Characteristics, Causes, and Management of Circumferential Stress-Corrosion Cracking
Bibliographic record
Abstract
While much more rare than axial stress-corrosion cracking (SCC), circumferential SCC (CSCC) has been observed in pipelines in Canada, the United States, and two European countries. In some cases, the CSCC has been of sufficient size to cause in-service leaks. Because the orientation of stress-corrosion cracks invariably is perpendicular to the maximum tensile stress, the axial stresses at the locations of the cracks must have been greater than the hoop stress. The Poisson effect and thermal effects can account for about half of the axial stresses. Evidence from the field suggests that there are three probable sources of additional axial stresses that can promote CSCC: residual stresses in bent pipe, axial stresses caused by movement of unstable soil on slopes, and residual stresses opposite rock dents. CSCC can be managed by one or a combination of the following procedures: direct assessment (DA), in-line inspection (ILI), or hydrostatic testing. Guidance for selection of sites for DA is derived from industry experience, which was determined from responses to a questionnaire and published reports. The capabilities of ILI to detect circumferential stress-corrosion cracks or the conditions that promote them are summarized. The benefits and limitations of hydrostatic testing also are described. A method for calculating the size of circumferential flaws that can cause ruptures is presented and compared with service experience. That information can provide useful guidance for ILI requirements and decisions about which flaws need to be removed immediately.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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 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".