Bibliographic record
Abstract
Much research has been performed over the past twenty-five years to refine our basic understanding of tubular stability, which includes bifurcation, imperfect systems, factors influencing tubular stability and post-buckling behaviour. Tubular instability resulting from load combinations is not a trivial topic, particularly when inelastic material behaviour occurs. Many influencing factors must be considered when attempting to understand (and predict) the onset of instability. Many existing collapse predictive methods are either simplistic or involve advanced plasticity or finite element methods. Simplistic methods are typically semi-empirical, and contain a degree of uncertainty resulting in conservative collapse predictions. Nonetheless, they are generally considered satisfactory for design purposes. Advanced methods normally involve high-end calculations using specialized software programs that might not be available for general use. Therefore, a relatively easy-to-use method that accurately predicts the actual collapse resistance is, in many cases, the most desirable option. This paper presents a collapse predictive methodology, developed from a variety of research projects performed over the last fifteen years. The prediction method, which can easily be entered into a spreadsheet program, is applicable to most forms of tubular members, including pipelines. Applicable load combinations include external pressure, axial tension and bending. An overview of the parameters influencing collapse resistance is also provided, including manufacturing history, material modelling, and tubular geometry and imperfections. Also presented is a summary of accuracy of the method to predict some test results. The test database largely contains results of collapse tests on tubular members subject to only external pressure, and axial tension with external pressure. The adaptation of the method to include external pressure with bending is summarized, and the accuracy of the prediction method is demonstrated by predicting the results of the Oman-India and Blue Stream pipeline collapse test programs, and comparing these predictions with those of other well known methodologies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".