To cite this version:M. Arnst, D. Clouteau, Marc Bonnet. Identification of non-parametric probabilistic models from measured transfer functions. Computer Methods in Applied Mechanics and Engineering, Elsevier, 2008Elsevier, , 197, pp.589-608. <10.1016Elsevier, /j.cma.2007 Inversion of probabilistic structural models using measured transfer functions ⋆
AbstractThis 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.