Fuzzy finite element model updating using metaheuristic optimization
  algorithms
  In this paper, a non-probabilistic method based on fuzzy logic is used to
update finite element models (FEMs). Model updating techniques use the measured
data to improve the accuracy of numerical models of structures. However, the
measured data are contaminated with experimental noise and the models are
inaccurate due to randomness in the parameters. This kind of aleatory
uncertainty is irreducible, and may decrease the accuracy of the finite element
model updating process. However, uncertainty quantification methods can be used
to identify the uncertainty in the updating parameters. In this paper, the
uncertainties associated with the modal parameters are defined as fuzzy
membership functions, while the model updating procedure is defined as an
optimization problem at each {\alpha}-cut level. To determine the membership
functions of the updated parameters, an objective function is defined and
minimized using two metaheuristic optimization algorithms: ant colony
optimization (ACO) and particle swarm optimization (PSO). A structural example
is used to investigate the accuracy of the fuzzy model updating strategy using
the PSO and ACO algorithms. Furthermore, the results obtained by the fuzzy
finite element model updating are compared with the Bayesian model updating
results.
