ScholarWorks@성균관대학교

초록

Building information modeling (BIM) enhances communication in the architecture, engineering, and construction industry and automates drawing generation. However, optimizing the placement of drawing elements remains a challenge. This paper proposes a framework using proximal policy optimization to improve BIM drawing element placement, focusing on floor plan-type drawings in the construction documentation phase. Deep reinforcement learning ensures stable performance in high-dimensional, sparse-data environments. Experiments on a dataset of 150 drawings showed that the interference ratio among drawing elements converged to zero within 0.05 s to 5 min, improving readability. Compared with conventional BIM processes, the proposed framework reduced “element position adjustment” time and commands by 93.9 % and 94.3 %, respectively, leading to an overall reduction of 25 % in work time and 17.9 % in commands. These results validate the framework's effectiveness in improving productivity and reducing manual effort. It enhances readability, minimizes human errors, and allows designers to focus on essential tasks. © 2025 Elsevier B.V.

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