기계학습 기반 노후 철근콘크리트 건축물의 축력허용범위 산정 방법

ML-based Allowable Axial Loading Estimation of Existing RC Building Structures
  • 황희진
  • 오근영
  • 강재도
  • 신지욱
Citations

SCOPUS

0

초록

Due to seismically deficient details, existing reinforced concrete structures have low lateral resistance capacities. Since these building structures suffer an increase in axial loads to the main structural element due to the green retrofit (e.g., energy equipment/device, roof garden) for CO2 reduction and vertical extension, building capacities are reduced. This paper proposes a machine-learning-based methodology for allowable ranges of axial loading ratio to reinforced concrete columns using simple structural details. The methodology consists of a two-step procedure: (1) a machine-learning-based failure detection model and (2) column damage limits proposed by previous researchers. To demonstrate this proposed method, the existing building structure built in the 1990s was selected, and the allowable range for the target structure was computed for exterior and interior columns.

키워드

Existing buildingsReinforced concrete frame buildingGreen retrofitVertical extensionAllowable axial load range
제목
기계학습 기반 노후 철근콘크리트 건축물의 축력허용범위 산정 방법
제목 (타언어)
ML-based Allowable Axial Loading Estimation of Existing RC Building Structures
저자
황희진오근영강재도신지욱
DOI
10.5000/EESK.2024.28.5.257
발행일
2024-09
저널명
한국지진공학회논문집
28
5
페이지
257 ~ 266