Field Experience With a Model for Determining Hydrostatic Re-Test Intervals
Bibliographic record
Abstract
At IPC 2006, a model was described that provided a scientific basis for determining hydrostatic re-test intervals for SCC in gas pipelines.[1,2] The model involves determining the maximum possible crack growth rate based upon previous hydrostatic-test intervals and pressures. It resulted in intervals that initially are short and subsequently get longer and longer. Compared to uniform intervals, this sequence is predicted to result in an equivalent level of safety with fewer re-tests. Several pipeline companies have adopted the model, and, in general, the model has been successful. The 2006 paper pointed out that the model was applicable to ruptures but not leaks. In addition, the model did not consider two possible, but unlikely, conditions. One is the possibility that a coating defect could develop after the first hydrostatic test and a severe chemical environment might develop under the defective coating. This possibility has never been observed. The second is the possibility that two or more nearly co-linear sub-critical cracks could coalesce to form a critical size flaw. That would cause a discontinuous step in the growth curve, which is not consistent with the model. The one and only exception to the model that has been observed to date was of this nature. Since this latter condition can occur for cracks at the toe of a double-submerged arc weld under tented tape coating, a special re-test schedule has been devised for this condition. The original assumption of the model that the failure pressure of a growing crack varies linearly with time was verified from a fracture surface that had markings corresponding to the position of the crack front at various known times during the history of the pipeline.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".