Overcoming Technical Limitations in Identifying and Characterizing Critical Complex Corrosion
Bibliographic record
Abstract
In 2009 a pipeline within the TransCanada pipeline system experienced a rupture. As this pipeline was already under a rigorous In Line Inspection (ILI) based corrosion management program this failure led to an extensive root cause analysis. Even though the hazard causing the failure was microbiologically induced corrosion (MIC) under tape coating, the more troubling question was “Why had the severity of this anomaly not been determined by the ILI based corrosion management program?” This led to an investigation of what key characteristics of the ILI signals resulting from areas of “complex corrosion” are more difficult to correctly interpret and size and furthermore where the line condition is such that manual verification is needed. By better understanding the limitations of the technology, processes used, and the critical defect signal characteristics, criteria were developed to ensure that “areas of concern” are consistently identified, manually verified and therefore the sizing is validated at these potentially higher risk locations. These new criteria were applied on ILI data and then validated against in-the-ditch measurements and a hydrotest. This process in conjunction with optimization of ILI sizing algorithms enabled the operator to overcome some of the known challenges in sizing areas of complex corrosion and update its corrosion management process to improve the detection and remediation of critical defects. This paper describes this investigation of the failure location, development of the complex corrosion criteria, and the validation of effectiveness of the criteria. The criteria are focused on external corrosion and have been currently validated on pipelines of concern. Application to other lines should be similarly validated.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".