A Study of Cases of Hydrostatic Tests Where Multiple Test Failures Have Occurred
Bibliographic record
Abstract
The retesting of pipelines for integrity management purposes often involves testing of pipelines where multiple test failures can be expected. Multiple failures are most likely to occur when an existing pipeline is tested to a hoop stress level in excess of those used in prior tests of the pipeline. A major cause of such failures is seam manufacturing defects, but other types of defects such as mechanical damage or stress corrosion cracking may cause numerous failures as well. The occurrence of multiple failures can be costly in terms of the time the pipeline must remain out of service. Multiple failures sometimes involve pressure reversals that may affect confidence in the level of integrity sought by the pipeline operator. The study described in this paper involved a review of five actual cases of hydrostatic tests where multiple test failures occurred. On the basis of these cases a method was developed for predicting the ultimate number of failures required to reach a desired test level from the pressure levels of the first few failures. In addition, an improved method for estimating the probability of a pressure reversal of a given size was developed. Pipeline operators could use these techniques to decide when to terminate a hydrostatic test and to assess the effectiveness of the test in terms of a level of confidence that an integrity-threatening pressure reversal will not occur.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".