TY - GEN
T1 - Sampling techniques in bayesian finite element model updating
AU - Boulkaibet, I.
AU - Marwala, T.
AU - Mthembu, L.
AU - Friswell, M. I.
AU - Adhikari, S.
PY - 2012
Y1 - 2012
N2 - Recent papers in the field of Finite Element Model (FEM) updating have highlighted the benefits of Bayesian techniques. The Bayesian approaches are designed to deal with the uncertainties associated with complex systems, which is the main problem in the development and updating of FEMs. This paper highlights the complexities and challenges of implementing any Bayesian method when the analysis involves a complicated structural dynamic model. In such systems an analytical Bayesian formulation might not be available in an analytic form; therefore this leads to the use of numerical methods, i.e. sampling methods. The main challenge then is to determine an efficient sampling of the model parameter space. In this paper, three sampling techniques, the Metropolis-Hastings (MH) algorithm, Slice Sampling and the Hybrid Monte Carlo (HMC) technique, are tested by updating a structural beam model. The efficiency and limitations of each technique is investigated when the FEM updating problem is implemented using the Bayesian Approach. Both MH and HMC techniques are found to perform better than the Slice sampling when Young's modulus is chosen as the updating parameter. The HMC method gives better results than MH and Slice sampling techniques, when the area moment of inertias and section areas are updated.
AB - Recent papers in the field of Finite Element Model (FEM) updating have highlighted the benefits of Bayesian techniques. The Bayesian approaches are designed to deal with the uncertainties associated with complex systems, which is the main problem in the development and updating of FEMs. This paper highlights the complexities and challenges of implementing any Bayesian method when the analysis involves a complicated structural dynamic model. In such systems an analytical Bayesian formulation might not be available in an analytic form; therefore this leads to the use of numerical methods, i.e. sampling methods. The main challenge then is to determine an efficient sampling of the model parameter space. In this paper, three sampling techniques, the Metropolis-Hastings (MH) algorithm, Slice Sampling and the Hybrid Monte Carlo (HMC) technique, are tested by updating a structural beam model. The efficiency and limitations of each technique is investigated when the FEM updating problem is implemented using the Bayesian Approach. Both MH and HMC techniques are found to perform better than the Slice sampling when Young's modulus is chosen as the updating parameter. The HMC method gives better results than MH and Slice sampling techniques, when the area moment of inertias and section areas are updated.
KW - Bayesian
KW - Finite element model updating
KW - Hybrid monte carlo method
KW - Markov chain monte carlo
KW - Metropolis-hastings method
KW - Sampling
KW - Slice sampling method
UR - http://www.scopus.com/inward/record.url?scp=84861752194&partnerID=8YFLogxK
U2 - 10.1007/978-1-4614-2431-4_8
DO - 10.1007/978-1-4614-2431-4_8
M3 - Conference contribution
AN - SCOPUS:84861752194
SN - 9781461424307
T3 - Conference Proceedings of the Society for Experimental Mechanics Series
SP - 75
EP - 83
BT - Topics in Model Validation and Uncertainty Quantification - Proceedings of the 30th IMAC, A Conference on Structural Dynamics, 2012
T2 - 30th IMAC, A Conference on Structural Dynamics, 2012
Y2 - 30 January 2012 through 2 February 2012
ER -