Managing Rooftopping: An In-Line Inspection Based Approach
Bibliographic record
Abstract
Abstract Variation in longitudinal seam weld geometries can pose a challenge for liquid operator’s crack management programs. Both, radial misalignment and angular misalignment (also known as “peaking” or “rooftopping”) contribute to what is termed an “anomalous” seam weld geometry. These anomalous geometries create two challenges for an operator’s crack management program: they can reduce the fatigue life of the seam weld due to an increased stress concentration; and they create an unexpected geometry which challenges crack in-line inspection tools. The combination of these challenges can create a situation where a seam weld flaw can propagate at a higher rate without being detected by in-line inspection technologies, increasing the risk of failure. It is therefore prudent for operators to understand where they may have anomalous seam weld geometries and adjust their crack management plan accordingly. This paper outlines the approach taken by TC Energy to leverage existing datasets to quantify anomalous seam weld geometries, and more specifically rooftopping, on pipe following an incident. Several different in-line inspection technologies were investigated to quantify the presence of rooftopping, with various levels of success. One of the most promising approaches involved sensor stand-off data of an ultrasonic wall thickness measurement technology. The technology was initially pulled through a calibration spool in which rooftopping was artificially introduced. Through collaboration between the TC Energy and NDT Global, an algorithm was developed to not only detect, but also size the levels of rooftopping within the pipeline. The results of the excavation program found that the technology could adequately detect rooftopping. The absolute sizing of rooftopping is an area of focus for future improvements; however, the ability to detect rooftopping allows for a better understanding of the pipeline system and adds another level of data integration which TC Energy has used to improve their liquids crack management program.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".