Journal of Information Technology in Construction
Journal of Information Technology in Construction
ITcon Vol. 31, pg. 561-583, http://www.itcon.org/2026/25
Machine learning-based decision support framework for BIM component specification
| DOI: | 10.36680/j.itcon.2026.025 | |
| submitted: | December 2025 | |
| published: | May 2026 | |
| editor(s): | Kumar B | |
| authors: | İpek Geç, Architect
Architectural Design Computing Program, Graduate School, Istanbul Technical University, Istanbul, Türkiye & ENKA Construction, ENKA Design Center, Istanbul, Türkiye https://orcid.org/0009-0004-2683-0886 gec@itu.edu.tr Orkan Zeynel Güzelci, PhD Department of Interior Architecture, Faculty of Architecture, Istanbul Technical University, Istanbul, Türkiye & Faculty of Architecture + DFL/CEAU, University of Porto, Porto, Portugal https://orcid.org/0000-0002-5771-4069 guzelci@itu.edu.tr & oguzelci@arq.up.pt | |
| summary: | Building Information Modeling (BIM) environments contain structured, data-rich models, yet the design logic embedded within them is rarely reused beyond individual projects. Although BIM systems capture relationships between spatial configurations and building components, this knowledge typically remains project-specific and is not systematically utilized in future design processes. This study introduces a machine learning (ML)-based computational framework that predicts door specification attributes from room-to-room spatial transitions, aiming to support data-informed decision-making in design. In this context, BIM is approached not only as a modeling environment but also as a source of transferable design knowledge. A dataset of 5,763 door instances was compiled from three Revit-based projects representing different building typologies: a hospital, an office, and a recreational facility. Spatial transitions were encoded using a Bag-of-Words (BoW) representation, and Random Forest algorithms were applied for classification and regression. Four scenarios were tested: (i) intra-project learning, (ii) cross-project learning, (iii) combined project learning, and (iv) domain-augmented retraining. To provide a benchmark, model performance was evaluated against simple baseline strategies, including majority-class prediction for classification and mean-value prediction for regression. Results showed strong performance for some targets within single projects, while others exhibited more moderate results. Performance decreased across projects due to inconsistent naming, class imbalance, and label mismatch, where the model encounters previously unseen categories. The findings demonstrate that archived BIM models can be transformed into predictive design intelligence, enhancing computational reasoning and efficiency within BIM-based workflows, while emphasizing the need for data standardization to achieve robust cross-project applicability. | |
| keywords: | Building Information Modeling, data-driven design, decision support systems, machine learning, spatial semantics | |
| full text: | (PDF file, 1.573 MB) | |
| citation: | Geç, İ., & Güzelci, O. Z. (2026). Machine learning-based decision support framework for BIM component specification. Journal of Information Technology in Construction (ITcon), 31, 561-583. https://doi.org/10.36680/j.itcon.2026.025 | |
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