Dense and Sparse Stress Corrosion Crack Initiation in an X65 Pipeline Steel With Mill Scale
Bibliographic record
Abstract
Abstract Near-neutral pH stress corrosion cracking (SCC) is a significant threat to the operational safety and reliability of gas and oil pipelines. The SCC cracks are typically formed in colonies with different crack density populations on the external surface of the pipe. The density of SCC cracks affect how pipeline integrity and remaining lifetime are assessed. Although sparse and dense crack colonies are commonly observed on pipelines, it has not been well established how these crack colonies with different crack populations were developed in the field. This research was made in an attempt to replicate near-neutral pH SCC cracks with different crack densities in the laboratory with realistic loading conditions commonly found during field operation. An X65 pipeline steel with different surface preparations was used. The results showed that the dense near-neutral pH SCC cracks were successfully reproduced on the primer-coated samples, whereas sparse cracks were reproduced on the mill-scaled and polished samples. The densely spaced cracks could transform into sparsely spaced cracks when most of the primer layer and mill scale had been removed during the long period of corrosion under cyclic loading and further corrosion occurred thinning the crack density. The results of crack initiation obtained from this investigation have also been found to be quite consistent with crack initiation scenarios found during field operation.
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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 | 0.000 | 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".