Digital Twin in the Architecture, Engineering, and Construction Industry: A Bibliometric Review
Publication: Construction Research Congress 2022
ABSTRACT
The architecture, engineering, and construction industry (AEC) is embracing digitization in the design, construction, and operation of built assets with the growing prominence of information technologies, such as building information modeling (BIM), internet of things (IoT), and artificial intelligence (AI). In this context, plenty of research efforts have been dedicated to digital twin (DT) applications. This research synthesizes state-of-art on DT in the AEC industry through bibliometric analysis, aiming to identify the research trends, challenges, and knowledge gaps in this growing area. A total of 75 publications regarding DT was identified and retrieved from Scopus. Then, VOSviewer was used for bibliometric analysis, including (1) keyword co-occurrence, and (2) citation analysis of selected publications. The identified research clusters and most-cited publications were discussed to clarify research trends and future needs. The findings revealed that future research should be directed to (1) data interoperability, (2) AIoT, and (3) AI. Moreover, extra research efforts should also be given to the DT applications during the design and construction phases of construction projects. This research contributes to the body of knowledge by quantitatively exposing research trends and needs for DT in the AEC industry.
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REFERENCES
Boje, C., Guerriero, A., Kubicki, S., and Rezgui, Y. (2020). Towards a semantic Construction Digital Twin: Directions for future research. Automation in Construction, 114, 103179. https://doi.org/10.1016/j.autcon.2020.103179.
Bosch-Sijtsema, P., Claeson-Jonsson, C., Johansson, M., and Roupe, M. (2021). The hype factor of digital technologies in AEC. Construction Innovation. https://doi.org/10.1108/CI-01-2020-0002.
El Jazzar, M., Piskernik, M., and Nassereddine, H. (2020). Digital twin in construction: An empirical analysis. EG-ICE 2020 Workshop on Intelligent Computing in Engineering, Proceedings, May, 501–510.
Fan, C., Jiang, Y., and Mostafavi, A. (2020). Social sensing in disaster city digital twin: Integrated textual–visual–geo framework for situational awareness during built environment disruptions. Journal of Management in Engineering, 36(3), 04020002.
Francisco, A., Mohammadi, N., and Taylor, J. E. (2020). Smart city digital twin–enabled energy management: Toward real-time urban building energy benchmarking. Journal of Management in Engineering, 36(2), 04019045.
Ham, Y., and Kim, J. (2020). Participatory sensing and digital twin city: updating virtual city models for enhanced risk-informed decision-making. Journal of Management in Engineering, 36(3), 04020005.
Havard, V., Jeanne, B., Lacomblez, M., and Baudry, D. (2019). Digital twin and virtual reality: a co-simulation environment for design and assessment of industrial workstations. Production and Manufacturing Research, 7(1), 472–489. https://doi.org/10.1080/21693277.2019.1660283.
Hou, L., Wu, S., Zhang, G. K., Tan, Y., and Wang, X. (2021). Literature Review of Digital Twins Applications in Construction Workforce Safety. Applied Sciences, 11(1), 339.
Kritzinger, W., Karner, M., Traar, G., Henjes, J., and Sihn, W. (2018). Digital Twin in manufacturing: A categorical literature review and classification. IFAC-PapersOnLine, 51(11), 1016–1022. https://doi.org/10.1016/j.ifacol.2018.08.474.
Lee, P. C., and Su, H. N. N. (2010). Investigating the structure of regional innovation system research through keyword co-occurrence and social network analysis. Innovation: Management, Policy and Practice. https://doi.org/10.5172/impp.12.1.26.
Lim, K. Y. H., Zheng, P., and Chen, C. H. (2019). A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives. Journal of Intelligent Manufacturing, 0123456789. https://doi.org/10.1007/s10845-019-01512-w.
Lin, Y. C., and Cheung, W. F. (2020). Developing WSN/BIM-based environmental monitoring management system for parking garages in smart cities. Journal of Management in Engineering, 36(3), 04020012.
Liu, H., Abudayyeh, O., and Liou, W. (2020). BIM-Based Smart Facility Management: A Review of Present Research Status, Challenges, and Future Needs. Construction Research Congress 2020: Computer Applications - Selected Papers from the Construction Research Congress 2020, January, 1087–1095. https://doi.org/10.1061/9780784482865.115.
Liu, X. (2013). Full-Text Citation Analysis : A New Method to Enhance. Journal of the American Society for Information Science and Technology, 64(July), 1852–1863. https://doi.org/10.1002/asi.
Loizou, S., Elgammal, A., Kumara, I., Christodoulou, P., Papazoglou, M. P., and Andreou, A. S. (2019). A Smart product co-design and monitoring framework via gamification and complex event processing (F. J., S. M., B. A., & H. S. (eds.); Vol. 2, pp. 237–244). SciTePress. https://doi.org/10.5220/0007720902370244.
Love, P. E., and Matthews, J. (2019). The ‘how’of benefits management for digital technology: From engineering to asset management. Automation in Construction, 107, 102930.
Lu, Q., Xie, X., Parlikad, A. K., and Schooling, J. M. (2020a). Digital twin-enabled anomaly detection for built asset monitoring in operation and maintenance. Automation in Construction, 118, 103277.
Lu, Q., Parlikad, A. K., Woodall, P., Don Ranasinghe, G., Xie, X., Liang, Z., Konstantinou, E., Heaton, J., and Schooling, J. (2020b). Developing a digital twin at building and city levels: Case study of West Cambridge campus. Journal of Management in Engineering, 36(3), 05020004.
Lu, Y., Liu, C., Wang, K. I. K., Huang, H., and Xu, X. (2020). Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues. Robotics and Computer-Integrated Manufacturing, 61(August 2019), 101837. https://doi.org/10.1016/j.rcim.2019.101837.
Tao, F., Qi, Q., Wang, L., and Nee, A. Y. C. (2019). Digital Twins and Cyber–Physical Systems toward Smart Manufacturing and Industry 4.0: Correlation and Comparison. Engineering, 5(4), 653–661. https://doi.org/10.1016/j.eng.2019.01.014.
Van Eck, N. J., and Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3.
Wei, Y., and Akinci, B. (2019). A vision and learning-based indoor localization and semantic mapping framework for facility operations and management. Automation in Construction, 107, 102915.
Wright, L., and Davidson, S. (2020). How to tell the difference between a model and a digital twin. Advanced Modeling and Simulation in Engineering Sciences, 7(1), 1–13.
Williams, R., Erkoyuncu, J. A., Masood, T., and Vrabic, R. (2020). Augmented reality assisted calibration of digital twins of mobile robots. IFAC-PapersOnLine, 53(3), 203–208. https://doi.org/10.1016/j.ifacol.2020.11.033.
Wu, J., Sadraddin, H. L., Ren, R., Zhang, J., and Shao, X. (2021). Invariant signatures of architecture, engineering, and construction objects to support BIM interoperability between architectural design and structural analysis. Journal of Construction Engineering and Management, 147(1), 04020148.
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Construction Research Congress 2022
Pages: 670 - 678
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Published online: Mar 7, 2022
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