Validation of Dent Fatigue Life Screening and Assessment Methods in API RP 1183
Bibliographic record
Abstract
Abstract Mechanical damage (MD) assessment and management tools have been developed on behalf of Pipeline Research Council International (PRCI), Interstate Natural Gas Association of America (INGAA), Canadian Energy Pipeline Association (CEPA), other research organizations and individual pipeline operators. Many of these tools are included in API Recommended Practice (RP) 1183. The current paper discusses the results of a study undertaken to validate the various dent fatigue life screening and assessment methods that were developed as part of various CEPA and PRCI projects, some of which have been incorporated into the first edition of API RP 1183. The work presented in this paper involved performing detailed analysis of data provided by pipeline operators for digs where mechanical damage features were identified by in-line inspection (ILI) systems and subsequently excavated and inspected in the ditch using non-destructive examination (NDE) methods. In total, data for 1320 dents were provided for review by pipeline operators based on their dent integrity management programs. More than 10% of these dents were found to have cracks during the in-ditch non-destructive inspection. Detailed dent geometry data, pressure data and in-ditch inspection reports were reviewed and analyzed to perform dent severity analysis. Factors such as pipe geometry, dent shape, dent restraint condition, effect of co-incident features and operational pressure cycle severity were considered to understand the strength of the relationship between various factors and the estimated dent fatigue life. The data was used to evaluate and validate the performance of the dent fatigue life screening and assessment methods developed as part of various CEPA and PRCI projects, some of which have been incorporated into the first edition of API RP 1183.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".