PR214-213800-R01 Evaluation of API RP 1183 Dent Fatigue Analyses using In-Service Dents Data
Bibliographic record
Abstract
The objective of this project is to validate the existing mechanical damage (MD) assessment and management tools based on in-service field dents data provided by pipeline operators. The data provided included in-line inspection (ILI), dent geometry data, operating pressure history data and in-ditch inspection observations. The dent fatigue life analysis tools were 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 and are included in API Recommended Practice (RP) 1183 (1). Since the assembly of API RP 1183, PRCI has continued its mechanical damage strategic research priority in the development of a greater understanding of the behavior of mechanical damage and the production of data to support engineering assessment. The research work included the following series of tasks: - Collect and collate mechanical damage field data provided by various pipeline operators. - Implement the fatigue life screening and assessment approaches using the provided dent geometry and pressure loading data. - Validate the fatigue life assessment results against the in-ditch inspection data. The data included presence of through wall cracks in dents resulting in leaks, location of surface cracks within dents and co-incident features like welds, corrosion, or gouges.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".