PR-214-203806-R01 Improve Dent-Cracking Assessment Methods
Bibliographic record
Abstract
This work was funded in part, under the Department of Transportation, Pipeline and Hazardous Materials Safety Administration. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Pipeline and Hazardous Materials Safety Administration, the Department of Transportation, or the U.S. Government. This project builds on mechanical damage (MD) assessment and management tools, developed on behalf of Pipeline Research Council International (PRCI), Interstate Natural Gas Association of America (INGAA), Canadian Energy Pipeline Association (CEPA), American Petroleum Institute (API), other research organizations and individual pipeline operators and were included in API RP 1183. These include dent shape, restraint condition and interacting feature characterization; operational maximum and cyclic internal pressure characterization, screening tools defining non-injurious dent shapes based on pipe size and operating condition, failure pressure and fatigue assessment tools for dents with/without interacting features (e.g., corrosion, welds, gouges) in the restrained and unrestrained condition, and direction on available remedial action and repair techniques. In completing this development, areas for improvement were identified. The current project enhances previously developed tools being adopted in an industry recommended practice (API RP 1183) for pipeline MD integrity assessment and management considering: - Enhancement of indentation crack formation strain estimation, - Understanding the role of ILI measurement accuracy on dent integrity assessment, and - Quantification of assessment method conservatism to support safety factor definition. Safety factors (Modeling bias) defined in the present study and evaluated for different fatigue life estimation approaches in the present work refer to the conservatism inherent in different in different fatigue life models and is represented as the ratio of experimental lives to predicted lives.
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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 | 0.008 | 0.022 |
| 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 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.048 | 0.049 |
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".