Press Coverage

august 2019 csengineermag.com 39 The Next Generation of Condition Assessments: Fast, Ac- curate and Cost Effective Traditionally, condition assessments of buried water mains typically fall into two categories: indirect and direct. An indirect desktop study method should always occur first. A direct or physical inspection and condition assessment are accurate for the pipe tested, but it tends to be slow, very expensive, and labor intensive. Multiple physical measure- ments are required for correlation and confirmation. The results are difficult to extrapolate to system-wide recommendations, which could be based on arbitrary assumptions and weights (i.e., older pipes are more in need of replacement than newer pipes). A performance-based buried infrastructure asset management approach involves a detailed inventory by pipeline segment and monitoring how well individual pipelines are meeting the level of service that is re- quired of them. Since buried infrastructure is not readily accessible, performance-based management of these buried assets has historically not been performed in the water industry. A more robust approach would be a large-scale comparison of various factors to generate a more refined and accurate prediction-based as- sessment on the disparate interactions between component variables. Machine Learning has emerged as a technology to make a significant impact in buried water infrastructure asset management. Machine Learning consumes large, complex data sets containing more variables than what humans can process with current tools. This objective, data-driven method overcomes human limitations with their inherent subjectivity and biases and provides more accurate results that help utilities make better replacement decisions. Due to the large amount of historical and geospatial data needed to run Machine Learning algorithms, water main condition assessments contain all the necessary components of an ideal application for water utilities. Pipe data and the surrounding environmental data covering in- stallation year, pipe material, break history, pressure class, geographi- cal location, elevation, pipe diameter, proximity to other infrastructure systems, and soil composition can all be taken into consideration while also assessing hundreds of other variables unique to a specific utility and pipe location. Consistently analyzing this data can uncover trends, gain insight on pipeline health, and offer data-driven assessments. New pipe data strengthens the predictive power of a Machine Learning algorithm. Machine Learning can also benefit utilities with a limited asset or breakage data by "filling in the gaps." Machine Learning can utilize many streams of data to perform certain predictions and begins to learn patterns that can inform situations where many of the common data points may not be available creating a new digital revolution in ad- vanced asset management practices. The more data a model contains, the more robust the model. As utilities are constantly collecting data such as new breaks and installed pipes, that data can continually be fed into a Machine Learning model. In February 2019, Fracta launched its next wave of capabilities. They couple its fast, accurate, and affordable LOF predictions with Con- sequence of Failure (COF) to calculate a monetized Business Risk Exposure (BRE) and an estimated replacement cost for every buried water main in a distribution system. Fracta COF determines the conse- quences, or severity, of the failure. Utilities can calculate the BRE in terms of risk ranking and direct and indirect costs. This approach gives an objective assessment and trans- lates the results into financial terms that water engineers, planners, and finance professionals can use to make fast, accurate and capital-effi- cient risk mitigation decisions about buried water main infrastructure. Incorporating a Machine Learning condition assessment like Fracta into a proper infrastructure and asset management program will enable utilities to meet the Modified Approach under GASB 34 for reporting the value of buried water mains. This will contribute to a more accurate accounting of the value of the assets. It also contributes to the reduc- tion 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 strat- egy so a utility will be in a better position to defend planning efforts and justify pipe replacement projects. DOUG HATLER is Chief Revenue Officer at Fracta. Doug Hatler

• Cover