대한건축학회논문집 (Journal of the Architectural Institute of Korea)

  • 제39권8호
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  • Pages.215-222
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  • 2023
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  • 2733-6239(pISSN)
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  • 2733-6247(eISSN)
대한건축학회 (Architectural Institute of Korea)
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노후 철근콘크리트 건물 기둥의 파괴 모드 분류와 변형 능력 예측식 제안

Classifying Failure Modes and Proposing Deformation Capacity Prediction Equations for Lightly Reinforced Concrete Columns in Aging Buildings

  • Koo, Su-hyun (Dept. of Architectural Engineering, Hanyang University) ;
  • Han, Sang Whan (Dept. of Architectural Engineering, Hanyang University)
  • 투고 : 2023.04.06
  • 심사 : 2023.08.08
  • 발행 : 2023.08.30
⟨ 이전 논문 다음 논문 ⟩

초록

This study addresses the seismic performance of existing reinforced concrete (RC) buildings, which were primarily designed for gravity loads alone. Assessing the buildings' seismic capability requires understanding the deformation capacity of the lightly reinforced concrete columns within them. The study's objective is twofold: to establish a procedure for categorizing column failure modes, and to present equations for predicting deformation capacities based on these failure modes. To achieve this, data from 189 lightly reinforced concrete columns across 53 different studies were collected. The effectiveness of the newly proposed failure classification procedure was validated by comparing its outcomes to the observed failure modes of the columns. Significantly improved accuracy was demonstrated compared to existing classification methods. The study further introduced empirical equations for forecasting column deformation capacity through regression analysis. These equations were validated by comparing calculated deformation capacities to actual measurements from column specimens. The empirical equations proposed in this study showed markedly higher coefficients of determination compared to existing equations proposed by Zhu, Elwood, & Haukaas (2007) and ASCE 41 (2017). This demonstrates that the suggested empirical equations can effectively forecast both the failure mode and deformation capacity of the lightly reinforced concrete columns.

키워드

과제정보

이 연구는 한국연구재단 연구비 지원에 의한 결과의 일부임 (과제번호:2020R1A2C2010548)

참고문헌

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