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An Adaptive Markov Chain Monte Carlo Method for Bayesian Finite Element Model Updating

  • I. Boulkaibet
  • 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

In this paper, an adaptive Markov Chain Monte Carlo (MCMC) approach for Bayesian finite element model updating is presented. This approach is known as the Adaptive Hamiltonian Monte Carlo (AHMC) approach. The convergence rate of the Hamiltonian/Hybrid Monte Carlo (HMC) algorithm is high due to its trajectory which is guided by the derivative of the posterior probability distribution function. This can lead towards high probability areas in a reasonable period of time. However, the HMC performance decreases when sampling from posterior functions of high dimension and when there are strong correlations between the uncertain parameters. The AHMC approach, a locally adaptive version of the HMC approach, allows efficient sampling from complex posterior distribution functions and in high dimensions. The efficiency and accuracy of the AHMC method are investigated by updating a real structure.

Keywords

Finite element model updating Bayesian Markov Chain Monte Carlo Hybrid Monte Carlo Adaptive 

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Copyright information

© The Society for Experimental Mechanics, Inc. 2016

Authors and Affiliations

  • I. Boulkaibet
    • 1
  • T. Marwala
    • 1
  • M. I. Friswell
    • 2
  • S. Adhikari
    • 2
  1. 1.Electrical and Electronic Engineering DepartmentUniversity of JohannesburgAuckland ParkSouth Africa
  2. 2.College of Engineering, Swansea UniversitySwanseaUK

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