Abstract
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.
Highlights
The design and engineering of utility infrastructure is a complex task due to a multitude of constraints, the necessary involvement of diverse domain specialists and the need to represent information on multiple spatial scales (Borrmann et al, 2014)
Returning to the example of a hospital, its Building Information Modelling (BIM) MEP model will likely indicate exactly where water will arrive – the utility provider can use this information to ensure that a node of high topological redun dancy is situated close to this entry point, reducing the engineering required to connect at this point; for an existing water distribution network (WDN), a new hospital’s internal network can be configured to ensure that the facility will draw water from a side of the building that is close to an existing node of high redundancy – designing for resilience must be considered in the context of financial budgets and an appropriate trade-off has to be found between reliability and cost (Oliker & Ostfeld, 2013)
The algorithm infers plausibility of existing connection using BIM data, a modified version of the algorithm could support feasibility testing of potential future connections: if routing redundancy can be increased by ensuring that a critical facility connects at a specific node in the network, competing BIM layouts pose different engineering challenges to realis ing this – for example, it might not be sensible for a hospital’s mains water network to accept water on the side that is farthest from a highredundancy WDN connection point, and this paper shows how such analytics can be semi-automated
Summary
The design and engineering of utility infrastructure is a complex task due to a multitude of constraints, the necessary involvement of diverse domain specialists and the need to represent information on multiple spatial scales (Borrmann et al, 2014). The scale of the building envelope lies at the boundary of the geospatial and BIM domains, the data stan dards of which are heterogeneous (Gilbert et al, 2020) This complexity and heterogeneity pose a challenge to understanding the connectivity of utility infrastructure networks between the internals of buildings and their surrounding urban area. The complete demand-supply network spans multiple spatial scales, from reservoirs that feed regions down to consumer units in buildings. This change in spatial scale and asset ownership belies the real-world con tinuity of resource flow across this interface but is reflected in the lack of integration of the digital representations of the subnetworks.
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