PR-214-183816-R01 Full-scale Fatigue Testing of Field Dents
Bibliographic record
Abstract
Significant effort has been spent in understanding dent fatigue behavior and developing engineering assessment tools for dent integrity management involving full-scale dent testing and numerical modeling through Pipeline Research Council International (PRCI), United States Department of Transportation (DOT) and Canadian Energy Pipeline Association (CEPA) sponsored research [1][2][3][4][5]. The results of the research work have been incorporated in American Petroleum Institute (API) recommended practice (RP), API RP 1183 [6]. The assessment tools have been validated and calibrated against full-scale dent fatigue tests. The experimental database of dent full-scale fatigue tests; however, consisted of dents created in the laboratory and the majority of these were created using dome shaped (semi-elliptical end caps) indenters. The current project scope was developed to address the specific gap between fabricated samples developed for full-scale test and real world samples, and to provide further validation of the dent fatigue life assessment methodologies incorporated in API RP 1183[6]. The field dents tested under the current project ranged in depth from 0.6 % to 11 % and included pipe samples with diameters ranging from 10" OD to 40" OD. The experimental data generated using former in-service pipeline samples was used to assess and validate the Level 2 and Level 3 dent fatigue assessment tools incorporated in API RP 1183 and support the improved management of mechanical damage so that dig programs can be better managed and the resources effectively utilized by the operating companies. Related webinar
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.069 | 0.031 |
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".