Structural Engineering and Mechanics

  • 제65권2호
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  • Pages.173-181
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  • 2018
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  • 1225-4568(pISSN)
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  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

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

  • Hwang, Yongmoon (Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology) ;
  • Jin, Seung-seop (Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology) ;
  • Jung, Ho-Yeon (Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology) ;
  • Kim, Sehoon (Department of Civil and Environmental Engineering, Sejong University) ;
  • Lee, Jong-Jae (Department of Civil and Environmental Engineering, Sejong University) ;
  • Jung, Hyung-Jo (Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology)
  • 투고 : 2017.12.15
  • 심사 : 2017.12.18
  • 발행 : 2018.01.25
⟨ 이전 논문 다음 논문 ⟩

초록

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.

키워드

과제정보

연구 과제 주관 기관 : Ministry of Land, Infrastructure and Transport (MOLIT)

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  2. Characterization of a carbon black rubber Poisson's ratio based on optimization technique applied in FEA data fit vol.76, pp.5, 2018, https://doi.org/10.12989/sem.2020.76.5.653
  3. Markov Chain Monte Carlo-based Bayesian method for nonlinear stochastic model updating vol.520, pp.None, 2018, https://doi.org/10.1016/j.jsv.2021.116595