A comparative analysis of optimization algorithms for finite element model updating on numerical and experimental benchmarks

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dc.contributor.authorRaviolo, Davide
dc.contributor.authorCivera, Marco
dc.contributor.authorZanotti Fragonara, Luca
dc.date.accessioned2024-03-22T11:40:08Z
dc.date.available2024-03-22T11:40:08Z
dc.date.issued2023-12-01
dc.description.abstractFinite Element Model Updating (FEMU) is a common approach to model-based Non-Destructive Evaluation (NDE) and Structural Health Monitoring (SHM) of civil structures and infrastructures. Its application can be further utilized to produce effective digital twins of a permanently monitored structure. The FEMU concept, simple yet effective, involves calibrating and/or updating a numerical model based on the recorded dynamic response of the target system. This enables to indirectly estimate its material parameters, thus providing insight into its mass and stiffness distribution. In turn, this can be used to localize structural changes that may be induced by damage occurrence. However, several algorithms exist in the scientific literature for FEMU purposes. This study benchmarks three well-established global optimization techniques—namely, Generalized Pattern Search, Simulated Annealing, and a Genetic Algorithm application—against a proposed Bayesian sampling optimization algorithm. Although Bayesian optimization is a powerful yet efficient global optimization technique, especially suitable for expensive functions, it is seldom applied to model updating problems. The comparison is performed on numerical and experimental datasets based on one metallic truss structure built in the facilities of Cranfield University. The Bayesian sampling procedure showed high computational accuracy and efficiency, with a runtime of approximately half that of the alternative optimization strategies.en_UK
dc.identifier.citationRaviolo D, Civera M, Zanotti Fragonara L. (2024) A comparative analysis of optimization algorithms for finite element model updating on numerical and experimental benchmarks. Buildings, Volume 13, Issue 12, December 2023, Article number 3010en_UK
dc.identifier.eissn2075-5309
dc.identifier.urihttps://doi.org/10.3390/buildings13123010
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/21079
dc.language.isoen_UKen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectfinite element model updatingen_UK
dc.subjectmodel calibrationen_UK
dc.subjectdigital twinen_UK
dc.subjectgeneralized pattern searchen_UK
dc.subjectsimulated annealingen_UK
dc.subjectgenetic algorithmen_UK
dc.subjectBayesian expected improvementen_UK
dc.titleA comparative analysis of optimization algorithms for finite element model updating on numerical and experimental benchmarksen_UK
dc.typeArticleen_UK
dcterms.dateAccepted2023-11-27

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