Advances in Computational Design

  • 제4권2호
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  • Pages.155-177
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  • 2019
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  • 2383-8477(pISSN)
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  • 2466-0523(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

Symbol recognition using vectorial signature matching for building mechanical drawings

  • Cho, Chi Yon (Department of Civil and Environmental Engineering, Carnegie Mellon University) ;
  • Liu, Xuesong (Department of Civil and Environmental Engineering, Carnegie Mellon University) ;
  • Akinci, Burcu (Department of Civil and Environmental Engineering, Carnegie Mellon University)
  • 투고 : 2018.09.29
  • 심사 : 2019.02.14
  • 발행 : 2019.04.25
⟨ 이전 논문 다음 논문 ⟩

초록

Operation and Maintenance (O&M) phase is the main contributor to the total lifecycle cost of a building. Previous studies have described that Building Information Models (BIM), if available with detailed asset information and their properties, can enable rapid troubleshooting and execution of O&M tasks by providing the required information of the facility. Despite the potential benefits, there is still rarely BIM with Mechanical, Electrical and Plumbing (MEP) assets and properties that are available for O&M. BIM is usually not in possession for existing buildings and generating BIM manually is a time-consuming process. Hence, there is a need for an automated approach that can reconstruct the MEP systems in BIM. Previous studies investigated automatic reconstruction of BIM using architectural drawings, structural drawings, or the combination with photos. But most of the previous studies are limited to reconstruct the architectural and structural components. Note that mechanical components in the building typically require more frequent maintenance than architectural or structural components. However, the building mechanical drawings are relatively more complex due to various type of symbols that are used to represent the mechanical systems. In order to address this challenge, this paper proposed a symbol recognition framework that can automatically recognize the different type of symbols in the building mechanical drawings. This study applied vector-based computer vision techniques to recognize the symbols and their properties (e.g., location, type, etc.) in two vector-based input documents: 2D drawings and the symbol description document. The framework not only enables recognizing and locating the mechanical component of interest for BIM reconstruction purpose but opens the possibility of merging the updated information into the current BIM in the future reducing the time of repeated manual creation of BIM after every renovation project.

키워드

과제정보

연구 과제 주관 기관 : Carnegie Mellon University

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피인용 문헌

  1. A semi-automated method for integrating textural and material data into as-built BIM using TIS vol.5, pp.2, 2019, https://doi.org/10.12989/acd.2020.5.2.127