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A Study on Automated Reinforcement Detailing for Reinforced Concrete Structures Using BIM

BIM 기반 철근콘크리트 구조물의 자동 배근 모델 생성

  • Park, U-Yeol (Department of Architectural Engineering, Andong National University) ;
  • Yun, Seok-Heon (Department of Architectural Engineering, GyeongSang National University)
  • Received : 2024.06.25
  • Accepted : 2024.07.16
  • Published : 2024.08.20

Abstract

Recent advancements in Building Information Modeling(BIM) have significantly impacted the construction industry, driving competitiveness and innovation. However, rebar construction, a critical component influencing project quality and cost, has lagged behind in BIM adoption. Traditional methods relying heavily on 2D drawings for rebar detailing have hindered efficiency and introduced potential errors. This paper presents a novel system designed to automate the detailed modeling of rebar, thereby promoting BIM integration within rebar construction and optimizing construction management processes. The system leverages confirmed structural drawings from the post-structural design phase to automatically generate intricate rebar models for columns and beams. To ensure adherence to domestic structural design standards, the system is developed using C# programming language and the Revit API. By automating rebar modeling, this system aims to minimize human error, reduce labor-intensive tasks, and enhance overall rebar construction efficiency through the effective utilization of generated rebar model data.

최근 건설산업의 경쟁력을 강화하고 스마트화를 추진하기 위한 수단으로서 BIM의 도입이 확대되고 있다. 그러나 품질 및 원가측면에서 중요한 위치를 차지하고 있는 철근공사는 주로 2D 도면 위주로 철근상세도를 작성하는 등 비효율적인 방식으로 공사관리가 이루어지고 있고 BIM의 활용도도 낮은 편이다. 이에 본 논문은 철근공사의 BIM 도입을 촉진하고 공사관리를 효율화하기 위하여 철근 상세 모델을 자동화하는 시스템을 구현하였다. 현실적인 요인을 감안하여 구조설계도면이 완성된 이후의 단계에서 확정된 구조도면의 정보를 바탕으로 기둥과 보 부재의 철근 상세 모델을 자동으로 생성한다. 실무에서의 활용도를 높이기 위하여 Revit API 개발 도구을 기반으로 C# 프로그래밍 언어를 사용하여 국내 구조설계기준에 적합한 철근 상세를 자동으로 생성할 수 있도록 구성하였다. 본 시스템을 활용하면 수작업 오류를 방지할 수 있고 작업시간 또한 상대적으로 단축할 수 있으며, 생성된 철근 모델 정보를 활용하여 철근공사의 효율성을 높일 수 있을 것으로 판단된다.

Keywords

Acknowledgement

This work is supported in 2024 by the Korea Agency for Infrastructure Technology Advancement(KAIA) grant funded by the Ministry of Land, Infrastructure and Transport(Grant RS-2021-KA163269). This paper was also supported by the Shinsung A&E Research Grant, 2023.

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