Studying Stress Corrosion Cracking Crack Initiation in Pipeline Steels in a Near-Neutral pH Environment: The Role of Hydrotesting
Bibliographic record
Abstract
Hydrostatic testing, or hydrotesting, has been widely used as a stress corrosion cracking management method in the pipeline industry, particularly in gas pipelines. Although the technique has been very useful in the prevention of operational failures, it is known that these high pressures can produce significant plastic deformation around stress concentrators, such as pits and other surface flaws, that might be present. This plasticity can temporarily retard long, well-developed cracks; however, the effect of this plasticity on growth of very small cracks has not previously been studied. In this work, a long-term test was conducted to simulate real pipeline pressure cycling conditions by the application of occasional hydrotesting loads on steel samples. Crack initiations from pits were compared between specimens undergoing no hydrotesting load (control specimens) and those that underwent three hydrotest cycles during the test. The results showed that pit-to-crack transition was enhanced by the application of three hydrotesting loads. Seventy percent more cracks were found to have grown beyond ferrite grain boundaries in the hydrotested specimens. This initial study indicated substantial differences between small crack formation with and without hydrotesting. These differences predict significantly higher short crack growth in the hydrotested samples. Further study is necessary to further delineate these effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".