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Lookup NU author(s): Tom Gilbert, Professor Philip James, Dr Luke Smith, Professor Stuart Barr
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2020. Utility networks comprise a fundamental part of our complex urban systems and the integration of digital representations of these networks across multiple spatial scales can be used to help address priority challenges. Deteriorating water utility infrastructure and low routing redundancy result in network fragility and thus supply outages when assets fail. Water distribution network configurations can be optimised for higher resilience but digital representations of the networks used for simulations and analyses are not integrated with the finer scale networks inside buildings. This integration is hindered by differences in conceptualisation and semantics employed by the relevant data standards. We suggest that the geospatial and geometric data contained in Building Information Modelling (BIM) and water distribution network (WDN) models can be used for their integration; and that this supports the use cases of optimising dynamic network partitioning, reducing the risk of underground utility strikes and planning for future network configurations with higher topological redundancy. In this study, we develop and demonstrate the application of a weight-based spatial algorithm for inferring water network connections between urban-scale WDNs and BIM models, showing that spatial data can be used in the absence of complete or consistent semantic representations. We suggest that the method has potential for transferability to infrastructure for other utility resources (such as waste water, electricity and gas) and make recommendations such as standardising the representation of connection points between disjoint utility network models and extending the normal practical spatial remit of BIM MEP modelling to encompass the space between buildings and WDNs.
Author(s): Gilbert T, James P, Smith L, Barr S, Morley J
Publication type: Article
Publication status: Published
Journal: Computers, Environment and Urban Systems
Year: 2021
Volume: 86
Print publication date: 01/03/2021
Online publication date: 16/12/2020
Acceptance date: 18/11/2020
Date deposited: 26/07/2021
ISSN (print): 0198-9715
ISSN (electronic): 1873-7587
Publisher: Elsevier Ltd
URL: https://doi.org/10.1016/j.compenvurbsys.2020.101570
DOI: 10.1016/j.compenvurbsys.2020.101570
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