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Supervised by Ministry of Industry and Information Technology of The People's Republic of China Sponsored by Harbin Institute of Technology Editor-in-chief Yu Zhou ISSNISSN 1005-9113 CNCN 23-1378/T

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Related citation:Yang Liu,De-Jun Wang,Jun Ma,Yang Li.Performance Comparison of Two Meta-Model for the Application to Finite Element Model Updating of Structures[J].Journal of Harbin Institute Of Technology(New Series),2014,21(3):68-78.DOI:10.11916/j.issn.1005-9113.2014.03.010.
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Performance Comparison of Two Meta-Model for the Application to Finite Element Model Updating of Structures
Author NameAffiliation
Yang Liu School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, China 
De-Jun Wang School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, China 
Jun Ma School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, China 
Yang Li School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, China 
Abstract:
To investigate the application of meta-model for finite element (FE) model updating of structures, the performance of two popular meta-model, i.e., Kriging model and response surface model (RSM), were compared in detail. Firstly, above two kinds of meta-model were introduced briefly. Secondly, some key issues of the application of meta-model to FE model updating of structures were proposed and discussed, and then some advices were presented in order to select a reasonable meta-model for the purpose of updating the FE model of structures. Finally, the procedure of FE model updating based on meta-model was implemented by updating the FE model of a truss bridge model with the measured modal parameters. The results showed that the Kriging model was more proper for FE model updating of complex structures.
Key words:  finite element model updating of structures  meta-model  kriging model  response surface model
DOI:10.11916/j.issn.1005-9113.2014.03.010
Clc Number:TB123
Fund:

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