Structural Engineering and Mechanics

  • 제74권2호
  • /
  • Pages.201-214
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  • 2020
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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

Performance evaluation of composite moment-frame structures with seismic damage mitigation systems using wavelet analyses

  • Kaloop, Mosbeh R. (Department of Civil and Environmental Engineering, Incheon National University) ;
  • Son, Hong Min (Department of Civil and Environmental Engineering, Incheon National University) ;
  • Sim, Hyoung-Bo (Department of Civil and Environmental Engineering, Incheon National University) ;
  • Kim, Dongwook (Department of Civil and Environmental Engineering, Incheon National University) ;
  • Hu, Jong Wan (Department of Civil and Environmental Engineering, Incheon National University)
  • 투고 : 2019.03.28
  • 심사 : 2019.11.21
  • 발행 : 2020.04.25
⟨ 이전 논문 다음 논문 ⟩

초록

This study aims at evaluating composite moment frame structures (CFS) using wavelet analysis of the displacement behavior of these structures. Five seismic damage mitigation systems' models of 9-story CFS are examined namely, basic (Model 1), reinforced (Model 2), buckling restrained braced (BRB) (Model 3), lead rubber bearing (LRB) (Model 4), and composite (Model 5) moment frames. A novel integration between continuous and discrete wavelet transforms is designed to estimate the wavelet power energy and variance of measurements' behaviors. The behaviors of the designed models are evaluated under influence of four seismic loads to study the dynamic performance of CFS in the frequency domain. The results show the behaviors of models 3 and 5 are lower than other models in terms of displacement and frequency performances. Model 3 has been shown lower performances in terms of energy and variance wavelets along the monitoring time; therefore, Model 3 demonstrates superior performance and low probability of failure under seismic loads. Furthermore, the wavelet variance analysis is shown a powerful tool that can be used to assess the CFS under seismic hazards.

키워드

과제정보

연구 과제 주관 기관 : Incheon National University

This work was supported by the Incheon National University Institute of Convergence Science & Technology Research Grant in 2018.

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