PhD ThesisThe growth of urban areas and their resource consumption presents a significant global
challenge. Existing utility resource supply systems are unresponsive, unreliable and costly.
There is a need to improve the configuration and management of the infrastructure networks
that carry these resources from source to consumer and this is best performed through analysis
of multi-scale, integrated digital representations. However, the real-world networks are
represented across different datasets that are underpinned by different data standards, practices
and assumptions, and are thus challenging to integrate.
Existing integration methods focus predominantly on achieving maximum information
retention through complex schema mappings and the development of new data standards, and
there is strong emphasis on reconciling differences in geometries. However, network topology
is of greatest importance for the analysis of utility networks and simulation of utility resource
flows because it is a representation of functional connectivity, and the derivation of this
topology does not require the preservation of full information detail. The most pressing
challenge is asserting the connectivity between the datasets that each represent subnetworks of
the entire end-to-end network system.
This project presents an approach to integration that makes use of abstracted digital
representations of electricity and water networks to infer inter-dataset network connectivity,
exploring what can be achieved by exploiting commonalities between existing datasets and data
standards to overcome their otherwise inhibiting disparities. The developed methods rely on the
use of graph representations, heuristics and spatial inference, and the results are assessed using
surveying techniques and statistical analysis of uncertainties. An algorithm developed for water
networks was able to correctly infer a building connection that was absent from source datasets.
The thesis concludes that several of the key use cases for integrated topological representation
of utility networks are partially satisfied through the methods presented, but that some
differences in data standardisation and best practice in the GIS and BIM domains prevent full
automation. The common and unique identification of real-world objects, agreement on a
shared concept vocabulary for the built environment, more accurate positioning of distribution
assets, consistent use of (and improved best practice for) georeferencing of BIM models and a
standardised numerical expression of data uncertainties are identified as points of development.Engineering and Physical Sciences Research Council
Ordnance Surve