Mimaride Dijital İkiz Prototipleme ve Simülasyon Süreci İçin YBM ve IoT Entegrasyonu ile İş Akışı Önerisi

Öz

Bina Bilgi Modellemesi (BIM) ile gerçek zamanlı Nesnelerin İnterneti (IoT) veri akışlarının entegrasyonu, mimari dijital ikizlerin geliştirilmesinde temel bir yaklaşım olarak ortaya çıkmaktadır. Bu çalışma, BIM ve IoT teknolojilerinin entegrasyonuyla mimari bir dijital ikizin elde edilmesine yönelik yapılandırılmış ve tekrarlanabilir bir süreç önermektedir. Önerilen çerçevede, 35 m²'lik kapalı bir odadan Arduino tabanlı sensörler kullanılarak gerçek zamanlı çevresel veriler toplanmış ve bu veriler Excel ve Dynamo aracılığıyla işlenerek Revit tabanlı BIM modeline senkronize edilmiştir. Sonuçlar, çevresel verilerin BIM ortamı ile başarılı ve kesintisiz bir şekilde gerçek zamanlı olarak senkronize edildiğini göstermekte ve bina performansı ile konfor koşullarının optimize edilmesi için değerli içgörüler sunmaktadır. Ancak, daha büyük alanlara ölçeklenebilirlik ve yanıt süresi gecikmeleri gibi bazı sınırlamalar daha fazla araştırma gerektirmektedir. Bu çalışma, gerçek zamanlı izleme ve dijital görselleştirme entegrasyonu ile akıllı bina yönetimi için yenilikçi bir süreç önerisi sunmaktadır. Gelecekteki çalışmaların, otomasyon iş akışlarını iyileştirmeye, gecikmeleri azaltmaya ve daha karmaşık çok odalı ortamlara uygulanabilirliği genişletmeye odaklanması önerilmektedir. Bu araştırma, mimarlar ve tesis yöneticileri için veri odaklı, gerçek zamanlı çevresel izleme ve gelişmiş karar verme süreçlerini mümkün kılarak dijital ikizler alanına önemli bir katkı sağlamaktadır.

Anahtar Kelimeler

• Dijital ikiz
• Yapı Bilgi Modelleme (YBM)
• Nesnelerin interneti (IOT)
• Otomatik görselleştirme
• Çevresel sensörler

Digital Twin Prototyping and Simulation Process Proposal in Architecture Through the Integration of BIM and IOT

Abstract

Building Information Modelling (BIM) integrated with real-time Internet of Things (IoT) data streams is emerging as a foundational approach in the development of architectural digital twins. This study proposes a structured and replicable process to achieve an architectural digital twin by integrating BIM and IoT technologies. In the proposed framework, real-time environmental data from a 35 m² indoor room was collected using Arduino-based sensors and processed through Excel and Dynamo to synchronize with a Revit-based BIM model. The results demonstrate successful and continuous real-time synchronization of environmental data with the BIM environment, offering valuable insights for optimizing building performance and comfort conditions. However, limitations such as scalability to larger spaces and response time delays require further research. The study presents a novel pipeline for smart building management by integrating real-time monitoring and digital visualization. Future studies should focus on improving automation workflows, minimizing latency, and extending applicability to complex, multi-room settings. This research contributes to the growing field of digital twins by enabling data-driven, real-time environmental monitoring and enhanced decision-making for architects and facility managers.

Keywords

• Digital twin
• Building information modeling (BIM)
• Internet of things (IOT)
• Automated Visualization
• Environmental sensors.

References

1. Abar, S., Theodoropoulos, G. K., Lemarinier, P., & O’Hare, G. M. P. (2017). Agent based modelling and simulation tools: A review of the state-of-art software. Computer Science Review, 24, 13–33. https://doi.org/10.1016/j.cosrev.2017.03.001
2. Aliane, N. (2010). Data acquisition and real-time control using spreadsheets: interfacing Excel with external hardware. ISA transactions, 49(3), 264–269. https://doi.org/10.1016/j.isatra.2010.03.009
3. Al-Qattan, E., Yan, W., & Galanter, P. (2017). Establishing parametric relationships for design objects through tangible interaction. In P. Janssen, P. Loh, A. Raonic, & M. A. Schnabel (Eds.), Protocols, flows and glitches: Proceedings of the 22nd International Conference on Computer-Aided Architectural Design Research in Asia (CAADRIA 2017) (pp. 147–156). The Association for Computer-Aided Architectural Design Research in Asia (CAADRIA). https://doi.org/10.52842/conf.caadria.2017.147
4. Chan, D. W. M., Olawumi, T. O., & Ho, A. M. L. (2019). Perceived benefits of and barriers to Building Information Modelling (BIM) implementation in construction: The case of Hong Kong. Journal of Building Engineering, 25. https://doi.org/10.1016/j.jobe.2019.100764
5. Chang, K. M., Dzeng, R. J., & Wu, Y. J. (2018a). An automated IoT visualization BIM platform for decision support in facilities management. Applied Sciences, 8(7). https://doi.org/10.3390/app8071086
6. Chehab, A., & Artail, H. (2004). Spreadsheet applications in electrical engineering: A review. International Journal of Engineering Education, 20(6), 902–908.
7. Doukari, O., Kassem, M., & Greenwood, D. (2023). Building Information Modelling. In T. Lynn & P. Rosati (Eds.), Palgrave studies in digital business and enabling technologies (pp. 39–51). Palgrave Macmillan. https://doi.org/10.1007/978-3-031-32309-6_3
8. Del Grosso, A., Basso, P., Ruffini, L., Figini, F., & Cademartori, M. (2017). Infrastructure management integrating SHM and BIM procedures. In M. Motavalli, A. Ilki, B. Havranek, & P. Inci (Eds.), Proceedings of the 4th International Conference on Smart Monitoring, Assessment and Rehabilitation of Civil Structures (SMAR 2017). Empa.

9. Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645–1660. https://doi.org/10.1016/j.future.2013.01.010
10. Kensek, K. M. (2014). Integration of environmental sensors with BIM: Case studies using Arduino, Dynamo, and the Revit API. Informes de La Construccion, 66(536). https://doi.org/10.3989/ic.13.151
11. Ku, K., & Taiebat, M. (2011). BIM experiences and expectations: The constructors’ perspective. International Journal of Construction Education and Research, 7(3), 175–197. https://doi.org/10.1080/15578771.2010.544155
12. Latiffi, A. A., Brahim, J., Mohd, S., & Fathi, M. S. (2015). Building information modeling (BIM): exploring level of development (LOD) in construction projects. Applied mechanics and materials, 773, 933-937. https://doi.org/10.4028/www.scientific.net/amm.773-774.933
13. Lin, Y. H., Liu, Y. S., Gao, G., Han, X. G., Lai, C. Y., & Gu, M. (2013). The IFC-based path planning for 3D indoor spaces. Advanced Engineering Informatics, 27(2), 189–205. https://doi.org/10.1016/j.aei.2012.10.001
14. Lu, Q., Parlikad, A. K., Woodall, P., Don Ranasinghe, G., Xie, X., Liang, Z., Konstantinou, E., Heaton, J., & Schooling, J. (2020). Developing a digital twin at building and city levels: Case Study of West Cambridge Campus. Journal of Management in Engineering, 36(3). https://doi.org/10.1061/(asce)me.1943-5479.0000763
15. Miettinen, R., & Paavola, S. (2014). Beyond the BIM utopia: Approaches to the development and implementation of building information modelling. Automation in Construction, 43, 84–91. https://doi.org/10.1016/j.autcon.2014.03.009
16. Natephra, W., & Motamedi, A. (2019). Live data visualization of IoT sensors using augmented reality (AR) and BIM. In M. Al-Hussein (Ed.), Proceedings of the 36th International Symposium on Automation and Robotics in Construction (ISARC 2019) (pp. 632–638). IAARC. https://doi.org/10.22260/isarc2019/0084
17. Oh, S., & Song, S. (2021). Detailed analysis of thermal comfort and indoor air quality using real-time multiple environmental monitoring data for a childcare center. Energies, 14(3). https://doi.org/10.3390/en14030643
18. Park, H. J. (2008). Evolution+ BIM: The utilization of building information modelling at an early design stage. CAADRIA 2008, 552–559.
19. Ruemler, S. P., Zimmerman, K. E., Hartman, N. W., Hedberg, T., & Feeney, A. B. (2016). Promoting model-based definition to establish a complete product definition. In Proceedings of the ASME 2016 11th International Manufacturing Science and Engineering Conference: Vol. 2. Materials; biomanufacturing; properties, applications and systems; sustainable manufacturing (Paper No. MSEC2016-8702). American Society of Mechanical Engineers. https://doi.org/10.1115/MSEC2016-8702
20. Shafiril, S., Yusoff, A., & Yusoff, N. C. (2016). The development of an automated system in detecting environmental data for the monitoring of forest activity. International Journal of Environmental Science and Development, 7(7), 532–536. https://doi.org/10.18178/ijesd.2016.7.7.834
21. Shishina, D., & Sergeev, P. (2019). Revit dynamo: Designing objects of complex forms. Toolkit and process automation features. Architecture and Engineering, 4(3), 30–38. https://doi.org/10.23968/2500-0055-2019-4-3-30-38
22. Smarsly, K., & Tauscher, E. (2016). Monitoring information modelling for semantic mapping of structural health monitoring systems. In N. Yabuki & K. Makanae (Eds.), Proceedings of the 16th International Conference on Computing in Civil and Building Engineering (ICCCBE 2016). Osaka University. http://www.see.eng.osaka-u.ac.jp/seeit/icccbe2016/Proceedings/Full_Papers/001-004.pdf
23. Tang, S., Shelden, D. R., Eastman, C. M., Pishdad-Bozorgi, P., & Gao, X. (2019). A review of building information modelling (BIM) and the internet of things (IoT) devices integration: Present status and future trends. Automation in Construction, 101, 127–139. https://doi.org/10.1016/j.autcon.2019.01.020
24. Teizer, J., Wolf, M., Golovina, O., Perschewski, M., Propach, M., Neges, M., & König, M. (2017). Internet of Things (IoT) for integrating environmental and localization data in building ınformation modelling (BIM). ISARC 2017 - Proceedings of the 34th International Symposium on Automation and Robotics in Construction, 603–609. https://doi.org/10.22260/isarc2017/0084
25. Valinejadshoubi, M., Moselhi, O., Bagchi, A., & Salem, A. (2021). Development of an IoT and BIM-based automated alert system for thermal comfort monitoring in buildings. Sustainable Cities and Society, 66. https://doi.org/10.1016/j.scs.2020.102602
26. Wehbe, R., & Shahrour, I. (2019). Use of BIM and smart monitoring for buildings’ indoor comfort control. MATEC Web of Conferences, 295, Article 02010. https://doi.org/10.1051/matecconf/201929502010
27. Wei, Z., Calautit, J., Wei, S., & Tien, P. W. (2024). Real-time clothing insulation level classification based on model transfer learning and computer vision for PMV-based heating system optimization through piecewise linearization. Building and Environment, 111277. https://doi.org/10.1016/j.buildenv.2024.111277
28. Wu, I. C., & Liu, C. C. (2020). A visual and persuasive energy conservation system based on BIM and IoT technology. Sensors, 20(1). https://doi.org/10.3390/s20010139
29. Yin, C., & McKay, A. (2018). Introduction to modelling and simulation techniques. In Proceedings of the 8th International Symposium on Computational Intelligence and Industrial Applications (ISCIIA 2018) and the 12th China–Japan International Workshop on Information Technology and Control Applications (ITCA 2018), Tengzhou, Shandong, China, 2–6 November 2018.
30. Zanchetta, C., Cecchini, C., & Bellotto, C. (2018). BIM-based multi-objective optimization process for energy and comfort simulation: Existing tools analysis and workflow proposal on a case study. Journal of Building Sustainability, 1(1), 11–26.