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Simplified GIS for Water Pipeline Management

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Over the past decade, water pipeline operators have seen new inspection technologies and failure analysis techniques that enable them to determine the condition of their pipeline assets, the pipeline deterioration rate and probability of failure. Routine inspection and analysis of a pipeline system and related aboveground features creates the numerous datasets necessary to accurately and efficiently plan capital improvement projects to maintain the pipelines and associated assets in a sustainable manner. The quantity and diversity of this data can be a challenge to manage especially if one uses differing electronic file systems and standard paper files. Complexity increases further when engineers discover that the “real-world” conditions surrounding their pipelines have changed from that shown on their original “as-built” plans of construction. By deciding upon a GIS-enabled database, pipeline engineers can work towards an intuitive and dynamic system that provides users with timely information and analysis tools. Using GIS technologies effectively for a water pipeline requires a detailed data structure to address the jointing aspects specific to water pipelines. The data model needs to track individual pipe segments, from bell to spigot, with inspection and integrity information. Enabling this database for view through common software packages such as Microsoft Word, delivers GIS technology with a high value data set that is very accurate at representing existing water pipeline infrastructure but without expensive licensing fees and long learning curves. Using case studies, the paper illustrates how GIS technologies can leverage off a well designed pipeline data model and pipeline operators can fully realize the advantages of maintaining their pipeline assets in a spatial database including 1) increased data availability; 2) identification of urgent repair needs; and 3) improved maintenance and capital planning.
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Keywords: condition assessment; inspection data; pipeline data modeling; scalable; spatial analysis; user-friendly

Document Type: Research Article

Publication date: 01 January 2009

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