A Differential Evolution Markov Chain Monte Carlo Algorithm for Bayesian Model Updating

M. Sherri, I. Boulkaibet, T. Marwala, M. I. Friswell

Research output: Contribution to journalConference articlepeer-review

10 Citations (Scopus)

Abstract

The use of the Bayesian tools in system identification and model updating paradigms has been increased in the last 10 years. Usually, the Bayesian techniques can be implemented to incorporate the uncertainties associated with measurements as well as the prediction made by the finite element model (FEM) into the FEM updating procedure. In this case, the posterior distribution function describes the uncertainty in the FE model prediction and the experimental data. Due to the complexity of the modeled systems, the analytical solution for the posterior distribution function may not exist. This leads to the use of numerical methods, such as Markov Chain Monte Carlo techniques, to obtain approximate solutions for the posterior distribution function. In this paper, a Differential Evolution Markov Chain Monte Carlo (DE-MC) method is used to approximate the posterior function and update FEMs. The main idea of the DE-MC approach is to combine the Differential Evolution, which is an effective global optimization algorithm over real parameter space, with Markov Chain Monte Carlo (MCMC) techniques to generate samples from the posterior distribution function. In this paper, the DE-MC method is discussed in detail while the performance and the accuracy of this algorithm are investigated by updating two structural examples.

Original languageEnglish
Pages (from-to)115-125
Number of pages11
JournalConference Proceedings of the Society for Experimental Mechanics Series
Volume5
DOIs
Publication statusPublished - 2019
Event36th IMAC, A Conference and Exposition on Structural Dynamics, 2018 - FL, United States
Duration: 12 Feb 201815 Feb 2018

Keywords

  • Bayesian model updating
  • Differential evolution
  • Finite element model
  • Markov Chain Monte Carlo
  • Posterior distribution function

ASJC Scopus subject areas

  • Mechanical Engineering
  • General Engineering
  • Computational Mechanics

Fingerprint

Dive into the research topics of 'A Differential Evolution Markov Chain Monte Carlo Algorithm for Bayesian Model Updating'. Together they form a unique fingerprint.

Cite this