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International Journal of Intelligent Data and Machine Learning

Open Access Peer Review International
Open Access

Assessing The Interoperability And Semantic Readiness Of BIM And IFC Data For AI Integration In The Architecture, Engineering, And Construction Industry: A Systematic Review

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Bima Satria Nugraha 1 , Professor Anindya larasati 1 , Dr. Huỳnh Chí Dũng 1 ,
4 Faculty of Civil and Environmental Engineering, Bandung Institute of Technology, Bandung, Indonesia
4 Department of Architecture and Design, National University of Singapore, Singapore
4 College of Construction Engineering, Hanoi University of Civil Engineering, Hanoi, Vietnam

Abstract

Purpose: This systematic review aims to critically assess the current state of Building Information Modeling (BIM) and Industry Foundation Classes (IFC) data interoperability and semantic readiness for scalable integration with Artificial Intelligence (AI) applications across the Architecture, Engineering, and Construction (AEC) industry.

Design/Methodology/Approach: A Systematic Literature Review (SLR) was conducted, adhering to PRISMA guidelines, analyzing key research focused on the intersection of BIM, IFC, and AI. A conceptual framework categorizing AI-ready data into five pillars—Structural Consistency, Semantic Completeness, Geometric Fidelity, Temporal Coherence, and Contextual Richness—was developed to synthesize findings.

Findings: While AI applications, notably in predictive maintenance, risk assessment, and generative design, exhibit clear reliance on BIM/IFC data, the implementation is often impeded by significant data quality challenges. The core issue lies in the semantic gap: IFC, designed primarily for data exchange, frequently lacks the explicit, complete, and consistently structured information required for machine learning algorithms. Current approaches heavily rely on labor-intensive pre-processing, graph-based data transformations, or domain-specific custom property sets, compromising true interoperability. Furthermore, the handling of geometric and topological data within IFC frequently suffers from inaccuracies that render it unsuitable for highly sensitive computational tasks like automated quantity take-off and robot navigation.

Originality/Value: This review introduces a novel framework for assessing AI-ready BIM data and systematically maps the specific data requirements of various AI applications to the current limitations of the IFC schema. It provides a foundational critique, guiding future research toward developing the necessary semantic middleware, robust geometric validation tools, and standardization efforts for achieving seamless BIM-AI integration.

Keywords

Building Information Modeling, Industry Foundation Classes, Artificial Intelligence, Semantic Interoperability, Construction Technology, Data Readiness, Knowledge Graph

References

De Souza, M.P.; Fialho, B.C.; Ferreira, R.C.; Fabricio, M.M.; Codinhoto, R. Modelling and Coordination of Building Design: An Experience of BIM Learning/Upskilling. Archit. Eng. Des. Manag. 2023, 19, 74–91.
NAM AI in Manufacturing. Available online: https://nam.org/issues/research-innovation-and- technology/ai/ (accessed on 5 December 2023).
Pan, Y.; Zhang, L. Integrating BIM and AI for Smart Construction Management: Current Status and Future Directions. Arch. Comput. Methods Eng. 2023, 30, 1081–1110.
Allen, G. Understanding AI Technology; Joint Artificial Intelligence Center (JAIC) The Pentagon United States: Arlington, VA, USA,2020.
Tomczak, A.; Berlo, L.V.; Krijnen, T.; Borrmann, A.; Bolpagni, M. A Review of Methods to Specify Information Requirements in Digital Construction Projects. IOP Conf. Ser. Earth Environ. Sci. 2021, 1101, 092024.
Bloch, T. Connecting Research on Semantic Enrichment of BIM-Review of Approaches, Methods and Possible Applications. J. Inf.Technol. Constr. 2022, 27, 416–440.
Noardo, F.; Harrie, L.; Arroyo Ohori, K.; Biljecki, F.; Ellul, C.; Krijnen, T.; Eriksson, H.; Guler, D.; Hintz, D.; Jadidi, M.A.; et al.Tools for BIM-GIS Integration (IFC Georeferencing and Conversions): Results from the GeoBIM Benchmark 2019. ISPRS Int. J.Geo-Inf. 2020, 9, 502.
McCarthy, J.; Minsky, M.L.; Rochester, N.; Shannon, C.E. A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence, August 31, 1955. AI Mag. 2006, 27, 12.
Turing, A.M. Computing Machinery and Intelligence; Springer: Berlin/Heidelberg, Germany, 2009.
McCulloch, W.S.; Pitts, W. A Logical Calculus of the Ideas Immanent in Nervous Activity. Bull. Math. Biophys. 1943, 5, 115–133.
Jackson, P. Introduction to Expert Systems; Addison- Wesley Longman Publishing Co., Inc.: Petaluma, CA, USA, 1990.
Waterman, D.A. A Guide to Expert Systems; Addison-Wesley Longman Publishing Co., Inc.: Petaluma, CA, USA, 1985.
Yoon, Y.; Guimaraes, T.; O’Neal, Q. Exploring the Factors Associated with Expert Systems Success. MIS Q. 1995, 19, 83–106.
Sagiroglu, S.; Sinanc, D. Big Data: A Review. In Proceedings of the 2013 International Conference on Collaboration Technologies and Systems (CTS), San Diego, CA, USA, 20–24 May 2013; IEEE:
Piscataway, NJ, USA, 2013; pp. 42–47.
Parate, H., Kishore Bandela, & Paniteja Madala. (2025). Quantity Take-Off Strategies: Reducing Errors in Roadway Construction Estimation. Journal of Mechanical, Civil and Industrial Engineering, 6(3), 01-09.
https://doi.org/10.32996/jmcie.2025.6.3.1
Jordan, M.I.; Mitchell, T.M. Machine Learning: Trends, Perspectives, and Prospects. Science 2015, 349, 255–260.
LeCun, Y.; Bengio, Y.; Hinton, G. Deep Learning. Nature 2015, 521, 436–444.
Chang, H.S.; Fu, M.C.; Hu, J.; Marcus, S.I. Google DeepMind’s AlphaGo: Operations Research’s Unheralded Role in the Path-Breaking Achievement. OrMs Today 2016, 43, 24–30.
Deng, J.; Dong, W.; Socher, R.; Li, L.-J.; Li, K.; Li,
F. Imagenet: A Large-Scale Hierarchical Image Database. In Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA, 20–25 June 2009; IEEE: Piscataway, NJ,USA, 2009; pp. 248–255.
Vaswani, A. Attention Is All You Need. In Advances in Neural Information Processing Systems; MIT Press: Cambridge, MA, USA,2017.
Radford, A. Improving Language Understanding by Generative Pre-Training. 2018. Available online: https://cdn.openai.com/research-covers/language- unsupervised/language_understanding_paper.pdf (accessed on 5 December 2023).
Brown, T.B. Language Models Are Few-Shot Learners. arXiv 2020, arXiv:200514165.
Bubeck, S.; Chandrasekaran, V.; Eldan, R.; Gehrke, J.; Horvitz, E.; Kamar, E.; Lee, P.; Lee, Y.T.; Li, Y.; Lundberg, S.; et al. Sparks of Artificial General Intelligence: Early Experiments with Gpt-4. arXiv 2023, arXiv:230312712.
International Organization for Standardization ISO/IEC 22989:2022 Information Technology— Artificial Intelligence—Artificial Intelligence Concepts and Terminology 2022. Available online: https://www.iso.org/standard/74296.html (accessed on 5 December 2023).
Standards Australia Standards Australia. Available online: https://www.standards.org.au/standards- catalogue/standarddetails?designation=as-iso-iec- 22989-2023 (accessed on 5 December 2023).
El Naqa, I.; Murphy, M.J. What Is Machine Learning? Springer: Berlin/Heidelberg, Germany, 2015.
Gurney, K. An Introduction to Neural Networks; CRC Press: Boca Raton, FL, USA, 2018.
Calder, B. Architecture: From Prehistory to Climate Emergency; Penguin: London, UK, 2021.
García, M.Á. Challenges of the Construction Sector in the Global Economy and the Knowledge Society. Int. J. Strateg. Prop. Manag.2005, 9, 65–77.
Young, D.; Panthi, K.; Noor, O. Challenges Involved in Adopting BIM on the Construction Jobsite. EPiC Ser. Built Environ. 2021, 2,302–310.
Buildings 2024, 14, 3305 50 of 54 Mahmoodzadeh, A.; Nejati, H.R.; Mohammadi, M. Optimized Machine Learning Modelling for Predicting the Construction Costand Duration of Tunnelling Projects. Autom. Constr. 2022, 139, 104305.
Hinze, J.; Appelgate, L.L. Costs of Construction Injuries. J. Constr. Eng. Manag. 1991, 117, 537–550.
You, H.; Ye, Y.; Zhou, T.; Zhu, Q.; Du, J. Robot- Enabled Construction Assembly with Automated Sequence Planning Based on ChatGPT: RoboGPT. Buildings 2023, 13, 1772.
Choi, W.; Na, S.; Heo, S. Integrating Drone Imagery and AI for Improved Construction Site Management through Building Information Modeling. Buildings 2024, 14, 1106.
Based on Deep Learning Network with Improved Particle Swarm Optimization. Artif. Intell. Evol. 2023, 4, 216–225.
Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.;Brennan, S.E.; et al. The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews. BMJ 2021, 372, 71.
Han, J.; Zhou, X.; Zhang, W.; Guo, Q.; Wang, J.; Lu, Y. Directed Representative Graph Modeling of MEP Systems Using BIM Data.Buildings 2022, 12, 834.
Zhao, Y.; Deng, X.; Lai, H. Reconstructing BIM from 2D Structural Drawings for Existing Buildings. Autom. Constr. 2021, 128,103750.
Vinod Kumar Enugala. (2025). "BIM-to-Field" Inspection Workflows for Zero Paper Sites. Utilitas Mathematica, 122(2), 372–404. Retrieved from https://utilitasmathematica.com/index.php/Index/arti cle/view/2711
Rausch, C.; Talebi, S.; Poshdar, M.; Li, B.; Schultz, C. Tolerance Management Domain Model for Semantic Enrichment of BIMs.
Jia, J.; Gao, J.; Wang, W.; Ma, L.; Li, J.; Zhang, Z. An Automatic Generation Method of Finite Element Model Based on BIM and Ontology. Buildings 2022, 12, 1949.
Chen, Z.; Chen, K.; Song, C.; Zhang, X.; Cheng, J.C.P.; Li, D. Global Path Planning Based on BIM and Physics Engine for UGVs in Indoor Environments. Autom. Constr. 2022, 139, 104263.
Hamieh, A.; Ben Makhlouf, A.; Louhichi, B.; Deneux, D. A BIM-Based Method to Plan Indoor Paths. Autom. Constr. 2020, 113,103120.
Feng, Y.; Wang, J.; Fan, H.; Hu, Y. A BIM-Based Coordination Support System for Emergency Response. IEEE Access 2021, 9,68814–68825.

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