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

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Title: Parallelized adaptive Bayesian updating with structural reliability methods for inference of large engineering models.
Authors: Simon, Patrick1 (AUTHOR) patrick.simon@bam.de, Schneider, Ronald1 (AUTHOR), Baeßler, Matthias1 (AUTHOR), Morgenthal, Guido2 (AUTHOR)
Source: Advances in Structural Engineering. Jan2026, Vol. 29 Issue 1, p150-175. 26p.
Subjects: Structural reliability, Engineering models, Bayesian field theory, Adaptive sampling (Statistics), Digital twin, Decision support systems, Mathematical optimization
Abstract: The reassessment of engineering structures, such as bridges, now increasingly involve the integration of models with real-world 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. [ABSTRACT FROM AUTHOR]
Copyright of Advances in Structural Engineering is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: <searchLink fieldCode="JN" term="%22Advances+in+Structural+Engineering%22">Advances in Structural Engineering</searchLink>. Jan2026, Vol. 29 Issue 1, p150-175. 26p.
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  Data: The reassessment of engineering structures, such as bridges, now increasingly involve the integration of models with real-world 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Advances in Structural Engineering is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1177/13694332251346848
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      – Code: eng
        Text: English
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        PageCount: 26
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      – SubjectFull: Structural reliability
        Type: general
      – SubjectFull: Engineering models
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      – SubjectFull: Bayesian field theory
        Type: general
      – SubjectFull: Adaptive sampling (Statistics)
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      – SubjectFull: Digital twin
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      – SubjectFull: Decision support systems
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      – SubjectFull: Mathematical optimization
        Type: general
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      – TitleFull: Parallelized adaptive Bayesian updating with structural reliability methods for inference of large engineering models.
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            NameFull: Schneider, Ronald
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            – D: 01
              M: 01
              Text: Jan2026
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              Y: 2026
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