Work

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

Year: 2025

Type: article

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.

Source: Advances in Structural Engineering

DOI: 10.1177/13694332251346848

Authors: Patrick Simon, Ronald Schneider, Matthias Baeßler, Guido Morgenthal

Institutions: Federal Institute For Materials Research and Testing, Bauhaus-Universität Weimar

Language: English

Topic: Structural Health Monitoring Techniques

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $4,150