Implementation and Validation of Reliability-Based Crack Assessment for Natural Gas Pipelines
Bibliographic record
Abstract
Abstract Traditional in-line inspection (ILI)-based crack management programs use deterministic methods, where the calculated failure pressure ratio (FPR) and ILI-reported crack depth are compared with their respective thresholds. In recent years, TC Energy has developed a probabilistic crack assessment method, where annual probability of small leak (POSL) and probability of failure (POF, i.e., probability of burst) are evaluated. The mitigation plan is then made by comparing the annual POSL and POF with their respective thresholds. The advantage of the probabilistic method over deterministic method is that the former portrays reality better by explicitly accounting for the uncertainties associated with pipeline geometric and material properties, ILI-reported crack sizes, crack growth and burst pressure models. This study demonstrates the safe implementation of the probabilistic assessment method for stress corrosion cracking (SCC) based on EMAT-reported ILI data and correlated dig data associated with three natural gas pipelines in Canada. Comprehensive validation was conducted by comparing the EMAT-based mitigation plan with the in-ditch assessment of a large set of dig data. Three key questions were addressed in the validation: (1) Does the probabilistic method capture all critical features identified in the field? (2) Whether the features avoided by the probabilistic method were unnecessary to excavate based on in-ditch assessment? (3) What is the benefit of the probabilistic method in comparison with the traditional deterministic method? The examination indicates that the developed probabilistic assessment process captures all the critical SCC features identified in the field, and the digs avoided by the probabilistic method are confirmed to be unnecessary in the field. The result demonstrates that the reliability-based method can reduce a significant number of unnecessary digs without compromising safety.
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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 | 0.005 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".