Abstract
The digital twin (DT) approach is an enabler for data-driven decision making in architecture, engineering, construction, and operations. Various open data models that can potentially support the DT developments, at different scales and application domains, can be found in the literature. However, many implementations are based on organization-specific information management processes and proprietary data models, hindering interoperability. This article presents the process and information management approaches developed to generate a federated open data model supporting DT applications. The business process modeling notation and transaction and interaction modeling techniques are applied to formalize the federated DT data modeling framework, organized in three main phases: requirements definition, federation, validation and improvement. The proposed framework is developed adopting the cross-disciplinary and multiscale principles. A validation on the development of the federated building-level DT data model for the West Cambridge Campus DT research facility is conducted. The federated data model is used to enable DT-based asset management applications at the building and built environment levels.
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Data Availability Statement
Some data models used during the study are available in a repository or online in accordance with funder data retention policies. The Industry Foundation Classes data model can be found at https://standards.buildingsmart.org/IFC/RELEASE/IFC4/ADD2_TC1/HTML/. The BrickSchema can be found at https://brickschema.org/. The description of the Crate data model can be found in Brazauskas et al. (2021). The IFC processing approach is described in Moretti et al. (2020).
Acknowledgments
This research forms part of the Centre for Digital Built Britain’s (CDBB) work at the University of Cambridge within the Construction Innovation Hub (Grant No. NMZM/429). The Construction Innovation Hub is funded by UK Research and Innovation through the Industrial Strategy Fund.
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Journal of Computing in Civil Engineering
Volume 37 • Issue 4 • July 2023
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© 2023 American Society of Civil Engineers.
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Received: Feb 15, 2022
Accepted: Dec 16, 2022
Published online: Apr 7, 2023
Published in print: Jul 1, 2023
Discussion open until: Sep 7, 2023
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