Abstract:
Water distribution networks are critical infrastructures, providing clean water to
millions of people. 3 billion litres of water are lost through pipe failure every day
in the UK, impacting serviceability. Statistical pipe failure models can reduce pipe
failures by providing valuable insights to enhance decision-making and promote
proactive management. This research aims to understand the complexity of pipe
failures in water distribution networks and develop a methodology for a reliable
pipe failure model that identifies the risk of failure. Through an embedded case
study and data-driven approach, several Objectives have been undertaken that
comprise the body of research delivered through several research papers.
This study offers several contributions to the immediate field of pipe failure
research. Firstly, the findings investigate new factors that form the various modes
and mechanisms of pipe failure, using alternative methods not commonly used in
pipe failure research are used, including Generalized Additive Model and
Dijkstra’s algorithm, and using data from a large UK water distribution network.
Secondly, the research develops a suitable methodology for predicting annual
pipe failures using an advanced machine learning method; a methodology that is
easily transferrable. Thirdly, the research provides a useful means of predicting
the risk of failure and visualising the results. Fourthly, the research investigates
the challenges of pipe failure models using a semi-structured interview approach
to review current practice. Finally, the research contributes by exploring several
different data-driven methods and an embedded case study design to contribute
to the broader context of pipe failure modelling.
The approach presented in this research provides a methodological framework
to enhance decision-making for asset management of pipes in clean water
networks. Furthermore, it highlights the main limitations, particularly data quality
and quantity, data-pre-processing, and model development, highlighting areas
for future progress.
