Development of a Methodology to Predict the Failure of Large-Diameter Cast Iron Water Mains
Bibliographic record
Abstract
Many of the water mains in North America are legacy cast iron pipes. These are susceptible to corrosion, which accelerates the failure of these pipes as they age. A reactive approach to the rehabilitation of small-diameter water mains can be justified due to the relatively low consequences of their failure. However, a proactive approach should be taken for large-diameter pipes, which tend to have a lower rate of failure but higher consequences of failure. The aim of this paper is to present a new mechanistic model to aid in predicting the failure of large-diameter, gray cast iron water mains. In this new model, failure is assumed to occur due to a combination of corrosion pitting and hoop stresses from external and internal loads on the pipe. A fracture mechanics approach is used to account for the loss in strength of the pipe due to corrosion pitting. To account for uncertainty in the data collection and modeling processes, model inputs are treated as stochastic variables and the model is applied within a Monte Carlo simulation (MCS) framework. A deterministic sensitivity analysis was undertaken to determine the sensitivity of the factor of safety to key variables. The methodology was applied to a 24 in. nominal diameter gray cast iron water main for an exposure time of 300 years. MCS was used to generate 10,000 realizations of the water main factor of safety over the 300-year period. An empirical cumulative distribution function (CDF) was developed, and the interval in which 80% of the resulting factor of safety values occurred was determined for each exposure time. The preliminary results suggested that a 24 in. cast iron main under the loads considered is not expected to fail.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".