Journal of KIBIM (한국BIM학회 논문집)

Korean Institute of Building Information Modeling (한국BIM학회)
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BIM Model Generation at Building Level using Automated Scan-to-BIM Process - Focused on Demonstration of BIM Modeling for Gangwon Fire Service Academy -

Scan-to-BIM 자동화 기술을 활용한 건축물 단위의 BIM 모델 생성 - 강원소방학교 BIM 모델링 실증을 중심으로 -

  • 박준우 (딥러닝 건축연구소, 세종대학교 건축공학과) ;
  • 김재홍 (세종대학교 건축공학과) ;
  • 김소현 (딥러닝 건축연구소, 세종대학교 건축공학과) ;
  • 이지민 (딥러닝 건축연구소, 세종대학교 건축공학과) ;
  • 최창순 (딥러닝 건축연구소, 세종대학교 건축공학과) ;
  • 정광복 (딥러닝 건축연구소, 세종대학교 건축공학과) ;
  • 이재욱 (딥러닝 건축연구소, 세종대학교 건축공학과)
  • Received : 2021.12.17
  • Accepted : 2021.12.15
  • Published : 2021.12.31
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Abstract

The successful implementation of Scan-to-BIM automation depends on the entire process from scanning of buildings, including indoor facilities and furniture, to generating BIM models. However, the conventional Scan-to-BIM process requires a lot of time, manpower, and cost for the manual generation of BIM models including indoor objects. To solve this problem, this study applied a Scan-to-BIM automation process using a deep learning model and parametric algorithm to an existing building, Kangwon Fire Service Academy. To improve the accuracy of the BIM model, after object data was extracted from the scan data, the data was corrected according to actual object-specific conditions. As a result, the accuracy of the BIM model created by the proposed Scan-to-BIM automation process was 91% compared to the actual area of the construction drawings. In addition, it was confirmed that the BIM objects were automatically generated for 10 object classes.

Keywords

Acknowledgement

본 연구는 국토교통부/국토교통과학기술진흥원(과제번호 21AATD-C163269-01)과 과학기술정보통신부/한국연구재단(NRF-2020R1A2C1010421)의 지원으로 수행되었음.

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