ABSTRACT
To reduce leakage and improve service levels, water companies are increasingly using statistical models of pipe failure using infrastructure, weather and environmental data. However, these models are often built by environmental data scientists with limited in-field experience of either fixing pipes or recording data about network failures. As infrastructure data can be inconsistent, incomplete and incorrect, this disconnect between model builders and field operatives can lead to logical errors in how datasets are interpreted and used to create predictive models. An improved understanding of pipe failure can facilitate improved selection of model inputs and the modelling approach. To enable data scientists to build more accurate predictive models of pipe failure, this paper summarises typical factors influencing failure for 5 common groups of materials for water pipes: 1) cast and spun iron, 2) ductile iron, 3) steel, 4) asbestos cement, 5) polyvinyl chloride (PVC) and 6) polyethylene (PE) pipes. With an improved understanding of why and how pipes fail, data scientists can avoid misunderstanding and misusing infrastructure and environmental data, and build more accurate models of infrastructure failure.
