A novel model for prediction of burst capacity of corroded pipelines subjected to combined loads of bending moment and axial compression
Bibliographic record
Abstract
Most of the commonly used standards and codes for burst pressure prediction at a corrosion defect of steel pipelines generally just consider internal pressure alone. However, an actual oil/gas pipeline is usally subjected to external loads, such as axial compression/tension and bending moment, which may affect the burst capacity of the pipeline. In this study, a three-dimensional (3D) nonlinear finite element (FE) model validated by burst tests was developed to investigate the effect of bending moment and axial force on the burst capacity of corroded pipelines . Subsequently, the effects of external loads (i.e., bending moment, and axial force) and corrosion geometry features (involving corrosion depth , width, length, and clock position) on the pipe burst pressure were determined. Then, based on a series of FE cases, a new burst prediction model for corroded pipelines subjected to the combination loads of bending load and axial compressive force was fitted and developed. Finally, the effectiveness and reliability of the new proposed model were verified by extensive parametric FE analysis and burst test data. The results show that the prediction errors for the failure pressures between the proposed-model and the FEM were less than 10% for most 92.793% of the 222 cases. Moreover, the proposed model can also be applied to the condition that the pipeline is under single internal pressure, and its prediction accuracy is better than that of other seven well-known models of ASME B31G, Mod B31G (0.85 d L), Z662, DNV, PCORRC, CUP, and Shell-92.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".