Failure of Pressurized Corroded Pipeline Subject to Axial Compression: A Parametric Study
Bibliographic record
Abstract
Onshore buried steel pipelines are used for transporting oil and gas to various cities and locations. These pipelines can be subjected to various loading, such as axial, bending, shear, and other complex loading from the geotechnical movements and temperature variations. For example, a buried pipeline situated on an unstable slope can be subjected to axial load and axial deformation. In addition, this pipe experiences pressure loading from the fluids that it transports. The buried pipelines also need to endure corrosive environmental and as a result, corrosion occurs in these pipelines. Corrosion is the primary cause for structural failure of buried oil and gas pipelines and corrosion may lead to a catastrophic rupture failure causing environmental damage, injuries to human and animals, and loss of production and revenue. Hence, understanding the structural behavior and failure conditions of corroded pipelines is important for the pipeline operators. Therefore, this project was undertaken to determine the conditions required for failures of corroded steel pipes when subjected to axial deformation and internal pressures. Then the effect of internal pressure, dimensions of the corrosion, and depth of corrosion on the failure condition and failure mode was studied. It was found that the increasing value of these parameters is beneficial for achieving a favorable failure mode, however it can reduce the axial load carrying capacity significantly.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".