Evaluation of New In-Ditch Methods for Measurement and Assessment of External Corrosion
Bibliographic record
Abstract
New technologies for in-ditch non-destructive evaluation were lately developed and are becoming of mainstream use in the evaluation of external corrosion features for both In-Line-Inspection performance evaluation and pipeline integrity assessment. However, doubt was cast about the reliability and repeatability of these new technologies (hardware and processing software) when compared with those used in the traditional external-corrosion in-ditch measurement and the reliability of the pipeline integrity assessment calculations (PBurst) embedded in their software when compared with industry-wide accepted calculation methods. Therefore, the primary objective of this study is to evaluate the variation and repeatability of the measurements produced by these new technologies in corrosion feature profiling and associated PBurst calculations. Two new 3D scanning systems were used for the evaluation of two pipe samples removed from service which contain complex external corrosion features in laboratory. The reliability of the 3D scanning system in measuring corrosion profiles was evaluated against traditional profile gage data. In addition, the associated burst pressures reported by the systems were compared with results obtained using industry-widely used calculation methods. Also, consistencies, errors and gaps in results were identified. In this paper, the approach used for this study is described first, the evaluation results are then presented and finally the findings and their implications are 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".