Parameter selection for model updating with global sensitivity analysis

Zhaoxu Yuan, Peng Liang, Tiago Silva, Kaiping Yu, John E. Mottershead

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)
16 Downloads (Pure)


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.

Original languageEnglish
Pages (from-to)483-496
Number of pages14
JournalMechanical Systems and Signal Processing
Publication statusPublished - 15 Jan 2019


  • Global sensitivity
  • Model updating
  • Parameter selection
  • Uncertainty


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