Journal of the Earthquake Engineering Society of Korea (한국지진공학회논문집)

Earthquake Engineering Society of Korea (한국지진공학회)
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Enhanced System Identification and Finite Element Model Updating for Concrete Random Fields

콘크리트 무작위장에 대한 시스템 식별 및 유한요소모델 업데이트

  • Goh, Wonhui (Department of Civil & Environmental Engineering, Seoul National University) ;
  • Chae, Yunbyeong (Department of Civil & Environmental Engineering, Institute of Construction and Environmental Engineering, Seoul National University)
  • 고원희 (서울대학교 건설환경공학부) ;
  • 채윤병 (서울대학교 건설환경공학부, 서울대학교 건설환경종합연구소)
  • Received : 2024.11.10
  • Accepted : 2025.02.03
  • Published : 2025.05.01
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Abstract

This study proposes an improved method for updating finite element models (FEM) by incorporating the random field characteristics of concrete material properties in reinforced concrete structures. Traditional FEM often assumes homogeneous material properties, which can lead to significant discrepancies between predicted and actual dynamic responses, especially in structures where the Young's modulus (E) of concrete varies due to factors like curing conditions, material composition, and construction methods. We employed a Gaussian random field model and a system identification (SI) technique to address this limitation to optimize sensor placement. We developed an FEM updating method that incorporates the spatial variability of concrete elasticity. This optimization allowed for a more accurate capture of dynamic properties across various structural locations, resulting in FEM updates that reflect concrete's inherent heterogeneity. The proposed method was validated through numerical examples, comparing dynamic response accuracy in models before and after updating. Results demonstrated that error values, measured in terms of maximum value error and normalized root mean squared Error (NRMSE), were significantly reduced in the updated models compared to the pre-update model. This approach effectively addresses the limitations of homogeneous assumptions in FEM.

Keywords

Acknowledgement

본 연구는 2022년도 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임(No. RS-2022-00144434).

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