Abstract:
The uninterrupted supply and reliable distribution of drinking water is fundamental
in a modern society; however, water pipelines are subject to a range of
operational and environmental factors which can lead to asset failure. For the
privatised water-sector in the UK, utility companies are moving towards the
deployment of statistical models for proactive asset management. For some
companies, statistical models have facilitated the migration away from static
annual burst targets, to targets which are dynamic and adjusted to observed
environmental conditions. There is an increasing need for the development of
accurate pipeline failure prediction models to support asset management and
regulatory reporting. This thesis evaluates several quantitative measures to
improve current methods of pipeline failure prediction. The aim of this thesis is to
establish the impact of soils, weather and trees on water infrastructure failure and
to develop a series of material-specific drinking water pipeline failure models for
an entire distribution network.
A quantitative assessment investigating the impact of data cleaning on the
attained model error of a series of previously developed models was conducted.
Material-specific variable selection and step-wise modelling approaches was
used to construct a series of improved statistical models, which have an
increased representation of the environmental factors leading to pipeline failure.
A detailed national tree inventory was investigated for its use in enhancing
pipeline failure predictions and for calculating failure rates of different pipe
materials under varying soil shrink swell and tree density conditions. The value in
creating separate winter and summer pipeline failure models was also evaluated,
to increase representation of the highly seasonal nature of pipeline failure. Finally,
a satellite approach was used to generate soil-related land surface deformation
measurements across a regional area was investigated. The result is a series of
enhanced statistical models for the prediction of water pipeline failure and a
greater understanding into the environmental drivers leading to asset failure.
