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

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


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.


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


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Copyright information

© The Society for Experimental Mechanics, Inc. 2019

Authors and Affiliations

  • M. Sherri
    • 1
  • I. Boulkaibet
    • 2
  • T. Marwala
    • 2
  • M. I. Friswell
    • 3
  1. 1.Department of Mechanical Engineering ScienceUniversity of JohannesburgAuckland ParkSouth Africa
  2. 2.Institute of Intelligent Systems, University of JohannesburgAuckland ParkSouth Africa
  3. 3.College of Engineering, Swansea University, Bay CampusSwanseaUK

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