Strain-based modeling of burst pressure in pipelines with selective seam weld corrosion
Bibliographic record
Abstract
A data-driven modeling process has been developed in this study to estimate the burst pressure of pipelines with surface wedge-shaped defects caused by selective seam weld corrosion (SSWC). The modeling of the pipeline burst pressure with SSWC was formulated through finite element analysis (FEA) conducted for different pipe attributes. Parametric studies for each pipe attribute were framed using the Buckingham π theorem, with burst pressure estimates derived by applying the Limiting Triaxial Strain criterion to the FEA stress and strain results. The model predictions were compared to failure pressure estimation results from standard methods used in the industry. A key finding from this research is the critical importance of incorporating the defect vertex radius into the data-driven burst pressure models. A sensitivity analysis was performed to assess how inaccuracies in vertex radius estimation affect predicted burst pressure, and a parametric method has been proposed to estimate the vertex radius using measurable defect parameters. Full-scale hydrostatic burst pressure tests were conducted and compared to the FEA predictions to validate the accuracy of the 3D FEA models and the limiting triaxial strain criterion of pipe failure.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".