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Verification of Reinforcing Arrangement Error in Precast Concrete Shear Walls Using BIM and Presentation of Flexural Ductility Model

BIM을 이용한 프리캐스트 콘크리트 전단벽의 배근 오류 검증 및 휨 연성 모델 제시

  • Ju-Hyun Mun (Department of Architectural Engineering, Kyonggi University)
  • 문주현 (경기대학교 스마트시티공학부 건축공학전공)
  • Received : 2024.04.29
  • Accepted : 2024.05.10
  • Published : 2024.06.30

Abstract

This study established a BIM procedure considering manufacturing errors in the production process, and evaluated the flexural ductility of precast all-lightweight aggregate concrete special shear walls (PLASWs) with spliced sleeve technique. In the production process, the concrete cover thickness of PALSW was on average 1.28 times greater than the cross-sectional details of the specimen modeled with Revit BIM program. In particular, the bending inner radius of the hoop and inner-cross tie were greater than the designed details. Consequently, the confinement effect of core concrete reduced from 64% to 54% due to the manufacturing errors in the transverse reinforcing bars, resulting in a decrease in the ductility of PALSW by approximately 4.91%. Considering these findings, the BIM of PLASW with spliced sleeve technique should compliment the bending inner radius of the transverse reinforcing bars, and the defined brittleness increase coefficient reflecting the decreased core concrete confining pressure in the stress-strain relationship of confined concrete should be evaluated as 1.8.

이 연구의 목적은 생산공정에서 발생할 수 있는 제작오차를 고려한 BIM 절차를 구축하고, 스플라이스 슬리브 공법으로 접합된 프리캐스트 전경량 골재콘크리트 특수전단벽(precast all-lightweight aggregate concrete special shear walls, PLASW)의 휨 연성 모델을 제시하는 데에 있다. 생산현장에서 제작된 PALSW의 콘크리트 피복 두께는 Revit BIM 프로그램으로 모델링된 단면상세보다 평균 1.28배 컸으며, 특히, 후프철근과 내부 크로스타이의 구부림 내면 반지름은 설계 단면상세보다 더 크게 있었다. 결과적으로 띠철근의 제작오차로 인해 코어 콘크리트의 구속비율이 64%에서 54%로 감소하였으며, PALSW의 휨 연성은 약 4.91% 감소하였다. 이 실험결과를 고려하여, 스플라이스 슬리브 공법으로 접합된 PLASW의 BIM 모델링은 띠철근의 구부림 내면 반지름을 보완해야 하며, 구속된 콘크리트의 응력-변형률 관계에서 구속압의 감소를 반영하여 취성도 증가계수는 1.8로 평가될 수 있다.

Keywords

Acknowledgement

이 논문은 2022년도 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구(No. 2022R1A2B5B03002476)의 연구비 지원으로 수행되었으며, 이에 감사드립니다.

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