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Data assessment and utilization for improving asset management of small and medium size water utilities

University of British Columbia

Data regarding water main breaks are essential for undertaking informed and effective infrastructure asset management. This thesis reports on the findings of a survey regarding water main break data collection practices across North America and develops an approach for constructing databases and integrating the data with break prediction models to improve the asset management practices of a utility. The survey determines the amount and type of data collected by water utilities, the level of comfort with the amount of data collected and the availability of alternate sources of data. The responses provide insight into the strategies and data collection practices of small to mid-size utilities and show that the amount of data collected by utilities can be classified by the degree of data richness and defined as either an expanded, intermediate, limited or minimal data set. Utilities can implement recommended practices to increase the amount of data they collect, increase effectiveness of data collection and processing and consider additional sources of data for wager main breaks to improve their data sets. The thesis also introduces an approach for constructing a water main break and general network database that relates data from multiple sources to augment the amount of data available for asset management analysis while maintaining existing data warehousing practices. When used, managers may gain insight into current and future performance of the distribution network and develop future asset management strategies. The approach is flexible, uses commonly available software tools and anticipates the evolution of data collection, verification and storage capabilities within the utility. Finally, a framework is presented that guides small to medium water utilities in identifying key data to be used in asset management and pipe break prediction modeling and in selecting appropriate water main break prediction models. The framework may be used to identify the magnitude of a utility's pipe burst problems today and in the future, enhance the development of pipe replacement priorities based on forecasted breaks and identify key data to collect in future data acquisition programs. Water utilities with varying amounts of data can easily implement it with their existing data management and analysis tools.

Text

2011-02-10

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http://hdl.handle.net/2429/31184

Civil Engineering

University of British Columbia

Graduate