This session will demonstrate how Asset Management practices, when combined with the latest condition assessment tools using artificial intelligence (specifically machine learning) to assess the condition of buried water mains, provides a new method for aligning maintenance, repair and replacement strategies to better allocate limited resources.
Underground pipe performance evaluations can be established with an objective, data driven approach like machine learning and used to meet accounting’s GASB 34 Modified Approach requirement of a systemwide condition assessment every three years. This approach heavily reduces the time required by finance to report on buried infrastructure systems, while increasing the accuracy and value of the financial statements. Machine learning-based condition assessment tools are now commercially available.
It also contributes to the reduction of economic impacts incurred from water main breaks, and more efficient allocation of funding by water utilities. Use of best practices and a more accurate, objective tool will align maintenance and capital repair and replacement strategies to more efficiently leverage scarce financial and human resources. They also inject financial integrity and accountability to the planning process and refine the investment strategy so a utility will be in a better position to defend planning efforts and justify pipe replacement projects.
