Initial Imperfection Models for Segments of Line Pipe
Bibliographic record
Abstract
Initial imperfections have long been acknowledged as having an effect on the behavior of shell structures, affecting both the global and local behavior. Yet, despite their significance, initial imperfections are rarely included in analytical models for pipelines. This is usually because of the complicated nature of initial imperfections, the difficulty in measuring them, and the small amount of available literature that describes their geometry. Some recent investigations at the University of Alberta in Edmonton have focused on the effect of initial imperfections on the behavior of segments of line pipe. Imperfections measured across the inside surface of pipe test specimens were incorporated into a finite element model that was developed to predict the experimental behavior of the specimens tested under combined loads of internal pressure, axial load and bending moment. Test-to-predicted ratios for the load carrying capacity of the test specimens had a mean value of 1.035 with a coefficient of variation of 0.047. The improvements in the accuracy of the finite element analysis models that include the initial imperfection pattern indicate their importance in modeling the experimental behavior. Once the importance of initial imperfections was established, idealized patterns were developed to simplify numerical modeling. This paper presents the results of different patterns investigated for both plain and girth-welded segments of line pipe and provides recommended simplified assumed initial imperfection patterns.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".