Structural model updating using vibration measurements

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 residu-als. The computational cost for estimating the gradients and Hessian is shown to be independ-ent 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 com-putational developments are illustrated by updating finite element models of multi-span rein-forced concrete bridges using simulated modal data.

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Identifiers

ORCID for Evangelos Ntotsios: orcid.org/0000-0001-7382-0948

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