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Parallelized adaptive Bayesian updating with structural reliability methods for inference of large engineering models

  • The reassessment of engineering structures, such as bridges, now increasingly involve the integration of models with realworld data. This integration aims to achieve accurate ‘as-is’ analysis within a digital twin framework. Bayesian model updating combines prior knowledge and data with models to enhance the modelling accuracy while consistently handling uncertainties. When updating large engineering models, numerical methods for Bayesian analysis present significant computational challenges due to the need for a substantial number of likelihood evaluations. The novelty of this contribution is to parallelize adaptive Bayesian Updating with Structural reliability methods combined with subset simulation (aBUS) to improve its computational efficiency. To demonstrate the efficiency and practical applicability of the proposed approach, we present a case study on the Maintalbrücke Gemünden, a large railway bridge. We leverage modal property data to update a linear-elastic dynamic structuralThe reassessment of engineering structures, such as bridges, now increasingly involve the integration of models with realworld data. This integration aims to achieve accurate ‘as-is’ analysis within a digital twin framework. Bayesian model updating combines prior knowledge and data with models to enhance the modelling accuracy while consistently handling uncertainties. When updating large engineering models, numerical methods for Bayesian analysis present significant computational challenges due to the need for a substantial number of likelihood evaluations. The novelty of this contribution is to parallelize adaptive Bayesian Updating with Structural reliability methods combined with subset simulation (aBUS) to improve its computational efficiency. To demonstrate the efficiency and practical applicability of the proposed approach, we present a case study on the Maintalbrücke Gemünden, a large railway bridge. We leverage modal property data to update a linear-elastic dynamic structural model of the bridge. The parallelized aBUS approach significantly reduces computational time, making Bayesian updating of large engineering models feasible within reasonable timeframes. The improved efficiency allows for a wider implementation of Bayesian model updating in structural health monitoring and maintenance decision support systems.zeige mehrzeige weniger

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Metadaten
Autor*innen:Patrick SimonORCiD, Ronald SchneiderORCiD, Matthias BaeßlerORCiD, Guido Morgenthal
Dokumenttyp:Zeitschriftenartikel
Veröffentlichungsform:Verlagsliteratur
Sprache:Englisch
Titel des übergeordneten Werkes (Englisch):Advances in Structural Engineering
Jahr der Erstveröffentlichung:2025
Organisationseinheit der BAM:7 Bauwerkssicherheit
7 Bauwerkssicherheit / 7.2 Ingenieurbau
Veröffentlichende Institution:Bundesanstalt für Materialforschung und -prüfung (BAM)
Verlag:Sage
Erste Seite:1
Letzte Seite:26
DDC-Klassifikation:Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Ingenieurbau
Freie Schlagwörter:Bayesian model updating; Bayesian updating with structural reliability methods; Modal analysis; Parallelization; Railway bridge; Structural health monitoring
Themenfelder/Aktivitätsfelder der BAM:Infrastruktur
Infrastruktur / Verkehrsinfrastrukturen
DOI:10.1177/13694332251346848
URN:urn:nbn:de:kobv:b43-633686
ISSN:1369-4332
ISSN:2048-4011
Verfügbarkeit des Dokuments:Datei für die Öffentlichkeit verfügbar ("Open Access")
Lizenz (Deutsch):License LogoCreative Commons - CC BY-NC - Namensnennung - Nicht kommerziell 4.0 International
Datum der Freischaltung:13.06.2025
Referierte Publikation:Ja
Datum der Eintragung als referierte Publikation:23.06.2025
Schriftenreihen ohne Nummerierung:Wissenschaftliche Artikel der BAM
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