In this paper, a non-probabilistic method based on fuzzy logic is used toupdate finite element models (FEMs). Model updating techniques use the measureddata to improve the accuracy of numerical models of structures. However, themeasured data are contaminated with experimental noise and the models areinaccurate due to randomness in the parameters. This kind of aleatoryuncertainty is irreducible, and may decrease the accuracy of the finite elementmodel updating process. However, uncertainty quantification methods can be usedto identify the uncertainty in the updating parameters. In this paper, theuncertainties associated with the modal parameters are defined as fuzzymembership functions, while the model updating procedure is defined as anoptimization problem at each {\alpha}-cut level. To determine the membershipfunctions of the updated parameters, an objective function is defined andminimized using two metaheuristic optimization algorithms: ant colonyoptimization (ACO) and particle swarm optimization (PSO). A structural exampleis used to investigate the accuracy of the fuzzy model updating strategy usingthe PSO and ACO algorithms. Furthermore, the results obtained by the fuzzyfinite element model updating are compared with the Bayesian model updatingresults.
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