aspg-22-4327-1791483633 20261008182033 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group International Journal of BIM and Engineering Science IJBES 2571-1075 2026 12 2 Sonia Ahmed Management in Construction Dept., CTU, Czech Republic Marek Salamak Civil Engineering Dept., Silesian University of Technology, Gliwice, Poland 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. 2026 19 24 4327 https://creativecommons.org/licenses/by/4.0/ 10.54216/IJBES.120203 https://www.americaspg.com/journal/22/article/4327 https://www.americaspg.com/storage/articles/manuscripts/01KT6NQ8ZHSCRV32GSM5AR2WHB.pdf