Critical Buckling Strain Equations for Energy Pipelines—A Parametric Study
Bibliographic record
Abstract
A recent review of available predictive critical buckling strain equations for segments of line pipe has shown that the equations give poor test-to-predicted ratios when validated using the more than 50 full-scale experimental pipeline test results available in the University of Alberta (U of A) database (Dorey, A. B., Murray, D. W., and Cheng, J. J. R., 2000, “An Experimental Comparison of Critical Buckling Strain Criteria,” Proceeding of the International Pipeline Conference, Calgary, Alberta, Oct. 1–5, ASME, New York, pp. 71–77, Paper No. IPC00-0157.). The pipeline specimens in the experimental database were subjected to a combination of axial load, internal pressure, and monotonically increasing curvature with magnitudes representative of those that might be experienced under field operating conditions. Research has been undertaken at the U of A to develop more reliable equations and a database of over 200 experimental and numerical results now exists. The numerical results were generated using a nonlinear finite element analysis (FEA) model that was validated using the experimental database. The FEA model provided a mean test-to-predicted ratio for the peak moment capacity of 1.025 with a coefficient of variation of 0.040 and a mean test-to-predicted ratio for the local critical buckling strain of 0.997 with a coefficient of variation of 0.067 (Dorey, A. B., Murray, D. W., and Cheng, J. J. R., 2005b, “A Comparison of Experimental and FEA Results for Segments of Line Pipe Under Combined Loads,” ASME J. Offshore Mech. Arct. Eng., in press.) for the 162 load cases analyzed. This paper presents the new predictive critical buckling strain equations developed from the U of A database.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".