Possibilistic approach for consideration of uncertainties to estimate structural capacity of ageing cast iron water mains
Bibliographic record
Abstract
Drinking water distribution networks form essential components of all urban centres. Water mains buried in the soil-backfill are exposed to different deleterious reactions, with the result being that the design factor of safety may significantly degrade, leading to structural failure. In particular, metallic distribution and trunk mains are subject to corrosion. Proactive pipeline management, which entails timely maintenance, repair, and renovation, can increase the service life of pipes. Several nondestructive evaluation techniques have recently become available to measure the remaining wall thickness of metallic pipes. In this paper, a previously developed analytical model based on Winkler-type pipe–soil interaction (WPSI) is cast in a "possibilistic" framework to translate the remaining pipe wall thickness to current structural factor of safety. The WPSI model takes into consideration external (traffic, frost, etc.) and internal (operating and surge pressures) loads, temperature changes, and loss of bedding support and the reduction of pipe structural capacity in the presence of corrosion pits. Uncertainties associated with the input data–parameters are handled using fuzzy arithmetic operations and interpreted through possibility theory. A Monte Carlo type random sampling method is carried out for performing sensitivity analyses to identify the critical data-parameters that merit further investigation.Key words: water mains, pipe–soil interaction, uncertainty analysis, fuzzy arithmetic, possibility theory.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".