Infrastructure asset management programs should be focused on managing assets in a way in which the investment for each asset can be optimized producing overall cost savings in both operations and capital budgets. While there is a great amount of evidence of increasing water main breaks and further aging water infrastructure failures an approach looking at each stage of the asset's life cycle is important in reducing costs, allocating resources effectively, and providing a financial condition where the debt ratio can have a margin necessary to issue debt to support water pipe repair and replacement projects. In order to best take into consideration all of the aspects of determining the likelihood of failure as part of the maintenance and capital repair and replacement programs, machine learning can be used as a desktop condition assessment exercise to increase accuracy in predicting the segments of pipe needing additional investment.
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