Abstract
A new model-updating parameter selection method based on global sensitivity analysis is presented in this work. A specifically designed evaluation function is used for the probability that the sample fits the distribution of test data. In contrast to other parameter selection methods the test-data information is introduced to the parameter selection procedure. Global sensitivity analysis is performed and a set of composite indices for parameter selection is calculated. The parameters are selected based on the values of these composite indices. The method is validated using simulation data from a pin-jointed truss structure model. The cases of independent and correlated parameters are studied and the presented method is shown to be effective for both.
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