The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Towards a semantic Construction Digital Twin: Directions for future research
Open accessAbstract
As the Architecture, Engineering and Construction sector is embracing the digital age, the processes involved in the design, construction and operation of built assets are more and more influenced by technologies dealing with value-added monitoring of data from sensor networks, management of this data in secure and resilient storage systems underpinned by semantic models, as well as the simulation and optimisation of engineering systems. Aside from enhancing the efficiency of the value chain, such information-intensive models and associated technologies play a decisive role in minimising the lifecycle impacts of our buildings. While Building Information Modelling provides procedures, technologies and data schemas enabling a standardised semantic representation of building components and systems, the concept of a Digital Twin conveys a more holistic socio-technical and process-oriented characterisation of the complex artefacts involved by leveraging the synchronicity of the cyber-physical bi-directional data flows. Moreover, BIM lacks semantic completeness in areas such as control systems, including sensor networks, social systems, and urban artefacts beyond the scope of buildings, thus requiring a holistic, scalable semantic approach that factors in dynamic data at different levels. The paper reviews the multi-faceted applications of BIM during the construction stage and highlights limits and requirements, paving the way to the concept of a Construction Digital Twin. A definition of such a concept is then given, described in terms of underpinning research themes, while elaborating on areas for future research.
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Cites
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Cited by
Pan, Y., & Zhang, L. (2021) · Automation in Construction · doi:10.1016/j.autcon.2020.103517 · Cited by 1,336
Digital twin application in the construction industry: A literature review
Opoku, D.-G. J., Perera, S., Osei-Kyei, R., & Rashidi, M. (2021) · Journal of Building Engineering · doi:10.1016/j.jobe.2021.102726 · Cited by 753
Construction with digital twin information systems
Sacks, R., Brilakis, I., Pikas, E., Xie, H. S., & Girolami, M. (2020) · Data-Centric Engineering · doi:10.1017/dce.2020.16 · Cited by 609
A BIM-data mining integrated digital twin framework for advanced project management
Pan, Y., & Zhang, L. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103564 · Cited by 518
Hosamo, H. H., Svennevig, P. R., Svidt, K., Han, D., & Nielsen, H. K. (2022) · Energy and Buildings · doi:10.1016/j.enbuild.2022.111988 · Cited by 322
Metadata
- Authors
- Boje, C., Guerriero, A., Kubicki, S., & Rezgui, Y.
- Venue
- Automation in Construction
- Publisher
- Elsevier BV
- Published
- 2020-06
- Volume
- 114
- Pages
- 103179
- ISSN
- 0926-5805 (ISSN-L)
- OpenAlex
- W3013120860
- Cited by
- 1,324
- References
- 8
- FWCI
- 72.11
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.