Integrity Planning and SCC Management in a Liquid Pipeline
Bibliographic record
Abstract
Stress corrosion cracking (SCC) is a major concern for many gas and oil pipeline operators. Extensive efforts continue to be made to develop strategies for a better management of the problem. Predictive models for stress corrosion crack growth were developed using lab testing data, limited inspection and excavation measurements since mid 70s and early 90s, respectively. In this paper, a systematic study of crack growth rates was conducted on the Imperial Oil Rainbow 24 NPS pipeline based on the two consecutive UltraScan Crack Detection (USCD) tool runs and field measurements. Findings of this study provide, perhaps, for the first time since the phenomenon was discovered, a direct measurement of crack growth rates for shallow cracks (in the category of <12.5%wt). Future integrity of the pipeline was assessed and the integrity management strategies were refined using the determined crack growth rate and fracture mechanics based approach. In addition, the susceptibility of SCC was studied in detail using a decision tree approach for data mining. Some important correlations between SCC susceptibility and environmental and mechanical variables were identified and presented. Findings on SCC susceptibility are discussed in terms of environmental and loading parameters such as soil, drainage, topography, pressure, and CP along the pipeline.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".