Dent Screening Criteria Based on Dent Restraint, Pipe Geometry and Operating Pressure
Bibliographic record
Abstract
Abstract A safety advisory (2010-01), issued by the National Energy Board (NEB) in June 2010, referenced two incidents which were a result of a fatigue crack failure that occurred within shallow dents [1]. The dents in both instances were less than 6% (of the OD). Currently, there is no consensus on how shallow dents or shallow dents with stress concentrators, as called by the ILI tool, are assessed and acted upon. BMT Canada Ltd. (BMT) was contracted by the Canadian Energy Pipeline Association (CEPA) to develop a definition for shallow dents, and two levels of screening method for the integrity assessment of shallow restrained dents and unrestrained dents. These two levels are known as CEPA Level 0 and CEPA Level 0.5 dent integrity assessment techniques that may be applied without finite element modelling or detailed calculations. The BMT dent assessment finite element (FE) modeling method was used to develop an extensive database of dents for different pipe geometries (OD/t), indenter shapes, pipe grades, and indentation depths. The results of the FE modelling were used to develop trends for the stress magnification factors (KM) across the range of pipes and dents modelled. These trends are used as the basis for the Level 0 and Level 0.5 dent screening and assessment approaches that can be used for both unrestrained dents and shallow restrained dents. The results show that for low OD/t pipe geometry and/or low spectrum severity indicator (SSI) [2] dent fatigue life may not pose an integrity threat. These dent screening approached have been adopted in the API Recommended Practice 1183 Dent Assessment and Management, that is currently under development.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".