| Title |
Modeling Element Relations as Structured Graphs Via Neural Structured Learning to Improve BIM Element Classification |
| Authors |
유영수(Yu, Youngsu) ; 이고은(Lee, Koeun) ; 구본상(Koo, Bonsang) ; 이관훈(Lee, Kwanhoon) |
| DOI |
https://doi.org/10.12652/Ksce.2021.41.3.0277 |
| Keywords |
BIM; IFC; 시멘틱 무결성; 인공신경망; N니 BIM; IFC; Semantic integrity; ANN; N니 |
| Abstract |
Building information modeling (BIM) element to industry foundation classes (IFC) entity mappings need to be checked to ensure the semantic integrity of BIM models. Existing studies have demonstrated that machine learning algorithms trained on geometric features are able to classify BIM elements, thereby enabling the checking of these mappings. However, reliance on geometry is limited, especially for elements with similar geometric features. This study investigated the employment of relational data between elements, with the assumption that such additions provide higher classification performance. Neural structured learning, a novel approach for combining structured graph data as features to machine learning input, was used to realize the experiment. Results demonstrated that a significant improvement was attained when trained and tested on eight BIM element types with their relational semantics explicitly
represented. |