HAL : hal-00169555, version 2

DOI : 10.1016/j.cma.2007.08.011

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Computer Methods in Applied Mechanics and Engineering 197 (2008) 589-608

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v1 (18-09-2007)

v2 (09-08-2008)

Identification of non-parametric probabilistic models from measured transfer functions

M. Arnst 1, D. Clouteau 1, Marc Bonnet 2

(2008)

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.

1 :	Laboratoire de mécanique des sols, structures et matériaux (MSSMat)
	CNRS : UMR8579 – Ecole Centrale Paris
2 :	Laboratoire de mécanique des solides (LMS)
	CNRS : UMR7649 – Polytechnique - X – Mines ParisTech

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:

Physique/Mécanique/Mécanique des solides Sciences de l'ingénieur/Mécanique/Mécanique des solides

hal-00169555, version 2

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Contributeur : Marc Bonnet <>

Soumis le : Samedi 9 Août 2008, 00:46:00

Dernière modification le : Samedi 9 Août 2008, 06:45:38