The water supply system in South Australia is a complex network of 26,800 kilometres of pipe, including 9000 kilometres in Adelaide (capital city of South Australia) metropolitan area. The use of data analytics to understand the factors related to pipe failures can be used to inform decisions for a better asset management program, particularly for the renewal program with a balance between cost and risk. The use of preventive actions for effective control of pipe failure is one of the options to provide water services in a more reliable and affordable way to the customers. Usually, by checking the frequency of pipe failure can alert the water utility asset planners/managers of imminent failures in a region that should be considered as a priority action in the asset management system. Maintenance decision is made based on several factors such as previous failure history, customer feedback and restrictions in the network including low flow. The impact of a water pipe failure can be very costly to a water utility, it is not only the financial cost of repair/replacement but also the negative visibility and damage to its reputation. Despite the huge yearly investment, water mains failure is one of the significant issues in water industries’ infrastructure management. Asset management is an essential component of any water utility, which helps to understand where and when the investment is needed and how the budget should be allocated. Water utilities are asset-intensive organisations and as such, the assets need to be managed efficiently and effectively to achieve the required service levels at an affordable (lowest possible) price for the customers. This not only validated the methodology introduced by this research is capable to identify the high-risk pipe failure areas but also facilitated linkage between government and public agencies externally via their open access data which may complement their core services. The results confirmed SA Water’s asset management system is very effective in managing its linear assets with the areas focused on the water main replacement program matched well with the pipe failure locations. The results were compared with the SA Water’s renewal program to validate the methodology introduced by this research. soil type, monthly rainfall pattern, etc. Using Adelaide (South Australia) as the location of our case study, open source databases, such as pipe failures of SA Water (Twitter feeds), data from the Bureau of Meteorology, Adelaide Metropolitan soil data reports, etc., have been used to identify pipe failure locations, which was then correlated with the environmental factors e.g. The present study provides a proof-of-concept data collection methodology for water pipes failure from publicly available (open) data sources and identification of the common causes of water pipes failure in a water supply network, which may be automated with artificial intelligent (AI) methodology.
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