.png){kind=icon}
.png){kind=icon}
Return
Parameter selection for model updating with global sensitivity analysis
.png){kind=icon}
OA
AI
DOI：10.1016/j.ymssp.2018.05.048.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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. (C) 2018 Elsevier Ltd. All rights reserved.
Keywords:
Model updating
Parameter selection
Uncertainty
Global sensitivity
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
8.9
Papers:
1.3W
Citations:
6.6W
Organization
View more organizations