Volume 12 , Issue 2 , PP: 34–46, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Sonia Ahmed 1 * , Marek Salamak 2
Doi: https://doi.org/10.54216/IJBES.120203
Accurate object semantics are essential for building information modeling (BIM) workflows to enable interoperability, model checking, quantity take-off, performance analysis, and other downstream engineering applications. However, in practice, Industry Foundation Classes (IFC)-based model exchanges often feature limited or poorly identified semantic tags, particularly during interoperability with authoring and reviewing tools. This research proposes a re-producible, geometry-based learning algorithm for the automatic recognition of BIM element categories based on publicly available IFC-based property data. The empirical analysis is based on 780 object instances from ten BIM categories from a publicly available sample of IFC object records. A rule based parser translates semi-structured BIM text exports into engineering features as bounding box dimensions, coordinates, elevations and object-status. The study compares three supervised machine-learning baselines via stratified five-fold cross-validation: logistic regression, random forest and extra trees. Random forest performed best overall with an accuracy of 0.992, balanced accuracy of 0.971, a weighted F1-score of 0.992, and a macro F1-score of 0.970. The analysis of feature importance shows that bounding-box height, width, length, spatial coordinates and externality related descriptors are the most important features. The results demon-strate significant semantics can be extracted from minimal engineering descriptors without the need for deep learning of meshes. This work provides an interpretable and efficient baseline for BIM enrichment, assessment, and interoperability-focused preprocessing for engineering science use-cases.
BIM , IFC , Semantic enrichment , Engineering informatics , Building data analytics , Machine learning
Emunds, C., Pauen, N., Richter, V., Frisch, J., & van Treeck, C. (2022). SpaRSE-BIM: Classification of IFC-based geometry via sparse convolutional neural networks. Advanced Engineering Informatics, 53, Article 101641. https://doi.org/10.1016/j.aei.2022.101641
Hu, D., Gan, V. J. L., Zhai, R., Wang, Y., & He, Y. (2024). Automated BIM-toscan point cloud semantic segmentation using a domain adaptation network with hybrid attention and whitening (DawNet). Automation in Construction, 164, Article 105473. https://doi.org/10.1016/j.autcon.2024.105473
Jiang, S., Feng, X., Zhang, B., & Shi, J. (2023). Semantic enrichment for BIM: Enabling technologies and applications. Advanced Engineering Informatics, 56, Article 101961. https://doi.org/10.1016/j.aei.2023.101961
Koo, B., Jung, R., & Yu, Y. (2021). Automatic classification of wall and door BIM element subtypes using 3D geometric deep neural networks. Advanced Engineering Informatics, 47, Article 101200. https://doi.org/10.1016/j.aei.2020.101200
Liu, H., Gan, V. J. L., Cheng, J. C. P., & Zhou, S. A. (2024). Automatic fine-grained BIM element classification using multi-modal deep learning (MMDL). Advanced Engineering Informatics, 61, Article 102458. https://doi.org/10.1016/j.aei.2024.102458
Mirarchi, C., Gholamzadehmir, M., Daniotti, B., & Pavan, A. (2024). Semantic enrichment of BIM: The role of machine learning-based image recognition. Buildings, 14 (4), Article 1122. https://doi.org/10.3390/buildings14041122
Parece, S., Resende, R., & Rato, V. (2024). A BIM-based tool for embodied carbon assessment using a construction classification system. Developments in the Built Environment, 19, Article 100467. https://doi.org/10.1016/j.dibe.2024.100467
Peng, Y., Au-Yong, C. P., & Myeda, N. E. (2024). Knowledge graph of building information modelling (BIM) for facilities management (FM). Automation in Construction, 165, Article 105492. https://doi.org/10.1016/j.autcon.2024.105492
Rogage, K., & Doukari, O. (2024). 3D object recognition using deep learning for automatically generating semantic BIM data. Automation in Construction, 162, Article 105366. https://doi.org/10.1016/j.autcon.2024.105366
Sacks, R., Wang, Z., Ouyang, B., Utkucu, D., & Chen, S. (2022). Toward artificially intelligent International Journal of BIM and Engineering Science (IJBES) Vol. 12, No. 02, PP. 34–46, 2026 cloud-based building information modeling for collaborative multidisciplinary design. Advanced Engineering Informatics, 53, Article 101711. https://doi.org/10.1016/j.aei.2022.101711
Utkucu, D., Ying, H., Wang, Z., & Sacks, R. (2024). Classification of architectural and MEP BIM objects for building performance evaluation. Advanced Engineering Informatics, 61, Article 102503. https://doi.org/10.1016/j.aei.2024.102503
Wang, Z., Sacks, R., & Yeung, T. (2022). Exploring graph neural networks for semantic enrichment: Room type classification. Automation in Construction, 134, Article 104039. https://doi.org/10.1016/j.autcon.2021.104039
Xue, F., Wu, L., & Lu, W. (2021). Semantic enrichment of building and city information models: A ten-year review. Advanced Engineering Informatics, 47, Article 101245. https://doi.org/10.1016/j.aei.2020.101245
Zhai, R., Zou, J., He, Y., & Meng, L. (2022). BIM-driven data augmentation method for semantic segmentation in superpoint-based deep learning network. Automation in Construction, 140, Article 104373. https://doi.org/10.1016/j.autcon.2022.104373
