Developing a Predictive Model of Near Neutral pH Stress Corrosion Cracking of Underground Pipelines
Bibliographic record
Abstract
Near neutral pH Stress Corrosion Cracking (NNpHSCC) associated with external corrosion of pipelines is an issue facing industry today. Determining areas of NNpHSCC susceptibility is crucial to developing Integrity Management Programs and inspection dig schedules. This research involved collecting pertinent field data (inspection dig reports, failure reports, loading histories) and developing a predictive model to help identify areas and lines most susceptible to NNpHSCC. The predictive model focused on the loading history (in this case, SCADA data) patterns to classify different groups of loading conditions. Hydrogen has been identified and established in previous literature to be a major contributor to NNpHSCC. Different Hydrogen Enhancement Factors (HEF) were applied based on how the mechanisms of hydrogen embrittlement react to the respective loading conditions. The predictive model illustrated a dormancy behaviour, similar to the one seen in field conditions and a mechanically activated growth dependent on both hydrogen and previous loading scenarios. A correlation was shown between a limited field sampling and the predicted values. Further improvements and calibrations can be made with the gathering of more field data and continued experimental validation. Once this validation has been performed, this model has the possibility to illustrate what loading conditions increase a segments susceptibility to NNpHSCC.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".