Bibliographic record
Abstract
Many gas pipeline operators use hydrotesting as a means of managing stress corrosion cracking in pipelines. Historically the interval at which the pipelines are hydrotested is largely empirical. If for instance a failure occurred on a pipeline section four years after a hydrotest, then four years would become the baseline interval. As failures are rare, these worst-case intervals are often applied across the system with little consideration to the many factors that would affect the relevant interval for different pipelines. This can result in an overly conservative frequency of hydrotests. In IPC 2006 an analytical method of determining hydrotest intervals, based on a time vs. pressure plot, was developed and published by Fessler et al. The study conducted herein examines this method, its assumptions, applicability, and limitations, with regards to an in-service pipeline system. It also discusses how this method was adapted to account for variable crack growth rates and failures. Application of this adapted method to the pipeline system and its results are discussed. It was found that this method could be used to predict hydrotest intervals for in-service pipelines. An additional method of using crack assessments with certain growth characteristics to develop hydrotest intervals is also presented. This method incorporated crack failure prediction calculations, and estimated crack growth rates, to predict hydrotest intervals. The results and the limitations are critically examined. Practical ways of improving the methods and ongoing work to improve the method are also explained.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".