Bibliographic record
Abstract
In addition to corrosion defects, dents are a common type of mechanical damage on pipelines, compromising the integrity and causing pipeline failures. Dents introduce a permanent plastic deformation on pipe body, resulting in significant stress and strain concentrations. While dents, especially constrained dents, do not decrease burst pressure of the pipelines, cracks usually initiate at the dents, especially under pressure fluctuations. Assessment of dents on pipelines is essential for determination of pipeline FFS. The chapter reviews the existing standards and codes used for dent assessment, describing the assessment principles, identifying limitations of the available methods, and imparting the improved strain determination for dent assessment. Various failure criteria for pipelines containing dents are summarized and their applicability is discussed. A new criterion based on modified strain determination at the dent is proposed. In addition, the combinations of a dent with other types of defects such as gouges, cracks, and corrosion are analyzed in terms of their impact on decreased burst strength of the pipelines and accurate assessment of the pipeline performance condition. In particular, the fatigue failure of dented pipelines is assessed with various levels of methods included in the standards. FE-based modeling for dent assessment on pipelines is introduced with detailed description of the modeling processes.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".