• 한국구조물진단유지관리공학회 논문집
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• 제19권1호
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• pp.81-89
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• 2015

본 연구에서는 철근콘크리트구조에서 전단벽체를 대상으로 설계단계에서부터 Set-Based Design과 계층화분석기법을 통하여 선정된 최적배근그룹을 적용한 S-BIM기반 자동철근배근시스템 제안을 목적으로 한다. 복합적인 기준에 대한 쌍대비교 분석을 통해 최적 철근배근그룹을 선정하고 BIM 소프트웨어 벤더 (Vendor)에서 제공하는 Open API를 이용하여 구조설계 알고리즘을 통한 부재의 생성 및 철근배근이 자동으로 수행될 수 있도록 하였다. 결과적으로 가중치가 가장 높은 ALT1 (0.142)의 최적철근배근그룹이 선정되었고, 이를 TS상에 생성하였다.

• 대한건축학회논문집:계획계
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• 제36권5호
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• pp.93-103
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• 2020

The purpose of this study is to suggest a supervisory way to improve the efficiency of Building Supervision using Deep Learning, especially object detecting technology. Since the establishment of the Building Supervision system in Korea, it has been changed and improved many times systematically, but it is hard to find any improvement in terms of implementing methods. Therefore, the Supervision is until now the area where a lot of money, time and manpower are needed. This might give a room for superficial, formal and documentary supervision that could lead to faulty construction. This study suggests a way of Building Supervision which is more automatic and effective so that it can lead to save the time, effort and money. And the way is to detect the hoop-bars of a column and count the number of it automatically. For this study, we made a hoop-bar detecting network by transfor learnning of YOLOv2 network through MATLAB. Among many training experiments, relatively most accurate network was selected, and this network was able to detect rebar placement in building site pictures with the accuracy of 92.85% for similar images to those used in trainings, and 90% or more for new images at specific distance. It was also able to count the number of hoop-bars. The result showed the possibility of automatic Building Supervision and its efficiency improvement.