Pipe Diameter Is a Dominant Factor in Pipe Failure Risk
Bibliographic record
Abstract
Pipe diameter is a key factor in water main design. Decisions around what diameter to use are generally driven by hydraulic considerations, such as offering sufficient flows and residual pressures during fire suppression usage. Considerations of the relative failure risks of different diameters are rarely taken into account; however, this novel research finding suggests that diameter is a much more significant factor than was previously understood. Previous studies have noted that small diameter mains are responsible for more leaks than transmission mains, and that customer services suffer more leaks than either of the two. These observations have come through the professional observations of experienced engineers and from small scale studies. Other observations have noted potential errors in the theory underpinning the design standards for small diameter pipe. None of these have previously translated to a systematic examination of the relationship between diameter and failure risk. This paper presents a novel finding of an inverse-linear relationship between pipe diameter and failure risk per unit length of pipe. This finding is supported by the largest database of pipe failure records ever compiled, including over 10 million pipe-years of records from nine utilities across North America, Europe, and Asia. These records show a clear and strong negative correlation between diameter and failure risk across all pipe materials. When the inverse of diameter is considered, the relationship with failure risk appears to be linear for many materials. This relationship has two major implications. First and foremost, the fact that small diameter pipes fail as much as 100x as frequently as large diameter mains should be incorporated into risk considerations during pipe design and asset management decisions. Second, it offers a novel and useful normalization: failures per million pipe diameters of length per year. This normalization provides a more robust and consistent measure of failure risk than the industry norms of failures per 1,000 pipes per year or failures per 100 km (or 100 mi) per year. That in turn allows the actual failure risk posed by other factors (in particular pipe material, which often correlates with diameter) to be more clearly demonstrated, exposing some misconceptions about the relative risk of different pipe materials.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".