Effect of Soil and Disbondment Configuration on CP Penetration into Coating Disbondment
Bibliographic record
Abstract
Abstract Tests were performed to investigate the effect of soil and disbondment configuration on CP penetration into coating disbondment. An innovative experimental setup was employed to measure the pH and potential of pipeline steel under a disbonded coating without disturbing the environment inside the disbondment. It was revealed that CP penetration was dependent on CP level, environment inside the disbondment, and distance from the holiday. CP penetration increased with increasing CP level, and decreased with increasing pH and distance from the holiday. For a circular FBE coating disbondment, the existence of clay soil in the solution acted as a physical barrier for OH- generated inside the disbondment to diffuse out, and lead to a very alkaline environment around the holiday area, i.e., pH 14. The presence of soil would cause an inhibiting effect to CP penetration. Majority of CP current was consumed at the holiday and vicinity, and only a very small portion of CP current could reach beyond 10 mm into the FBE and HPPC coating disbondment even at CP level of -1126 mVSCE. CP current could reach deeper into a narrow tunnel-shaped disbondment than into a circular-shaped disbondment with similar conditions, i.e., similar sizes of holiday and disbondment gap. Tunnel-shaped coating disbondment was subject to hydrogen accumulation inside the disbondment. In particular, HPPC coating disbondment had a higher tendency to trap hydrogen gas than FBE coating disbondment. The trapped hydrogen gas could effectively block the CP current from penetrating into the disbondment. The critical parameters required for the occurrence of near neutral pH SCC, i.e., near neutral pH and potentials close to Ecorr, could be achieved inside the tunnel-shaped HPPC coating disbondment under elevated CP levels of -926 mV and -1126 mV due to the presence of trapped hydrogen gas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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".