Using AI To Reduce Water Loss And Enhance Pipe Reliability
By David Kushner
AI models give utility managers actionable insights into the health of their pipe networks, enabling a shift from reactive to predictive maintenance.
Water utilities across the U.S. face a growing infrastructure challenge. The average failure age for a water pipe is 53 years, and 33%1 of the water mains in the U.S. are over 50 years old. Water utilities find it challenging to identify which pipes are at risk of failure, because they lack the data needed for making informed decisions on pipe replacement. This makes it difficult to prioritize maintenance or replacement, resulting in decision-making based on often costly speculation instead of data-driven insights. However, advancements in artificial intelligence (AI) and machine learning (ML) are transforming how utilities can predict and prevent pipe failures and stay ahead of these infrastructure challenges.
The Growing Problem Of Aging Water Infrastructure
The U.S. alone has over 770,000 miles of aging water mains. The sheer scale of these systems makes it difficult for utilities to stay on top of maintenance and replacement schedules. Historically, utilities have used reactive approaches to managing pipe failures — responding to breaks and leaks after they occur. This method often results in high costs, water loss, and service disruptions, with pipes being replaced only after they fail.
Complicating matters further, the data available to most utilities making these decisions are incomplete. Utilities often rely on pipe age and material type when prioritizing which pipes to replace or repair, but these factors alone don’t provide actionable insight into whether the pipe is at the end of its useful life and whether other pipes should be prioritized for replacement. Environmental conditions such as soil type, temperature variations, and pressure loads, as well as historical failure patterns, are critical in determining which pipes are most at risk. Without accurate data across these variables, utilities are left to rely on institutional knowledge and gut feelings, potentially missing critical early warning signs of failure.
Enter AI And ML
AI and ML advancements are revolutionizing various industries, and water utilities are also experiencing this transformation. By leveraging these technologies, utilities are now able to combine historical data, real-time sensor readings, and environmental factors to gain deeper insights into the health of their pipe networks.
AI and ML models are adept at analyzing extensive data sets to identify patterns and connections that may be challenging, if not impossible, for humans to detect. For example, an AI model can analyze a combination of factors such as pipe age, material, historical failure rates, water pressure, and even weather patterns to predict which pipes are most likely to fail in the near future. These insights allow utilities to prioritize replacements and repairs based on actual risk rather than age alone.
In many cases, these AI-driven models rely on both structured and unstructured data. Structured data, such as pipe material and installation date, can be combined with unstructured data like maintenance records and work orders to create a comprehensive view of each pipe’s condition. By continuously learning and refining predictions over time, AI models help utilities make better decisions about where to allocate resources and when to intervene.
Moving From Reactive To Predictive Maintenance
The transition from reactive to predictive maintenance is one of the most significant benefits AI offers water utilities. Rather than waiting for a pipe to burst or leak, predictive maintenance strategies allow utilities to forecast problems before they become emergencies. This proactive shift can reduce water loss, lower repair costs, and improve service reliability.
Predictive maintenance depends on data analytics to continuously monitor the state of infrastructure in real time. For instance, pressure sensors installed throughout a distribution network can detect minute changes in water pressure that may indicate a developing leak. When combined with AI algorithms that analyze historical failure patterns, these real-time data inputs provide a powerful tool for identifying potential issues before they escalate.
Furthermore, predictive maintenance can extend the life of aging infrastructure. Instead of replacing pipes on a set schedule based on age alone, utilities can focus on preventive measures for pipes identified as high-risk by AI models. This approach not only reduces unnecessary replacements but also maximizes the lifespan of pipes that are still in good condition.
Reducing Costs And Enhancing Efficiency
Cost savings are one of the most immediate benefits of predictive maintenance. Repairing a pipe before it fails is generally much less expensive than dealing with the aftermath of a major burst. The cost of water loss, service interruptions, and emergency repairs can add up quickly, particularly in urban areas where large-scale infrastructure failures can disrupt entire neighborhoods.
AI can also help utilities optimize their maintenance budgets by enabling precise planning. Instead of allocating resources on pipes that may or may not need replacement, utilities can focus their efforts on high-risk areas, ensuring that repairs and replacements are carried out where they are needed most. This targeted approach reduces waste and helps utilities operate more efficiently.
In addition to lowering costs, predictive maintenance can improve the overall quality of service to customers. By preventing unexpected outages and reducing water loss, utilities can ensure a more consistent supply of clean water to their customers. This not only benefits consumers but also helps utilities meet regulatory requirements for water quality and conservation.
Improving Decision-Making With Data-Driven Insights
One of the key advantages of AI is its ability to provide data-driven insights that enhance decision-making at all levels of an organization. For water utilities, this means moving away from intuition-based decisions toward evidence-based strategies that maximize infrastructure performance and minimize risk.
AI models can offer utility managers clear, actionable insights on the pipes most susceptible to failure and the expected timing of these failures. With this information, decision-makers can better prioritize projects, ensuring timely completion of critical repairs and replacements. In addition, the transparency provided by AI models allows utilities to justify their decisions to regulators and stakeholders, helping to build trust and confidence in their infrastructure management strategies.
Looking Ahead: The Future Of AI In Water Utilities
The shift from reactive to predictive maintenance represents a critical opportunity for water utilities to improve their operations and serve their communities more effectively. Water utilities that embrace AI-based predictive maintenance strategies stand to benefit from reduced water loss, lower repair costs, and improved service reliability — all of which contribute to greater sustainability and operational efficiency. As the challenges of aging infrastructure continue to grow, adopting proactive solutions powered by AI will be essential for maintaining safe and reliable water systems for the future.
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About The Author
David Kushner has been with Itron for more than 20 years. Currently, in his role as director of global data management for water, electricity and gas, Kushner is committed to working with utilities and municipalities to reach resourcefulness and sustainability goals.