Machine Learning for Pipe Condition Assessments

Abstract

Key Takeaways Utilities replace water mains by responding to failures or proactively choosing pipes likely to fail. Machine learning can find fragile pipes more accurately than using age or historical breaks as indicators. More accurate and often less expensive than other condition assessments, machine learning uses hundreds of variables to find patterns most people can't see. Timely selection of the right pipes to inspect, repair, or replace can reduce breaks and optimize the pipes’ remaining useful life.

Topics & Concepts