Integrating a Machine Learning-based Space Classification Model with an Automated Interior Finishing System in BIM Models

The need for adopting automation technologies to improve inefficiencies in interior finishing modeling work is increasing during the Building Information Modeling (BIM) design stage. As a result, the use of visual programming languages (VPL) for practical applications is growing. However, undefined or incorrect space designations in BIM models can hinder the development of automated finishing modeling processes, resulting in erroneous corrections and rework. To address this challenge, this study first developed a rule-based automated interior finishing detailing module for floors, walls, and ceilings. In addition, an automated space integrity checking module with 86.69% ACC using the Multi-Layer Perceptron (MLP) model was developed. These modules were integrated into a design automation module for interior finishing, which was then verified for practical utility. The results showed that the automation module reduced the time required for modeling and integrity checking by 97.6% compared to manual work, confirming its utility in assisting BIM model development for interior finishing works.