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Inversion of probabilistic structural models using measured transfer functions

Authors
Journal
Computer Methods in Applied Mechanics and Engineering
0045-7825
Publisher
Elsevier
Publication Date
Volume
197
Identifiers
DOI: 10.1016/j.cma.2007.08.011
Keywords
  • Probabilistic Modelling
  • Inverse Problem
  • Identification
  • Relative Entropy
  • Likelihood
  • Non-Parametric Probabilistic Model
Disciplines
  • Ecology
  • Engineering
  • Geography
  • Mathematics

Abstract

Abstract 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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