Pipeline Failure and Deterioration Models
Bibliographic record
Abstract
Abstract The utility managers and other authorities rely on location‐specific pipeline failure information to develop and implement long‐ and short‐term management plans. The pipeline failure or deterioration depends on multiple pipe dependent, time‐dependent, and pipe and time‐dependent factors. For this, substantial efforts have been made to develop pipeline failure or deterioration prediction models using different statistical models like regression‐based, survival analysis‐based, and Bayesian inference‐based models. The performance of these models depends significantly on the quantity and quality of available data. Most prediction models focus on larger utilities where sufficient amount of pipeline failure and other related information is available. Small and medium‐size utilities often suffer due to technical and financial resource shortages and data scarcity. It is essential to consider model uncertainties for pipeline failure or deterioration prediction model development. In this study, a brief review of existing regression‐based, survival analysis‐based, and Bayesian inference‐based pipeline failure prediction models are presented. To demonstrate the applicability of different pipeline failure and deterioration prediction models, the pipeline failure data of the City of Calgary is considered.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".