Mechanical Properties of Cold Bend Pipes
Bibliographic record
Abstract
Cold bends are frequently required in energy pipelines in order to change the vertical and horizontal orientations of the pipeline route. They are produced by plastically bending a pipe joint in a cold bending machine by creating a series of uniformly spaced incremental bends. This procedure acts to reduce the moment capacity and buckling strain of the pipe, and studying the changes in pipe properties caused by cold bending is valuable in assessing the level of this strength reduction. Accordingly, the initial imperfections and material transformations of five full-scale cold bend pipes were assessed in this research program. The imperfections were measured at several locations around the circumference of the specimens, along the entire bend length. It was determined that the distribution of imperfections was similar in shape to a sine function, and their amplitude ranged from 0.3mmto1.0mm. Tension coupon tests were conducted on the intrados, extrados, and virgin materials of the specimens. It was revealed that the extrados material exhibited an increase in yield strength due to work hardening and that the intrados material demonstrated a reduction in yield strength due to the Bauschinger effect. It was established that the imperfections, and material transformations in the specimens were predominantly unaffected by the incremental-bend magnitude or spacing that was employed during the cold bending procedure.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".