SCC Integrity Management for a Gas Pipeline Using a Combined Approach EW ILI, Calibration Excavation and FAD Analysis
Bibliographic record
Abstract
Gas pipeline operators face significant challenges with respect to quantifying and managing SCC in gas pipelines. Following SCCDA excavations, SCC was found on one of TCPL’s gas pipelines. A combined approach was then introduced to manage SCC, which consists of comparison of two consecutive Elastic Wave Inspection Runs prioritization of excavations, refinement of ILI tool sizing performance, and remediation using a Fracture Mechanics based FAD (Failure Assessment Diagram) methodology. The overall process from the ILI inspections to crack growth comparison, as well as integrity assessment and rehabilitation has demonstrated the effectiveness of the approach for SCC integrity management. In this paper, the history of the subjected pipeline segment is described. The concept of the approach is presented. The process of applying the approach to manage the pipeline integrity is outlined with examples for demonstration. The potential of utilizing this approach and process to other pipelines and crack detection ILI tools in gas pipelines in terms of POI, sizing, and excavation is discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".