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Article Dans Une Revue Computer Methods in Applied Mechanics and Engineering Année : 2008

Identification of non-parametric probabilistic models from measured transfer functions

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.
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Dates et versions

hal-00169555 , version 1 (18-09-2007)
hal-00169555 , version 2 (09-08-2008)

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M. Arnst, Didier Clouteau, Marc Bonnet. Identification of non-parametric probabilistic models from measured transfer functions. Computer Methods in Applied Mechanics and Engineering, 2008, 197, pp.589-608. ⟨10.1016/j.cma.2007.08.011⟩. ⟨hal-00169555v2⟩
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