The Development and Use of an Absolute Depth Size Specification in ILI-Based Crack Integrity Management of Pipelines
Bibliographic record
Abstract
To provide a more insightful and accurate feature description from Crack In-line Inspection (ILI) reporting as per the Fitness For Service analysis in API 1176, individual crack dimensions must be established to a given accuracy. PII Pipeline Solutions established an absolute depth sizing specification conforming to the dig verification processes of API 1163. This change represented a significant shift from a traditional reporting format for depth sizing in “bands” of 1–2 mm, 2–3 mm and > 3 mm depths within crack ILI inspection reporting. When assessing features with characteristics stated in a sizing band, the pipeline integrity assessment approach required a conservative assumptions that all of the features in that band must be treated as if they are in the deepest band value. The implication then meant that the specification created only 3 sizes of crack depths 1–2 mm, 2–3 mm, > 3 mm (± 0.5mm tolerance at 90% certainty). In practical terms a large quantity of features in the significant band of 2–3 mm must be treated as potential dig candidates with a depth of at least 3 mm, making length characteristics as the only severity ranking basis for any priority dig selection. Previous attempts at establishing absolute depth sizing for crack inspection required a series of calibration digs. The large sample size over multiple inspection runs and pipeline sections allowed for a statistical specification algorithm is developed as part of the analysis process, therefore no additional reporting time, or excavation cost was involved. The new absolute sizing algorithm has provided operators with a means of prioritizing digs, based upon individual feature length and depths. Replacing the traditional depth bands with individual feature specific peak depths and thereby providing a major step forward in achieving a cost effective process of prioritizing crack mitigation in pipelines. Following the dig verification process in API 1163, significant populations of infield NDE results were utilized on a variety of pipeline sections of different diameters. Predicted absolute depth estimation accuracy was determined for specific feature types and thereby created a depth tolerance, with statistical certainty levels established that match those available and recognized with metal loss ILI. This paper describes the process and the means by which an absolute depth crack ILI specification was established using characteristics from a significant set of real features. It also describes benefits realized within pipeline integrity engineering of moving to such a new reporting protocol.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".