kadoke, Annika Robens-Radermacher, Ralf HerrmannORCiD, Jörg F. UngerORCiD |
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Dokumenttyp: | Zeitschriftenartikel |
Veröffentlichungsform: | Verlagsliteratur |
Sprache: | Englisch |
Titel des übergeordneten Werkes (Englisch): | Engineering reports |
Jahr der Erstveröffentlichung: | 2023 |
Organisationseinheit der BAM: | 7 Bauwerkssicherheit |
7 Bauwerkssicherheit / 7.2 Ingenieurbau | |
7 Bauwerkssicherheit / 7.7 Modellierung und Simulation | |
8 Zerstörungsfreie Prüfung | |
8 Zerstörungsfreie Prüfung / 8.1 Sensorik, mess- und prüftechnische Verfahren | |
Veröffentlichende Institution: | Bundesanstalt für Materialforschung und -prüfung (BAM) |
Verlag: | Wiley |
Verlagsort: | Hoboken, NJ |
Aufsatznummer: | e12669 |
Erste Seite: | 1 |
Letzte Seite: | 27 |
Freie Schlagwörter: | Damage detection; Estimation; Finite element analysis; Load identification; Model updating; System identification; Variational Bayesian statistics |
Themenfelder/Aktivitätsfelder der BAM: | Infrastruktur |
DOI: | 10.1002/eng2.12669 |
URN: | urn:nbn:de:kobv:b43-574496 |
ISSN: | 2577-8196 |
Verfügbarkeit des Dokuments: | Datei für die Öffentlichkeit verfügbar ("Open Access") |
Lizenz (Deutsch): | Creative Commons - CC BY - Namensnennung 4.0 International |
Datum der Freischaltung: | 11.05.2023 |
Referierte Publikation: | Ja |
Datum der Eintragung als referierte Publikation: | 22.05.2023 |
Paper des Monats: | Ja |
Schriftenreihen ohne Nummerierung: | Wissenschaftliche Artikel der BAM |