  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.
