Abstract
In the field of construction, a digital twin can provide insight into the performance of a building or infrastructure throughout its lifecycle, from design to operation and maintenance. The Digital twin as a concept is not the birth of today, it has emerged over the past decade in industry and production. Nowadays many of the fields are developing their sectors using digital twins. In fact, we are currently witnessing a revolution in the field of digital twins, particularly in the construction sector. This paper aims to provide different definitions for the digital twin in the construction sector, and represents its advantages and limits. It also outlines the steps of digitalizing a site during construction. The process of digitizing a construction includes various steps such as data collection, modeling and visualization, requiring a cooperative effort between all stakeholders. In general, the digital twin has the potential to revolutionize the construction industry, although it requires attentive planning and execution to realize all its benefits.
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References
Barbosa, F., et al.: Reinventing Construction: A Route of Higher Productivity. McKinsey Global Institute (2017)(Accessed 01 June 2021)
Chui, M., Mischke, J.: The Impact and Opportunities of Automation in Construction, McKinsey & Company (December 2021). https://www.mckinsey.com/business-functions/operations/our-insights/the-impact-and-opportunities-of-automation-in-construction (Accessed 01 June 2021)
Shah, Z., Aamir M., Syed A.H., MD. Jalil P. , Mikael G., Mohsen G.: Industrial digital twins at the nexus of NextG wireless networks and computational intelligence: a survey, J. Netw. Comput. Appli. (2022)
Negri, E., Fumagalli, L., Macchi, M.: A review of the roles of digital twin in CPS-based production systems. In: 27th International Conference on Flexible Automation and Intelligent Manufacturing, Modena, Italy (2017)
Glaessgen, E., Stargel, D.: The digital twin paradigm for futur NASA and US air force vehicles. In: 3rd AIAA/ASME/ASCE AHS/ASC Structures, Structural Dynamics and Materials Conference (2012)
Kritzinger, W., Karner, M., Traar, G., Henjes, J., Sihn, W.: Digital twin in manufacturing: a categorical literature review and classification. 1In: 6th IFAC Symposium on Information Control Problems in Manufacturing; Bergamo, Italy (2018)
Zavari, M., Shahhosseini, V., Ardeshir, A., Sebt, M.H.: Multi-objective optimization of dynamic construction site layout using BIM and GIS. J. Build. Eng. (2022)
Baduge, S.K., et al.: Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications. Autom. Const. (2022)
Rao, A.S., et al.: Real-time monitoring of construction sites: Sensors, methods, and applications. Autom. Const. (2022)
Roberston, C.: (30 January 2023). https://medium.com/@caserobertson/the-difference-between-ground-based-and-remote-sensing-methods-of-data-collection-e232a58f791f
Ali, M., et al.: Use of satellite imagery in constructing a household GIS database for health studies in Karachi, Pakistan. Inter. J. Health Geograph. (2004)
Jensen, J.R.: Introductory Digital Image Processing: A Remote Sensing Perspective (2004)
Yin, Y., Antonio, J.: Application of 3D laser scanning technology for image data processing in the protection of ancient building sites through deep learning. Image Vis. Comput. 102, 103969 (2020)
Shanti, M.Z., Cho, C.S., de Soto, B.G., Byon, Y.J., Yeun, C.Y., Kim, T.Y.: Real-time monitoring of work-at-height safety hazards in construction sites using drones and deep learning. J. Safety Res. 83, 364–70 (2022)
Stone, P.: (product strategist, flow forma) (19 May 2020). https://www.flowforma.com/blog/5-key-trends-for-digitalization-in-the-construction-industry
Paul, S.T.: (product strategist, flow forma) (19 May 2022). https://www.flowforma.com/blog/5-key-trends-for-digitalization-in-the-construction-industry
Zhao, L.J., Chen, L.J., Ranjan, R., Choo, K.K.R., He, J.J.: Geographical information system parallelization for spatial big data processing. Rev. Cluster Comput. J. Netw. Softw. Tools Appli. 19(1), 139–152 (2016)
Wu, J.Y., Ta, N., Song, Y., Lin, J., Chai, Y.W.: Urban form breeds neighborhood vibrancy: A case study using a GPS-based activity survey in suburban Beijing. Cities 74, 100–108 (2018)
Kenneth, J., Flannigan: (2022). https://www.autodesk.com/solutions/construction-sequencing-workflow
Rao, A.S., et al.: Real-time monitoring of construction sites: Sensors, methods, and applications. Autom. Const. 136, 104099 (2022)
Moselhi, O., Bardareh, H., Zhu, Z.: Automated data acquisition in construction with remote sensing technologies. Applo. Sci. 10(8), 2846 (2020)
Kenneth, J., Dueker: vol. 91(9 ) (1 Janauary 1995). https://ascelibrary.org/doi/abs/, https://doi.org/10.1061/(ASCE)08873801(1995)9
Sampaio, A., Henriques, P., Martins, O.: Virtual Reality technology used in civil engineering education. Open Virt. Reality J. 2, 18–25 (2010)
Koskela, M., Kiltti, P., Vilpola, I., Tervonen, J.: Suitability of a virtual learning environment for higher education. Electr. J. e-Learn. 3, 21–30 (2005)
Reiners, D., Stricker; D., Klinker, G., Müller, S.: Augmented reality for construction tasks: Doorlock assembly. In: International workshop on Augmented Reality: Placing Artificial Objects in Real Scenes, pp. 31–46 (10 Nov 1999)
Boje, C., Guerriero, A., Kubicki, S., Rezgui, Y.: Towards a semantic construction digital twin: directions for future research. Autom. Const. 114, 103179 (2020)
Peter, P., Jane, M.: The ‘how’ of benefits management for digital technology: From engineering to asset management. Autom. Const. (2019)
Jiang, F., Ma, L., Broyd, T., Chen, K.: Digital twin and its implementations in the civil engineering sector. Autom. Constr. 130, 103838 (2021)
Singh, M., Fuenmayor, E., Hinchy, E.P., Qiao, Y., Murray, N., Devine, D.: Digital Twin: Origin to Future. Appl. Syst. Innov. 4, 36 (2021)
Lim, K., Zheng, P., Chen, C.: A state-of-the-art survey of Digital Twin: Techniques, engineering product lifecycle management and business innovation perspectives. J. Intell. Manuf. 31, 1313–1337 (2020)
Harrison, R., Vera, D., Ahmad, B.: A Connective framework to support the lifecycle of cyber-physical production systems. Proc. IEEE 109, 568–581 (2021)
Rathore, M., Shah, S., Shukla, D., Bentafat, E., Bakiras, S.: The role of AI, machine learning, and big data in digital twinning: a systematic literature review, challenges, and opportunities. IEEE Access 9, 32030–32052 (2021)
Juarez, M., Botti, V., Giret, A.: Digital twins: review and challenges. J. Comput. Inf. Sci. Eng.Comput. Inf. Sci. Eng. 21, 030802 (2021)
Russell, H.: Sustainable urban governance networks: data-driven planning technologies and smart city software systems. Geopolit. Hist. Int. Relations 12, 9–15 (2020)
Funding
This research was carried out as part of the Research Chair “Digital twins of construction and infrastructure in their environment” at ESTP, funded by Egis, Bouygues Construction, Schneider Electric, BRGM, SNCF Réseau, and ENSAM.
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Al Omari, M., Eslahi, M., Meouche, R.E., Ducoulombier, L., Guillaumat, L. (2024). Digital Twin for Construction Sites: Concept, Definition, Steps. In: Ben Ahmed, M., Boudhir, A.A., El Meouche, R., Karaș, İ.R. (eds) Innovations in Smart Cities Applications Volume 7. SCA 2023. Lecture Notes in Networks and Systems, vol 938. Springer, Cham. https://doi.org/10.1007/978-3-031-54376-0_17
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