An adaptive markov Chain monte carlo method for Bayesian finite element model updating

Boulkaibet, I., Marwala, T., Friswell, M.I. and Adhikari, S. (2016) An adaptive markov Chain monte carlo method for Bayesian finite element model updating. In: Di Miao, D., Tarazaga, P. and Castellini, P. (eds.) Special Topics in Structural Dynamics, Volume 6: Proceedings of the 34th IMAC, A Conference and Exposition on Structural Dynamics 2016. Series: Conference proceedings of the Society for Experimental Mechanics series. Springer: Cham, pp. 55-65. ISBN 9783319299099 (doi: 10.1007/978-3-319-29910-5_6)

Full text not currently available from Enlighten.

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.

Item Type:Book Sections
Status:Published
Glasgow Author(s) Enlighten ID:Adhikari, Professor Sondipon
Authors: Boulkaibet, I., Marwala, T., Friswell, M.I., and Adhikari, S.
College/School:College of Science and Engineering > School of Engineering > Infrastructure and Environment
Publisher:Springer
ISBN:9783319299099
Published Online:04 May 2016

University Staff: Request a correction | Enlighten Editors: Update this record