Likelihood Of Failure: Machine Learning Pipe Failure Prediction

Ranking replacements by age, material, or break history sends crews to the oldest pipe, which is not always the weakest. This machine learning approach scores every segment in a distribution network against more than 120 variables, returning failure probabilities at one, two, three, and five years. The model is trained on over 560,000 recorded breaks across 200,000+ miles of pipe, then calibrated to a utility's own failure record rather than an industry average. Each segment also gets a percentile rank and its top five risk drivers, with no new sensors or excavation. Review the inputs, outputs, and platform views to see how the scoring works.