Robust Updating of Uncertain Computational Models Using Experimental Modal Analysis

In this paper, a methodology is presented to perform the robust updating of complex uncertain dynamic systems with respect to modal experimental data in the context of structural dynamics. Because both model uncertainties and parameter uncertainties must be considered in the computational model, the uncertain computational model is constructed by using the nonparametric probabilistic approach. We present an extension to the probabilistic case of the input-error methodology for modal analysis adapted to the deterministic updating problem. It is shown that such an extension to the robust-updating context induces some conceptual difficulties and is not straightforward. The robust-updating formulation leads us to solve a mono-objective optimization problem in the presence of inequality probabilistic constraints. A numerical application is presented to show the efficiency of the proposed method.

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