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Communication Dans Un Congrès Année : 2006

Identification of non-parametric probabilistic structural dynamics models from measured transfer functions

Maarten Arnst
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Marc Bonnet
Didier Clouteau

Résumé

This paper addresses the inversion of probabilistic models for the dynamical behaviour of structures using experimental data sets of measured frequency-domain transfer functions. The inversion is formulated as the minimization, with respect to the unknown parameters to be identified, of an objective function that measures a distance between the data and the model. Two such distances are proposed, based on either the loglikelihood function, or the relative entropy. As a comprehensive example, a probabilistic model for the dynamical behaviour of a slender beam is inverted using simulated data. The methodology is then applied to a civil and environmental engineering case history involving the identification of a probabilistic model for ground-borne vibrations from real experimental data.

Dates et versions

hal-00128916 , version 1 (04-02-2007)

Licence

Paternité - Pas d'utilisation commerciale

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Citer

Maarten Arnst, Marc Bonnet, Didier Clouteau. Identification of non-parametric probabilistic structural dynamics models from measured transfer functions. Seventh World Conference on Computational Mechanics, 2006, Los Angeles, United States. ⟨10.1016/j.cma.2007.08.011⟩. ⟨hal-00128916⟩
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