Age Is a Misunderstood Predictor of Pipe Failure Risk
Bibliographic record
Abstract
Pipe age may well be the most common factor used in making water main rehabilitation and replacement decisions. Data from the largest database of pipe failure records compiled to date suggest that it is often being used incorrectly, resulting in billions of dollars in unnecessary pipe replacement. Prior studies have shown a linear relationship between age and failure risk for the first few decades of pipe life. Many models assume that break rates will rise exponentially, despite a lack of published data. This study analyzes over 10,000,000 pipe-years of monitoring records from 6 utilities in 3 continents, providing sufficient data for the longer term relationship between age and break rate to become clear. The results are unexpected: for most pipe materials, failure rates reach a peak after a few decades, and then begin to decrease. This decrease is deep and prolonged for many pipe materials, offering stretches as long as 50 years where failure rates are lower than when the pipes were just a few decades old. These “golden years” extending beyond their design life may offer decades of unexpected useful life from existing pipelines.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".