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Compressive and Shear Performance Evaluation of DfMA Modular Systems for Progressive Collapse Prevention and Construction Optimization

연속붕괴 방지와 시공 최적화를 위한 DfMA 모듈러시스템의 압축 및 전단성능 연구

  • Shin, Yun-Seong (Deep Learning Architecture Research Center, Department of Architectural Engineering, Sejong University) ;
  • Lee, Dong-Kyu (Deep Learning Architecture Research Center, Department of Architectural Engineering, Sejong University) ;
  • Kim, Yong-Nam (MIRAE Structural Engineering Co.) ;
  • Jeong, Moon-Sook (Samsung C&T Corp.) ;
  • Lee, Kihak (Deep Learning Architecture Research Center, Department of Architectural Engineering, Sejong University)
  • 신윤성 (세종대학교 딥러닝건축연구소, 건축공학과) ;
  • 이동규 (세종대학교 딥러닝건축연구소, 건축공학과) ;
  • 김용남 ((주)미래구조엔지니어링) ;
  • 정문숙 ((주)삼성물산 건설부문 모듈러팀) ;
  • 이기학 (세종대학교 딥러닝건축연구소, 건축공학과)
  • Received : 2024.03.13
  • Accepted : 2024.06.20
  • Published : 2024.07.30

Abstract

This paper proposes a novel modular steel assembly method for manufacturing and assembly design (DfMA) using innovative connectors produced by casting with 3D printed sand molds and rectangular steel cross-section (RHS) members. The demand for modular construction methods in high-rise buildings is growing due to their economic and sustainability advantages in reducing construction time. With the increasing demand for modular construction techniques in high-rise buildings, rational structural design of modular joints is crucial for safety against progressive collapse and practicality. This paper presents the results of experiments on tension, compression, and shear of modular nodes at each location, and compare and validate the performance of modular nodes through FEM analysis. This paper describes the structural performance validation, development of finite element models, and experimental methods and results for new modular joints for parametric investigation, demonstrating the reliability of finite element analysis models.

Keywords

Acknowledgement

이 연구는 (주)삼성물산 건설부문 모듈러팀의 모듈러 접합부 연구와 행정안전부 국립재난안전연구원 재난안전 공동연구 기술개발사업의 지원을 받아 구행된 연구임. 과제번호:2022-MOIS63-003(RS-2022-ND641021)

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