Moretti, N;
Xie, X;
Merino Garcia, J;
Chang, J;
Kumar Parlikad, A;
(2023)
Federated Data Modeling for Built Environment Digital Twins.
Journal of Computing in Civil Engineering
, 37
(4)
10.1061/JCCEE5.CPENG-4859.
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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.
Type: | Article |
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Title: | Federated Data Modeling for Built Environment Digital Twins |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1061/JCCEE5.CPENG-4859 |
Publisher version: | http://dx.doi.org/10.1061/JCCEE5.CPENG-4859 |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment |
URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10169376 |
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