Benchmarking Your Break Rates: How Do Water Main Break Rate Studies Compare to AI/Machine Learning Findings?

Over 54,000 community water systems face the inevitable cost of pipe repair and replacement. The United States EPA states that nearly 60% of the total system costs are found in the distribution and transmission pipe lines. Infrastructure asset management is an approach which can help utilities bring together the concepts, tools, and techniques to manage assets at an acceptable service level at the lowest life-cycle cost. The water industry has seen many types of academic surveys and studies on water main replacement programs and the benefits of asset management and prioritization. However, many utilities have not historically tracked all of the elements of water main break data. Over the past 20 years, most utilities have come to realize the importance of tracking all aspects of their infrastructure in a GIS system and have records of what kinds, sizes, and repair history of all pipes. As this trend continues, more data and analysis will be available to the industry to improve water distribution system repair and replacement decision making. One factor used to quantify the occurrences of failing underground pipe networks is water main break rates. Water main break rates are calculated for all pipe materials used in water delivery to create a measurement to judge pipe performance and durability. Water main break rates can vary year to year and by utility. However, in aggregate, break rates produce a compelling story which can aid our prudent decision making as it relates to repairing and replacing our underground pipes. This presentation reviews the newest water main break study metrics and findings including comparisons between typical surveyed data to actual AI/Machine Learning findings which can also help benchmark an organizations' break rates.