1. Parameter selection for model updating with global sensitivity analysis.
- Author
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Yuan, Zhaoxu, Liang, Peng, Silva, Tiago, Yu, Kaiping, and Mottershead, John E.
- Subjects
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SENSITIVITY analysis , *STOCHASTIC models , *FINITE element method , *GAUSSIAN processes , *COVARIANCE matrices - Abstract
The problem of selecting parameters for stochastic model updating is one that has been studied for decades, yet no method exists that guarantees the ‘correct’ choice. In this paper, a method is formulated based on global sensitivity analysis using a new evaluation function and a composite sensitivity index that discriminates explicitly between sets of parameters with correctly-modelled and erroneous statistics. The method is applied successfully to simulated data for a pin-jointed truss structure model in two studies, for the cases of independent and correlated parameters respectively. Finally, experimental validation of the method is carried out on a frame structure with uncertainty in the position of two masses. The statistics of mass positions are confirmed by the proposed method to be correctly modelled using a Kriging surrogate. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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