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Year 2025, Volume: 12 Issue: 3, 706 - 736, 30.09.2025
https://doi.org/10.54287/gujsa.1750405

Abstract

References

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  • Ammar, A., Nassereddine, H., AbdulBaky, N., AbouKansour, A., Tannoury, J., Urban, H., & Schranz, C. (2022). Digital twins in the construction industry: a perspective of practitioners and building authority. Frontiers in Built Environment, 8, 834671. https://doi.org/10.3389/fbuil.2022.834671
  • Aragón, A., Arquier, M., Tokdemir, O. B., Enfedaque, A., Alberti, M. G., Lieval, F., Loscos, E., Pavón, R. M., Novischi, D. M., Legazpi, P. V., & Yagüe, Á. (2025). Seeking a definition of digital twins for construction and infrastructure management. Applied Sciences, 15(3), 1557. https://doi.org/10.3390/app15031557
  • Ba, L., Tangour, F., El Abbassi, I., & Absi, R. (2025). Analysis of digital twin applications in energy efficiency: a systematic review. Sustainability, 17(8), 3560. https://doi.org/10.3390/su17083560
  • Boje, C., Kubicki, S., Guerriero, A., Rezgui, Y., & Zarli, A. (2022). Digital twins for the built environment. In Buildings and Semantics (pp. 179-199). CRC Press. https://doi.org/10.1201/9781003204381-10
  • Caiza, G., & Sanz, R. (2024a). An immersive digital twin applied to a manufacturing execution system for the monitoring and control of industry 4.0 processes. Applied Sciences, 14(10), 4125. https://doi.org/10.3390/app14104125
  • Caiza, G., & Sanz, R. (2024b). Immersive digital twin under ISO 23247 applied to flexible manufacturing processes. Applied Sciences, 14(10), 4204. https://doi.org/10.3390/app14104204
  • Calvetti, D., Mêda, P., Hjelseth, E., & Sujan, S. F. (2023). Digital twin for AECOO – framework proposal and use cases. In: ECPPM 2022 - eWork and eBusiness in Architecture, Engineering and Construction 2022 (pp. 221-228). CRC Press. https://doi.org/10.1201/9781003354222-28
  • D'Amico, R. D., Erkoyuncu, J. A., Addepalli, S., & Penver, S. (2022). Cognitive digital twin: an approach to improve the maintenance management. CIRP Journal of Manufacturing Science and Technology, 38, 613-630. https://doi.org/10.1016/j.cirpj.2022.06.004
  • El Bazi, N., Mabrouki, M., Laayati, O., Ouhabi, N., El Hadraoui, H., Hammouch, F.-E., & Chebak, A. (2023). Generic multi-layered digital-twin-framework-enabled asset lifecycle management for the sustainable mining industry. Sustainability, 15(4), 3470. https://doi.org/10.3390/su15043470
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  • Ghorbani, Z., & Messner, J. (2024). A categorical approach for defining digital twins in the AECO industry. Journal of Information Technology in Construction, 29, 198-218. https://doi.org/10.36680/j.itcon.2024.010
  • Guerra, V., Hamon, B., Bataillou, B., Inamdar, A., & van Driel, W. D. (2024). Towards a digital twin architecture for the lighting industry. Future Generation Computer Systems, 155, 80-95. https://doi.org/10.1016/j.future.2024.01.028
  • Hakiri, A., Gokhale, A., Yahia, S. Ben, & Mellouli, N. (2024). A comprehensive survey on digital twin for future networks and emerging internet of things industry. Computer Networks, 244, 110350. https://doi.org/10.1016/j.comnet.2024.110350
  • Hananto, A. L., Tirta, A., Herawan, S. G., Idris, M., Soudagar, M. E. M., Djamari, D. W., & Veza, I. (2024). Digital twin and 3d digital twin: concepts, applications, and challenges in industry 4.0 for digital twin. Computers, 13(4), 100. https://doi.org/10.3390/computers13040100
  • Huang, H., Ji, T., & Xu, X. (2022). Digital Twin platforms: architectures and functions. Volume 2: Manufacturing Processes; Manufacturing Systems, 85819, V002T06A008. https://doi.org/10.1115/MSEC2022-85085
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  • Lindkvist, C. M., Hafeld, A., & Haugen, T. B. (2022). Interfacing between FM and project phases through digital processes and collaborative practices. IOP Conference Series: Earth and Environmental Science, 1101(6), 062010. https://doi.org/10.1088/1755-1315/1101/6/062010
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A Data-Driven BIM Framework for Digital Twin Integration with ISO 23247-Compliant Automation in Construction

Year 2025, Volume: 12 Issue: 3, 706 - 736, 30.09.2025
https://doi.org/10.54287/gujsa.1750405

Abstract

The increasing complexity of today's construction projects makes advanced data management and interoperability solutions essential for optimizing decision-making processes, ensuring regulatory compliance, and enabling real-time monitoring. While traditional BIM methods are effective in terms of graphical visualization, they lack structured parametric and regulatory data integration, which limits their potential for synchronization with digital twin systems. This data fragmentation leads to inefficiencies in automation processes, reducing the effectiveness of predictive analytics and lifecycle adaptability. To address this gap, this study presents a BIM framework based on the ISO 23247 standard, aiming to achieve structured data management and digital twin integration by systematically classifying and organizing Graphical, Non-Graphical, and Document Data. The proposed framework enhances BIM's functionality as an intelligent asset management system by increasing interoperability, enabling automated compliance verification, and strengthening sensor-driven analysis. Industry case studies validate the framework's adaptability across design models, regulatory documents, and predictive analyses, and demonstrate its scalability in digital construction environments. Additionally, this study highlights the role of AI-powered compliance automation in optimizing regulatory oversight and operational efficiency and examines its potential for industry-wide standardization. Future research should focus on expanding digital twin applications, integrating AI-powered automation, and developing structured BIM methods. This study provides a solid foundation for data-driven construction management by aligning BIM workflows with ISO 23247, ensuring long-term scalability and efficiency.

References

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  • Ammar, A., Nassereddine, H., AbdulBaky, N., AbouKansour, A., Tannoury, J., Urban, H., & Schranz, C. (2022). Digital twins in the construction industry: a perspective of practitioners and building authority. Frontiers in Built Environment, 8, 834671. https://doi.org/10.3389/fbuil.2022.834671
  • Aragón, A., Arquier, M., Tokdemir, O. B., Enfedaque, A., Alberti, M. G., Lieval, F., Loscos, E., Pavón, R. M., Novischi, D. M., Legazpi, P. V., & Yagüe, Á. (2025). Seeking a definition of digital twins for construction and infrastructure management. Applied Sciences, 15(3), 1557. https://doi.org/10.3390/app15031557
  • Ba, L., Tangour, F., El Abbassi, I., & Absi, R. (2025). Analysis of digital twin applications in energy efficiency: a systematic review. Sustainability, 17(8), 3560. https://doi.org/10.3390/su17083560
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  • D'Amico, R. D., Erkoyuncu, J. A., Addepalli, S., & Penver, S. (2022). Cognitive digital twin: an approach to improve the maintenance management. CIRP Journal of Manufacturing Science and Technology, 38, 613-630. https://doi.org/10.1016/j.cirpj.2022.06.004
  • El Bazi, N., Mabrouki, M., Laayati, O., Ouhabi, N., El Hadraoui, H., Hammouch, F.-E., & Chebak, A. (2023). Generic multi-layered digital-twin-framework-enabled asset lifecycle management for the sustainable mining industry. Sustainability, 15(4), 3470. https://doi.org/10.3390/su15043470
  • Faliagka, E., Christopoulou, E., Ringas, D., Politi, T., Kostis, N., Leonardos, D., Tranoris, C., Antonopoulos, C. P., Denazis, S., & Voros, N. (2024). Trends in digital twin framework architectures for smart cities: a case study in smart mobility. Sensors, 24(5), 1665. https://doi.org/10.3390/s24051665
  • Ferko, E., Bucaioni, A., & Behnam, M. (2022). Architecting digital twins. IEEE Access, 10, 50335-50350. https://doi.org/10.1109/ACCESS.2022.3172964
  • Galuzin, V., Galitskaya, A., Grachev, S., Larukhin, V., Novichkov, D., Skobelev, P., & Zhilyaev, A. (2022). Autonomous digital twin of enterprise: method and toolset for knowledge-based multi-agent adaptive management of tasks and resources in real time. Mathematics, 10(10), 1662. https://doi.org/10.3390/math10101662
  • Ghorbani, Z., & Messner, J. (2024). A categorical approach for defining digital twins in the AECO industry. Journal of Information Technology in Construction, 29, 198-218. https://doi.org/10.36680/j.itcon.2024.010
  • Guerra, V., Hamon, B., Bataillou, B., Inamdar, A., & van Driel, W. D. (2024). Towards a digital twin architecture for the lighting industry. Future Generation Computer Systems, 155, 80-95. https://doi.org/10.1016/j.future.2024.01.028
  • Hakiri, A., Gokhale, A., Yahia, S. Ben, & Mellouli, N. (2024). A comprehensive survey on digital twin for future networks and emerging internet of things industry. Computer Networks, 244, 110350. https://doi.org/10.1016/j.comnet.2024.110350
  • Hananto, A. L., Tirta, A., Herawan, S. G., Idris, M., Soudagar, M. E. M., Djamari, D. W., & Veza, I. (2024). Digital twin and 3d digital twin: concepts, applications, and challenges in industry 4.0 for digital twin. Computers, 13(4), 100. https://doi.org/10.3390/computers13040100
  • Huang, H., Ji, T., & Xu, X. (2022). Digital Twin platforms: architectures and functions. Volume 2: Manufacturing Processes; Manufacturing Systems, 85819, V002T06A008. https://doi.org/10.1115/MSEC2022-85085
  • Iliuţă, M.-E., Moisescu, M.-A., Pop, E., Ionita, A.-D., Caramihai, S.-I., & Mitulescu, T.-C. (2024). Digital twin—a review of the evolution from concept to technology and its analytical perspectives on applications in various fields. Applied Sciences, 14(13), 5454. https://doi.org/10.3390/app14135454
  • Iranshahi, K., Brun, J., Arnold, T., Sergi, T., & Müller, U. C. (2025). Digital twins: recent advances and future directions in engineering fields. Intelligent Systems with Applications, 26, 200516. https://doi.org/10.1016/j.iswa.2025.200516
  • Karatzas, S., Papageorgiou, G., Lazari, V., Bersimis, S., Fousteris, A., Economou, P., & Chassiakos, A. (2024). A text analytic framework for gaining insights on the integration of digital twins and machine learning for optimizing indoor building environmental performance. Developments in the Built Environment, 18, 100386. https://doi.org/10.1016/j.dibe.2024.100386
  • Krishnamenon, M., Tuladhar, R., Azghadi, M. R., Loughran, J. G., & Pandey, G. (2021). Digital twins and their significance in engineering asset management. 2021 International Conference on Maintenance and Intelligent Asset Management (ICMIAM), 1-6. https://doi.org/10.1109/ICMIAM54662.2021.9715200
  • Kumar, R., & Agrawal, N. (2024). Shaping the future of industry: understanding the dynamics of industrial digital twins. Computers & Industrial Engineering, 191, 110172. https://doi.org/10.1016/j.cie.2024.110172
  • Lindkvist, C. M., Hafeld, A., & Haugen, T. B. (2022). Interfacing between FM and project phases through digital processes and collaborative practices. IOP Conference Series: Earth and Environmental Science, 1101(6), 062010. https://doi.org/10.1088/1755-1315/1101/6/062010
  • Liu, Y., Feng, J., Lu, J., & Zhou, S. (2024). A review of digital twin capabilities, technologies, and applications based on the maturity model. Advanced Engineering Informatics, 62, 102592. https://doi.org/10.1016/j.aei.2024.102592
  • Luther, W., Baloian, N., Biella, D., & Sacher, D. (2023). Digital twins and enabling technologies in museums and cultural heritage: an overview. Sensors, 23(3), 1583. https://doi.org/10.3390/s23031583
  • Mata, O., Ponce, P., Perez, C., Ramirez, M., Anthony, B., Russel, B., Apte, P., MacCleery, B., & Molina, A. (2025). Digital twin designs with generative AI: crafting a comprehensive framework for manufacturing systems. Journal of Intelligent Manufacturing, 1-24. https://doi.org/10.1007/s10845-025-02583-8
  • Michael, J., Cleophas, L., Zschaler, S., Clark, T., Combemale, B., Godfrey, T., Khelladi, D. E., Kulkarni, V., Lehner, D., Rumpe, B., Wimmer, M., Wortmann, A., Ali, S., Barn, B., Barosan, I., Bencomo, N., Bordeleau, F., Grossmann, G., Karsai, G., … Vangheluwe, H. (2025). Model‐driven engineering for digital twins: opportunities and challenges. Systems Engineering, 28(5), 659-670. https://doi.org/10.1002/sys.21815
  • Mihai, S., Yaqoob, M., Hung, D. V, Davis, W., Towakel, P., Raza, M., Karamanoglu, M., Barn, B., Shetve, D., Prasad, R. V, Venkataraman, H., Trestian, R., & Nguyen, H. X. (2022). Digital twins: a survey on enabling technologies, challenges, trends and future prospects. IEEE Communications Surveys & Tutorials, 24(4), 2255-2291. https://doi.org/10.1109/COMST.2022.3208773
  • Moiceanu, G., & Paraschiv, G. (2022). Digital twin and smart manufacturing in industries: a bibliometric analysis with a focus on industry 4.0. Sensors, 22(4), 1388. https://doi.org/10.3390/s22041388
  • Mylonas, G., Kalogeras, A., Kalogeras, G., Anagnostopoulos, C., Alexakos, C., & Munoz, L. (2021). Digital twins from smart manufacturing to smart cities: a survey. IEEE Access, 9, 143222-143249. https://doi.org/10.1109/ACCESS.2021.3120843
  • Nhamage, I. A. (2023). Development of BIM-based digital twin model for fatigue assessment in metallic railway bridges. U.Porto Journal of Engineering, 9(5), 12-23. https://doi.org/10.24840/2183-6493_009-005_001565
  • Nour El-Din, M., Pereira, P. F., Poças Martins, J., & Ramos, N. M. M. (2022). Digital twins for construction assets using BIM standard specifications. Buildings, 12(12), 2155. https://doi.org/10.3390/buildings12122155
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There are 50 citations in total.

Details

Primary Language English
Subjects Civil Construction Engineering, Architectural Engineering
Journal Section Research Article
Authors

Murat Aydın 0000-0002-3928-2936

Publication Date September 30, 2025
Submission Date July 25, 2025
Acceptance Date September 22, 2025
Published in Issue Year 2025 Volume: 12 Issue: 3

Cite

APA Aydın, M. (2025). A Data-Driven BIM Framework for Digital Twin Integration with ISO 23247-Compliant Automation in Construction. Gazi University Journal of Science Part A: Engineering and Innovation, 12(3), 706-736. https://doi.org/10.54287/gujsa.1750405