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Interval model updating with irreducible uncertainty using the Kriging predictor

Stochastic model updating methods have emerged recently which take into account aleatoric (irreducible) uncertainties. However, the majority of the methods use probabilistic models. Funded by the European Union's 6th Framework Project, this paper proposed the use of possibilistic models (e.g. interval model) and developed an interval model updating technique. The results are validated experimentally. The method requires less measurement data for system identification, which led to significant saving in cost without sacrificing accuracy. This research has led to collaboration with DLR (German Aerospace Centre, http://www.dlr.de ).