Artificial Intelligence in Building Information Modeling Research: Country and Document-based Citation and Bibliographic Coupling Analysis

Öz

The intense association of the architecture, engineering, construction, operation, and facility management (AECO/FM) industry with cognitive and behavioral technologies leads to the increase in productivity of industry activities. In light of these thoughts, the building information modeling (BIM) platform is included in the AECO/FM industry to further increase efficiency and deliver construction projects economically, timely, and safely. While the BIM platform can work integrated with many programs and systems, concepts that offer innovative and fast solutions such as artificial intelligence (AI) benefit the AECO/FM industry. The main aim of this study is to understand the tendency of AI in BIM research carried out in different countries and by various scholars. This study adopts a bibliometric search, and a scientometric analysis and mapping approach with applying document-based citation analysis, country-based citation analysis, and country-based bibliographic coupling analysis of scientific research of AI and BIM integration. Data on the use of AI and BIM has been collected by reviewing and screening articles selected from the Scopus database. The results reveal that information management, decision support systems, genetic algorithms, neural networks, knowledge-based systems, machine learning, and deep learning effect AI in BIM research. This article contributes to the AECO/FM literature by analyzing and visualizing the current status and relationship between AI and BIM. Therefore, the findings highlight the gaps and trends in AI and BIM studies and provide new recommendations for future studies.

Anahtar Kelimeler

• Artificial Intelligence
• Building Information Modeling
• Scientometric Analysis
• Scientometric Mapping

Kaynakça

1. 1. Liu Z, Lu Y, Peh L C (2019) A review and scientometric analysis of Global Building Information Modeling (BIM) Research in the Architecture, Engineering and Construction (AEC) industry. Buildings, 9, 10 DOI: 10.3390/buildings9100210.
2. 2. Ganzha M, Paprzycki M, Pawłowski W, Szmeja P, Wasielewska K (2017) Semantic interoperability in the Internet of Things: An overview from the INTER-IoT perspective. Journal of Network and Computer Applications, 81, 111-124 DOI: 10.1016/j.jnca.2016.08.007.
3. 3. Aljobaly O, Banawi A (2020) Evaluation of the Saudi Construction Industry for Adoption of Building Information Modelling. Advances in Artificial Intelligence, Software and Systems Engineering. 488-498 DOI: 10.1007/978-3-030-20454-9_49.
4. 4. Parveen, R. (2018). Artificial intelligence in construction industry: Legal issues and regulatory challenges. International Journal of Civil Engineering and Technology, 9(13), 957-962.
5. 5. Guzmán, J.I. And Malcolm, A.A. (2002) "Autonomous vehicles in the construction process", Construction Innovation, Vol. 2 Issue: 3, pp.211- 224, https://doi.org/10.1108/14714170210814775.
6. 6. Li, C. Z., Xue, F., Li, X., Hong, J., and Shen, G. Q. (2018). An internet of things-enabled BIM platform for on-site assembly services in prefabricated construction. Automation in Construction, 89, 146-161. doi:10.1016/j.autcon.2018.01.001. 7. Bruno, S., De Fino, M., and Fatiguso, F. (2018). Historic building information modelling: Performance assessment for diagnosis-aided information modelling and management. Automation in Construction, 86, 256-276. doi:10.1016/j.autcon.2017.11.009.
7. 8. O'Dwyer, E., Pan, I., Acha, S., and Shah, N. (2019). Smart energy systems for sustainable smart cities: Current developments, trends and future directions. Applied Energy, 237, 581-597. doi:10.1016/j.apenergy.2019.01.024.
8. 9. Hwang, B., Shan, M., and Looi, K. (2018). Knowledge-based decision support system for prefabricated prefinished volumetric construction. Automation in Construction, 94, 168-178. doi:10.1016/j.autcon.2018.06.016.

9. 10. Sacks, R., Girolami, M., Brilakis, I. (2020). Building Information Modelling, Artificial Intelligence and Construction Tech, Developments in the Built Environment, 100011, ISSN 2666-1659, https://doi.org/10.1016/j.dibe.2020.100011. 11. Ozturk, G.B. Interoperability in building information modeling for AECO/FM industry. Automation in Construction, 2020, 113, 103122. https://doi.org/10.1016/j.autcon.2020.103122. 12. Xu, Y.; Zeng, J.; Chen, W.; Jin, R.; Li, B.; and Pan, Z. (2018). “A holistic review of cement composites reinforced with graphene oxide”, Construction Building Materials, Vol. 171, pp.291–302. https:// doi.org/10.1016/j.conbuildmat.2018.03.147. 13. Cobo, M.J.; López-Herrera, A.G.; Herrera-Viedma, E.; and Herrera, F. (2011). “An approach for detecting, quantifying, and visualizing the evolution of a research field: a practical application to the fuzzy sets theory field”, Journal of Informetrics, Vol. 5 No. 1, pp.146-166. https:// doi.org/10.1016/j.joi.2010.10.002.
10. 14. Hosseini, M.R.; Martek, I.; Zavadskas, E.K.; Aibinu, A.A.; Arashpour, M.; and Chileshe, N. (2018). “Critical evaluation of off-site construction research: a scientometric analysis”, Automation in Construction, Vol. 87, pp.235–247. https://doi.org/10.1016/j.autcon.2017.12. 002.
11. 15. Van Eck, N.J. and Waltman, L. (2014). “Visualizing bibliometric networks”, in: Y. Ding, R. Rousseau, & D. Wolfram (Eds.), Measuring Scholarly Impact: Methods and Practice, Springer, Cham, pp. 285–320. http://dx.doi.org/10.1007/978-3-319-10377-8_13. 16. Natephra, W., Yabuki, N., and Fukuda, T. (2018). Optimizing the evaluation of building envelope design for thermal performance using a BIM-based overall thermal transfer value calculation. Building and Environment, 136, 128-145. doi:10.1016/j.buildenv.2018.03.032. 17. Parra, J., Pérez-Pons, M., and González, J. (2021). Technology as a lever for the evolution and recovery of the financial and construction sectors in Spain doi:10.1007/978-3-030-53829-3_17.
12. 18. Khairulzaman, H. A., and Usman, F. (2018). Automation in civil engineering design in assessing building energy efficiency. International Journal of Engineering and Technology, 7(4), 722-727. doi:10.14419/ijet.v7i4.35.2309. 19. Bruno S, Fino M D, Fatiguso F (2018) Automation in Construction Historic Building Information Modelling: performance assessment for diagnosis-aided information modelling and management. Automation in Construction. 86, December 2017, 256-276 DOI: 10.1016/j.autcon.2017.11.009. 20. Chen K, Lu W, Peng Y, Rowlinson S, Huang G Q (2015) Bridging BIM and building: From a literature review to an integrated conceptual framework. International Journal of Project Management. 33, 6, 1405-1416 DOI: 10.1016/j.ijproman.2015.03.006. 21. Golparvar-Fard M, Peña-Mora F, Savarese S (2015) Automated Progress Monitoring Using Unordered Daily Construction Photographs and IFC-Based Building Information Models. Journal of Computing in Civil Engineering. 29, 1 DOI: 10.1061/(ASCE)CP.1943-5487.0000205. 22. Huang Y, Huang K, Wang L, Tao D, Tan T, Li X (2008) Enhanced biologically inspired model. IEEE Conference on Computer Vision and Pattern Recognition. DOI: 10.1109/CVPR.2008.4587599.
13. 23. Jalaei F, Canada C, Jrade A (2015) Integrating decision support system (DSS) and building information modeling (BIM) to optimize the selection of sustainable building components Journal of Information Technology in Construction, 20, 399-420 DOI: itcon.org/2015/25.
14. 24. In J, Kim J, Fischer M, Orr R (2015) Automation in Construction BIM-based decision-support method for master planning of sustainable large-scale developments. Automation in Construction, 58, 95-108 DOI: 10.1016/j.autcon.2015.07.003. 25. Liu R, Yang B, Zio E, Chen X (2018) Artificial intelligence for fault diagnosis of rotating machinery: A review. Mechanical Systems and Signal Processing, 108, 33-47 DOI: 10.1016/j.ymssp.2018.02.016.
15. 26. Memon A H, Rahman I A, Memon I, Azman N I A (2014) BIM in Malaysian Construction Industry: Status, Advantages, Barriers and Strategies to Enhance the Implementation Level. Research Journal of Applied Sciences, Engineering and Technology, 606-614 DOI: airitilibrary.com/Publication/alDetailedMesh?docid=20407467-201408-201502170022-201502170022-606-614.
16. 27. Teizer J (2015) Status quo and open challenges in vision-based sensing and tracking of temporary resources on infrastructure construction sites. Advanced Engineering Informatics, 29, 2, 225-238 DOI: 10.1016/j.aei.2015.03.006.
17. 28. Tixier A J, Hallowell M R, Rajagopalan B, Bowman D (2016) Automation in Construction Application of machine learning to construction injury prediction. Automation in Construction, 69, 102-114 DOI: 10.1016/j.autcon.2016.05.016.
18. 29. Woo J, Menassa C (2014) Virtual Retrofit Model for aging commercial buildings in a smart grid environment. Energy and Buildings, 80, 424-435 DOI: 10.1016/j.enbuild.2014.05.004.
19. 30. Zhang J, El-gohary, N M (2017) Automation in Construction Integrating semantic NLP and logic reasoning into a unified system for fully-automated code checking. Automation in Construction, 73, 45-57 DOI: 10.1016/j.autcon.2016.08.027.