| description abstract | This study introduces a framework for transplanting a building information modeling (BIM) library. Design detailing constitutes 50%–60% of the total design time, even within the BIM context. Previous studies have highlighted the potential of integrating BIM and artificial intelligence (AI) for enhanced productivity. However, challenges arise due to architects’ preferences for unique project-specific details when applying generalized AI approaches based on big data. To address this, we propose a BIM library transplant framework. This framework automatically identifies objects at a high level of development (LOD) from a selected existing BIM model (i.e., a donor model) and matches them with low-LOD objects in a new model (i.e., a recipient model). Subsequently, it replaces the low-LOD objects with corresponding high-LOD objects. The framework involves three steps: (1) extracting the library from the donor model, (2) matching the library, and (3) transplanting the library from the donor to recipient model. To validate its efficacy, we implemented the BIM library transplant framework as a Revit add-on, employing the random forest classifier as the object-matching AI model. Our results indicate that the implemented framework has the potential to reduce detailing time by approximately 60%–70%, while achieving an accuracy of 65%–80%. | |
