Experimental validation of FE model updating based on multi-objective optimization using the surrogate model

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In this paper, finite element (FE) model updating based on multi-objective optimization with the surrogate model for a steel plate girder bridge is investigated. Conventionally, FE model updating for bridge structures uses single-objective optimization with finite element analysis (FEA). In the case of the conventional method, computational burden occurs considerably because a lot of iteration are performed during the updating process. This issue can be addressed by replacing FEA with the surrogate model. The other problem is that the updating result from single-objective optimization depends on the condition of the weighting factors. Previous studies have used the trial-and-error strategy, genetic algorithm, or user's preference to obtain the most preferred model; but it needs considerable computation cost. In this study, the FE model updating method consisting of the surrogate model and multi-objective optimization, which can construct the Pareto-optimal front through a single run without considering the weighting factors, is proposed to overcome the limitations of the single-objective optimization. To verify the proposed method, the results of the proposed method are compared with those of the single-objective optimization. The comparison shows that the updated model from the multi-objective optimization is superior to the result of single-objective optimization in calculation time as well as the relative errors between the updated model and measurement.
Publisher
TECHNO-PRESS
Issue Date
2018-01
Language
English
Article Type
Article
Keywords

STRUCTURAL IDENTIFICATION; GENETIC ALGORITHM; ELEMENT; UNCERTAINTY

Citation

STRUCTURAL ENGINEERING AND MECHANICS, v.65, no.2, pp.173 - 181

ISSN
1225-4568
DOI
10.12989/sem.2018.65.2.173
URI
http://hdl.handle.net/10203/241547
Appears in Collection
CE-Journal Papers(저널논문)
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