Sequential surrogate modeling for efficient finite element model updating

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Despite the numerous studies concerning finite element model updating (FEMU), a challenging computational cost issue persists. Therefore, surrogate modeling has recently gained considerable attention in FEMU. Conventionally, surrogate models are constructed by identical samples for all outputs. It is very inefficient and subjective, if various response-surfaces exhibit even for identical parameters. Accordingly, we propose a sequential surrogate modeling for FEMU. It uses infill criteria to guide sampling for updating surrogate models automatically. The proposed method is successful to construct the different response-surfaces and apply FEMU. It is promising for constructing surrogate models with minimal user intervention and tremendous computational efficiency. (C) 2016 Elsevier Ltd. All rights reserved
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Issue Date
2016-05
Language
English
Article Type
Article
Keywords

RESPONSE-SURFACE METHOD; MONTE-CARLO-SIMULATION; STOCHASTIC PREDICTIONS; GLOBAL OPTIMIZATION; STRUCTURAL IDENTIFICATION; POLYMERIC NANOCOMPOSITES; FRAMEWORK; UNCERTAINTY; RELIABILITY; PARAMETERS

Citation

COMPUTERS & STRUCTURES, v.168, pp.30 - 45

ISSN
0045-7949
DOI
10.1016/j.compstruc.2016.02.005
URI
http://hdl.handle.net/10203/209000
Appears in Collection
CE-Journal Papers(저널논문)
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