Bayesian Nonlinear Finite Element Model Updating of a Full-Scale Bridge-Column Using Sequential Monte Carlo

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dc.coverageDOI: 10.1007/978-3-030-47638-0_43
dc.creatorRamancha, Mukesh K.
dc.creatorAstroza, Rodrigo
dc.creatorConte, Joel P.
dc.creatorRestrepo, Jose I.
dc.creatorTodd, Michael D.
dc.date2020
dc.date.accessioned2025-11-18T19:48:18Z
dc.date.available2025-11-18T19:48:18Z
dc.description<p>Digital twin-based approaches for structural health monitoring (SHM) and damage prognosis (DP) are emerging as a powerful framework for intelligent maintenance of civil structures and infrastructure systems. Model updating of nonlinear mechanics-based Finite Element (FE) models using input and output measurement data with advanced Bayesian inference methods is an effective way of constructing a digital twin. In this regard, the nonlinear FE model updating of a full-scale reinforced-concrete bridge column subjected to seismic excitations applied by a large shake table is considered in this paper. This bridge column, designed according to US seismic design provisions, was tested on the NEES@UCSD Large High-Performance Outdoor Shake Table (LHPOST). The column was subjected to a sequence of ten recorded earthquake ground motions and was densely instrumented with an array of 278 sensors consisting of strain gauges, linear and string potentiometers, accelerometers and Global Positioning System (GPS) based displacement sensors to measure local and global responses during testing. This heterogeneous dataset is used to estimate/update the material and damping parameters of the developed mechanics-based distributed plasticity FE model of the bridge column. The sequential Monte Carlo (SMC) method (set of advanced simulation-based Bayesian inference methods) is used herein for the model updating process. The inherent architecture of SMC methods allows for parallel model evaluations, which is ideal for updating computationally expensive models.</p>eng
dc.descriptionDigital twin-based approaches for structural health monitoring (SHM) and damage prognosis (DP) are emerging as a powerful framework for intelligent maintenance of civil structures and infrastructure systems. Model updating of nonlinear mechanics-based Finite Element (FE) models using input and output measurement data with advanced Bayesian inference methods is an effective way of constructing a digital twin. In this regard, the nonlinear FE model updating of a full-scale reinforced-concrete bridge column subjected to seismic excitations applied by a large shake table is considered in this paper. This bridge column, designed according to US seismic design provisions, was tested on the NEES@UCSD Large High-Performance Outdoor Shake Table (LHPOST). The column was subjected to a sequence of ten recorded earthquake ground motions and was densely instrumented with an array of 278 sensors consisting of strain gauges, linear and string potentiometers, accelerometers and Global Positioning System (GPS) based displacement sensors to measure local and global responses during testing. This heterogeneous dataset is used to estimate/update the material and damping parameters of the developed mechanics-based distributed plasticity FE model of the bridge column. The sequential Monte Carlo (SMC) method (set of advanced simulation-based Bayesian inference methods) is used herein for the model updating process. The inherent architecture of SMC methods allows for parallel model evaluations, which is ideal for updating computationally expensive models. © 2020, The Society for Experimental Mechanics, Inc.spa
dc.identifierhttps://investigadores.uandes.cl/en/publications/754a7a81-6437-43b1-a39c-e0e4f990ac22
dc.identifier.urihttps://repositorio.uandes.cl/handle/uandes/55493
dc.languageeng
dc.publisherSpringer
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.sourceMao, Zhu (Ed.), Model Validation and Uncertainty Quantification, Volume 3 - Proceedings of the 38th IMAC, A Conference and Exposition on Structural Dynamics, 2020, p.389-397. Springer. [ISBN 9783030487782]
dc.subjectBayesian inference
dc.subjectDigital twin
dc.subjectEarthquake
dc.subjectFinite element
dc.subjectFull-scale structural systems
dc.subjectModel updating
dc.subjectSequential Monte Carlo
dc.subjectStructural health monitoring
dc.titleBayesian Nonlinear Finite Element Model Updating of a Full-Scale Bridge-Column Using Sequential Monte Carloeng
dc.typeConference contributioneng
dc.typeContribución a la conferenciaspa
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