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

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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.

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Authors and Affiliations

  1. Electrical and Electronic Engineering Department, University of Johannesburg, 524, Auckland Park, 2006, South Africa

    I. Boulkaibet & T. Marwala

  2. College of Engineering, Swansea University, Singleton Park, Swansea, SA2 8PP, UK

    M. I. Friswell & S. Adhikari

Authors
  1. I. Boulkaibet
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  2. T. Marwala
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  3. M. I. Friswell
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  4. S. Adhikari
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Corresponding author

Correspondence to I. Boulkaibet .

Editor information

Editors and Affiliations

  1. University of Bristol, Bristol, United Kingdom

    Dario Di Miao

  2. Virginia Polytechnical Institute, Blacksburg, USA

    Pablo Tarazaga

  3. DIISM, Università Politecnica delle Marche, Ancona, Italy

    Paolo Castellini

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© 2016 The Society for Experimental Mechanics, Inc.

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Boulkaibet, I., Marwala, T., Friswell, M.I., Adhikari, S. (2016). An Adaptive Markov Chain Monte Carlo Method for Bayesian Finite Element Model Updating. In: Di Miao, D., Tarazaga, P., Castellini, P. (eds) Special Topics in Structural Dynamics, Volume 6. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-319-29910-5_6

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  • DOI: https://doi.org/10.1007/978-3-319-29910-5_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-29909-9

  • Online ISBN: 978-3-319-29910-5

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