Finite element model updating using the shadow Hybrid Monte Carlo technique

I. Boulkaibet, L. Mthembu, T. Marwala, M. I. Friswell, S. Adhikari

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationSpecial Topics in Structural Dynamics - Proceedings of the 31st IMAC, A Conference on Structural Dynamics, 2013
Pages489-498
Number of pages10
DOIs
Publication statusPublished - 2013
Event31st International Modal Analysis Conference on Structural Dynamics, IMAC 2013 - Garden Grove, CA, United States
Duration: 11 Feb 201314 Feb 2013

Publication series

NameConference Proceedings of the Society for Experimental Mechanics Series
Volume6
ISSN (Print)2191-5644
ISSN (Electronic)2191-5652

Conference

Conference31st International Modal Analysis Conference on Structural Dynamics, IMAC 2013
Country/TerritoryUnited States
CityGarden Grove, CA
Period11/02/1314/02/13

Keywords

  • Bayesian
  • Finite element model updating
  • Hybrid monte carlo method
  • Markov chain monte carlo
  • Sampling
  • Shadow hybrid monte carlo

ASJC Scopus subject areas

  • General Engineering
  • Computational Mechanics
  • Mechanical Engineering

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