Estimating time to failure of ageing cast iron water mains under uncertainties
Bibliographic record
Abstract
Water distribution networks form essential components of water supply systems in most urban centres. Water mains buried in the soil/backfill are exposed to different deleterious reactions and as a result, their design factors of safety may significantly degrade with time, leading to structural failure. In most cases, a combination of circumstances leads to the failure of a pipe. Factors contributing to pipe failure include operational conditions, design parameters, external loads (traffic, frost, etc.), internal loads (operating and surge pressures), temperature changes, loss of bedding support, pipe properties and condition, and corrosion pit geometry. These are recorded rarely, if at all and it is therefore very difficult to ascertain the precise causes of failure. Even if all this information were available, any attempt to estimate the pipe condition state would involve considerable uncertainty due to large spatial and temporal variability that is inherent in this information. Estimation of time to failure is further exacerbated by the uncertainties in determining future corrosion rates. In this paper, corrosion models and a previously developed analytical model based on Winkler-type pipe-soil interaction are used to estimate time to failure. Since available data are insufficient to establish credible probability distributions, uncertainties in the input data/parameters are handled using possibility theory and fuzzy arithmetic. Sensitivity analyses are carried out to identify the critical data/parameters that merit further investigation.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".