Non-Linear Corrosion Growth: A More Appropriate and Accurate Model for Predicting Corrosion Growth Rate
Bibliographic record
Abstract
External metal-loss corrosion is one of the major contributing factors for pipeline failures in North America. Corrosion growth rate plays a crucial role in managing corrosion hazard for gas and liquid pipelines. Quantifying the growth of corrosion over time is critically important for the risk and reliability analysis of pipelines, planning for corrosion mitigation and repair, and determination of time intervals for corrosion inspections. Conservatism in predicting the growth rate has significant engineering implication as non-conservatism can lead to critical anomalies being missed by mitigation actions and may cause pipeline failure; whereas, over conservatism can lead to unnecessary inspections and anomaly mitigations that may result in significant unnecessary cost to pipeline operators. As more and more pipelines are now being inspected by in-line inspection (ILI) tools on a regular basis, the ILI data from multiple inspections provide valuable information about the growth of corrosion anomalies on the pipeline. Although the application of linear growth rate calculated by comparing depths from two successive ILI is a common practice in the pipeline industry, research has shown that the growth of corrosion anomaly is non-linear and anomaly-specific. The authors of this paper have previously developed anomaly-specific non-linear corrosion growth model based on multiple ILI data. The objectives of this paper are to demonstrate the appropriateness of anomaly-specific non-linear corrosion growth model, and to illustrate the advantages of using non-linear corrosion growth model in the integrity management program. Two case studies were performed to illustrate the application of non-linear growth model by incorporating the measurement errors associated with the ILI tools, which include both the bias (constant and non-constant) and random scattering error. The findings of these case studies are presented in this paper.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".