Learning from Multiple Corrosion Growth Rate (Run-Comparison) Studies
Bibliographic record
Abstract
Abstract Corrosion management using inline inspection is highly sensitive to the defect sizing accuracy and the also to the growth rates of these defects. In the pipeline industry corrosion growth has been measured using Run-comparisons. However many types of Run-comparison methodologies have been used in the past. Within the last decade or two Run-comparison techniques have evolved from box matching of defect samples to signal matching of the total defect populations. Theoretically some of the methods have been found to be superior. However in practice it is hard to validate this without multiple sets of Run-comparisons on the same pipeline. Multiple Run-comparison analyses have been performed on the TransCanada system to establish corrosion growth rates. Comparison of the results from these various analyses gives insight into the accuracy and uncertainty of each type of estimate. In an effective integrity management process the best available corrosion growth data should be used. To do so it is important to understand the conservatism and the uncertainty involved in each type of estimate. When using a Run-comparison to predict future growth it is assumed that the growth within the last ILI interval will continue (with associated uncertainty) during the next inspection interval. It is important to know if these assumptions hold in practice. The validity of this assumption is examined in this study. In the context of this paper these assumptions are validated for external corrosion on onshore pipelines. Characteristics of internal and offshore corrosion are very different in it space and time variation. Correlations of external corrosion growth in onshore pipelines with defect size and location are also examined. Learning from multiple corrosion growth studies gives insight into the actual corrosion rate variation along a pipeline as well as general growth characteristics. Different types of corrosion growth modeling for use in probabilistic integrity management programs are also discussed.
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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 | 0.024 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".