• Journal of the Korea institute for structural maintenance and inspection
• /
• v.19 no.1
• /
• pp.81-89
• /
• 2015

This study takes shear wall of reinforced concrete structure as study object, and the purpose of this study is to suggest structure BIM based on automatic reinforcing bar placement system applying set-based design through the most optimum reinforcing bar placement group that was selected by applying AHP (analytical hierarchy process) method from design step. For this, the most optimum reinforcing bar placement group was selected by pairwise comparison analysis on complex standard of multiple alternatives. And shear wall automatic reinforcing bar placement system has been developed, which can automatically generate members and arrange reinforcing bar by structure design algorithm and using open API (application programming interface) provided by a BIM software vendor. As a result, the most optimum reinforcing bar placement group of the highest weight, ALT1, was selected and was generated using Tekla Structure program.

• Journal of the Architectural Institute of Korea Planning & Design
• /
• v.36 no.5
• /
• pp.93-103
• /
• 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.