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Finite Element Model Updating Using the Shadow Hybrid Monte Carlo Technique

  • I. Boulkaibet
  • L. Mthembu
  • T. Marwala
  • M. I. Friswell
  • S. Adhikari
Conference paper
Part of the Conference Proceedings of the Society for Experimental Mechanics Series book series (CPSEMS)

Abstract

Recent research in the field of Finite Element Model (FEM) updating has advocated the benefits of adopting Bayesian analysis techniques. These techniques are well suited to dealing with the uncertainties associated with complex systems. However, Bayesian formulations require the evaluation of the Posterior Distribution Function (pdf) which may not be available in analytical form. This is the case in FEM updating. In such cases sampling methods can provide good approximations of the Posterior distribution when implemented in the Bayesian context. In this paper, we propose the use of the Shadow Hybrid Monte Carlo (SHMC) technique for the problem of determining the most probable FEM updating parameters for the given data. SHMC is based on Hybrid Monte Carlo (HMC) and designed to improve sampling by allowing for larger system sizes and time steps. The accuracy and efficiency of this sampling method is tested on the updating of a structural beam models.

Keywords

Bayesian Sampling Finite Element Model updating Markov Chain Monte Carlo Hybrid Monte Carlo method Shadow Hybrid Monte Carlo 

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Copyright information

© The Society for Experimental Mechanics, Inc. 2013

Authors and Affiliations

  • I. Boulkaibet
    • 1
  • L. Mthembu
    • 1
  • T. Marwala
    • 1
  • M. I. Friswell
    • 2
  • S. Adhikari
    • 2
  1. 1.The Centre For Intelligent System Modelling (CISM), Electrical and Electronics Engineering DepartmentUniversity of JohannesburgJohannesburgSouth Africa
  2. 2.College of EngineeringSwansea UniversitySwanseaUK

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