Performance Evaluation of Small-Diameter Cast Iron Mains and Their Role in Water Loss
Bibliographic record
Abstract
Small-diameter (equal or less than 4-in.) cast iron pipes generally do not have high consequences when they fail. However, many of those old small pipes are leaking or breaking at a much higher rate compared to large diameter mains, due to relatively thinner walls, corrosion, and other factors. In current practice, the risk assessment of water mains relies heavily on main break or leak records to determine the likelihood of failures. However, in many cases, leaks from small-diameter mains seep into the surrounding soil and never come up to the ground surface. This type of leakage could continue for a long time and cause big water loss problems before it is detected and fixed after a leak survey is performed. While conducting leak detection for all small-diameter mains is ideal, it could be cost-prohibitive if the water system is large. It is more practical to evaluate the performance of those small-diameter cast iron mains and replace pipes at the end of their life to avoid or reduce water loss. This paper presents a performance evaluation of small-diameter cast iron water mains using more than 35-year main break data. Field observations from actual pipe failures are conducted to validate the evaluation. Results from leak monitoring for areas with small cast iron mains are used to correlate with findings of the evaluation. The avoided water loss by implementing a proactive management plan for these small cast iron mains is calculated. The findings from this study could provide insights for other utilities with small-diameter cast iron mains.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".