• Korean Architects
• /
• no.9 s.127
• /
• pp.55-66
• /
• 1979

• Korean Architects
• /
• no.3 s.53
• /
• pp.34-39
• /
• 1973

• Korean Architects
• /
• no.11 s.152
• /
• pp.20-22
• /
• 1981

• Korean Journal of Heritage: History & Science
• /
• v.42 no.2
• /
• pp.170-183
• /
• 2009

• Journal of KIBIM
• /
• v.11 no.4
• /
• pp.53-62
• /
• 2021

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.

• Korean Architects
• /
• no.7 s.67
• /
• pp.51-61
• /
• 1974

• Construction Engineering and Management
• /
• v.24 no.3
• /
• pp.49-54
• /
• 2023

• Korean Architects
• /
• no.1 s.29
• /
• pp.28-50
• /
• 1971

• Korean Architects
• /
• no.9 s.80
• /
• pp.62-70
• /
• 1975

• Korean Architects
• /
• no.5 s.55
• /
• pp.11-27
• /
• 1973