Bibliographic record
Abstract
Buckles and the associated wrinkles are another type of geometric anomalies present on pipelines, resulting from manufacturing such as cold bending of pipe segments or from pipeline construction such as loss of stability during pipe laying. Buckling failures often occur on buried pipelines, especially when the pipelines contain local damages such as dents or corrosion defects, under a bending moment and/or a compressive loading resulting from anormal operating conditions such as elevated temperature and pressure and/or pipe-soil interactions. Assessment of pipeline buckling for determination of buckling resistance, i.e., critical bending moment or critical compressive loading, under given conditions is critical to pipeline integrity maintenance. The FE-based models are developed to evaluate buckling of steel pipes containing either a dent or a corrosion defect while considering the parametric effects such as steel grade, internal pressure, compressive load/bending moment, and the defect dimension (length, width, and depth). The buckling resistance of the pipelines is defined based on the critical compressive loading or critical bending moment. Moreover, a new method is proposed to evaluate the burst capacity of corroded or dented pipeline under the axial compressive loading and bending moment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".