Automatic Classification of BIM Object Based on IFC Data Using the Uniclass Classification Standard

Abstract

Classification of BIM objects is critical for enhancing information interoperability and standardization within construction projects; however, research on automated BIM object classification based on standardized classification systems remains limited. Therefore, this study proposes an automated method to classify BIM objects using IFC data under the Uniclass system, aiming to enhance standardization, semantic clarity, and practical applicability. The proposed method first assigns Uniclass codes to 8715 BIM objects, then extracts 13 types of IFC-derived feature variables—including semantic, spatial, and dimensional information, and uses 2 categories of Uniclass coding information (EF and Ss tables) as classification labels, each comprising 11 and 17 classes, respectively. A Random Forest model with 100 decision trees and 10-fold cross-validation is then employed to perform automatic classification. Experimental results demonstrate that the proposed method achieves classification accuracies of 1.00 and 0.99 for BIM objects under the Elements/Functions and Systems classification tasks. This study demonstrates that accurate and fine-grained classification of BIM objects can be achieved using only low-LOD IFC data, thereby contributing to standardized information structuring and facilitating intelligent model management during the early design phase.