Bending Capacity of Corroded Pipeline Subjected to Internal Pressure and Axial Loadings
Bibliographic record
Abstract
Offshore pipelines operating in a harsh environment are usually subjected to combinations of bending moment and axial loadings in addition to internal pressure. Due to the corrosive media transported in the pipelines and corrosive substances within seawater and soil outside the pipelines, local corrosion defects will generate on the pipeline’s inner and outer walls, reducing its ultimate bearing capacity. This paper presents a series of full-scale failure tests and nonlinear finite element analysis (FEA) to study the bending capacity and failure mode of corroded pipelines with outside locally-thinned-areas (LTAs) subjected to combinations of internal pressure, axial compressive force and bending moment. The LTAs are loaded in compression to simulate corrosion. Material tests of API 5L X56 seamless pipe steel were conducted and the stress-strain relationship was obtained. FEA results of the moment versus curvature relation, bending capacity and local buckling behavior of each specimen model matched the experimental results very well, validating the accuracy of this simulation. Additional FEA is then performed to investigate the effect of corrosion geometric parameters, such as corrosion depth, corrosion width, and corrosion length, on the ultimate moment. Among them, the width is of the greatest impact, followed by is the depth, the length impact can be ignored.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".