Bibliographic record
Abstract
Abstract Dents are an integrity threat to oil and gas pipelines. Due to the higher perceived risk of cracking associated with dents on welds, the acceptability thresholds for dents interacting with welds are more stringent than that for plain dents in the pipe body. Similar to the depth based criteria, when curvature strain assessment is utilized to determine dent acceptability, the acceptability threshold for a plain dent on the pipe body and a dent interacting with a weld is 6% (material specific limits are also available) and 4%, respectively. When applying these screening criteria based on in-line inspection data, one practical challenge is to determine whether a dent is interacting with weld or not. Since weld interaction directly dictates the dent acceptability threshold, it affects repair decisions and hence may have a large impact on a pipeline operators’ budget and potentially pipeline safety. Overly conservative dent-weld interaction rules can cause unnecessary repair or investigation; whereas non-conservative dent-weld interaction rules may lead to injurious and non-compliant dents left in place unrepaired. This paper presents a study of the dent-weld interaction rules from strain assessment perspective. A strain assessment database with greater than 9,000 dents, detected and measured by ILI, was utilized in this study to investigate the potential of applying existing dent-weld interaction criteria for strain assessment purposes. Recommendations are made with respect to applying such criteria for dent screening based on ILI data.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".