000013319 001__ 13319
000013319 005__ 20161114160336.0
000013319 04107 $$aeng
000013319 046__ $$k2009-06-22
000013319 100__ $$aPapadimitriou, C.
000013319 24500 $$aStructural model updating using vibration measurements

000013319 24630 $$n2.$$pComputational Methods in Structural Dynamics and Earhquake Engineering
000013319 260__ $$bNational Technical University of Athens, 2009
000013319 506__ $$arestricted
000013319 520__ $$2eng$$aA multi-objective optimization framework is presented for updating finite element models of structures based on vibration measurements. The method results in multiple Pareto optimal structural models that are consistent with the measured data and the residuals used to measure the discrepancies between the measured and the finite element model predicted characteristics. The relation between the multi-objective identification method and conventional single-objective weighted residuals methods for model updating is discussed. Computational algorithms for the fast, efficient and reliable solution of the resulting optimization problems are presented. The algorithms are classified to gradient-based, evolutionary strategies and hybrid techniques. In particular, efficient algorithms are introduced for reducing the computational cost involved in estimating the gradients and Hessians of the objective functions representing the modal residuals. The computational cost for estimating the gradients and Hessian is shown to be independent of the number of structural model parameters. The methodology is particularly efficient to system with several number of model parameters and large number of DOFs where repeated gradient and Hessian evaluations are computationally time consuming. Theoretical and computational developments are illustrated by updating finite element models of multi-span reinforced concrete bridges using simulated modal data.

000013319 540__ $$aText je chráněný podle autorského zákona č. 121/2000 Sb.
000013319 653__ $$aModel Updating, Structural Identification, Multi-Objective Optimization, Pareto Optimality. Abstract. A multi-objective optimization framework is presented for updating finite element models of structures based on vibration measurements. The method results in multiple Pareto optimal structural models that are consistent with the measured data and the residuals used to measure the discrepancies between the measured and the finite element model predicted characteristics. The relation between the multi-objective identification method and conventional single-objective weighted residuals methods for model updating is discussed. Computational algorithms for the fast, efficient and reliable solution of the resulting optimization problems are presented. The algorithms are classified to gradient-based, evolutionary strategies and hybrid techniques. In particular, efficient algorithms are introduced for reducing the computational cost involved in estimating the gradients and Hessians of the objective functions representing the modal residuals. The computational cost for estimating the gradients and Hessian is shown to be independent of the number of structural model parameters. The methodology is particularly efficient to system with several number of model parameters and large number of DOFs where repeated gradient and Hessian evaluations are computationally time consuming. Theoretical and computational developments are illustrated by updating finite element models of multi-span reinforced concrete bridges using simulated modal data.

000013319 7112_ $$aCOMPDYN 2009 - 2nd International Thematic Conference$$cIsland of Rhodes (GR)$$d2009-06-22 / 2009-06-24$$gCOMPDYN2009
000013319 720__ $$aPapadimitriou, C.$$iNtotsios, E.
000013319 8560_ $$ffischerc@itam.cas.cz
000013319 8564_ $$s304267$$uhttps://invenio.itam.cas.cz/record/13319/files/CD467.pdf$$yOriginal version of the author's contribution as presented on CD, section: Identification methods in structural dynamics - i (MS).
000013319 962__ $$r13074
000013319 980__ $$aPAPER