TY  - JOUR
A1  - Simon, Patrick
A1  - Schneider, Ronald
A1  - Baeßler, Matthias
A1  - Morgenthal, Guido
T1  - Parallelized adaptive Bayesian updating with structural reliability methods for inference of large engineering models
JF  - Advances in Structural Engineering
N2  - 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 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.
KW  - Bayesian model updating
KW  - Bayesian updating with structural reliability methods
KW  - Structural health monitoring
KW  - Parallelization
KW  - Modal analysis
KW  - Railway bridge
PY  - 2025
UR  - https://nbn-resolving.org/urn:nbn:de:kobv:b43-633686
DO  - https://doi.org/10.1177/13694332251346848
SN  - 1369-4332
SN  - 2048-4011
SP  - 1
EP  - 26
PB  - Sage
AN  - OPUS4-63368
LA  - eng
AD  - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER  -