Parameter selection for model updating with global sensitivity analysis

Yuan, Zhaoxu ORCID: 0000-0002-0400-3423, Liang, Peng, Silva, Tiago, Yu, Kaiping and Mottershead, JE ORCID: 0000-0003-1279-2562
(2019) Parameter selection for model updating with global sensitivity analysis. Mechanical Systems and Signal Processing, 115. 483 - 496.

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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.

Item Type: Article
Uncontrolled Keywords: model updating, parameter selection, uncertainty, global sensitivity
Depositing User: Symplectic Admin
Date Deposited: 08 Jun 2018 09:54
Last Modified: 01 Sep 2022 07:31
DOI: 10.1-16/j.ymssp.2018.05.048
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