Assessment of Interaction Between a Dent and an Adjacent Corrosion Feature on Pipelines and the Effect on Pipeline Failure Pressure by Finite-Element Modeling
Bibliographic record
Abstract
Dents and corrosion are two types of defects commonly found on pipelines. Although major efforts have been made to assess the defects of each type, there is a limited understanding of interaction between the two defects in adjacency. In this work, a finite-element (FE) model was developed, enabling assessment of the interaction between a dent and an adjacent corrosion feature and prediction of failure pressure of the pipelines. Results showed that the geometries of the corrosion feature and the dent affected their interaction. As the interaction increased, the failure pressure of the pipelines decreased. A criterion was proposed to determine the critical spacing between the dent and the corrosion feature, below which an interaction between them existed. The dependences of the critical spacing on corrosion depth, corrosion length, and dent depth were determined. For example, the critical spacing between a dent 20 mm in depth and a corrosion feature 100 mm in length and 50% of pipe wall thickness on an X46 steel pipe was 150 mm. When a corrosion feature was sufficiently long (i.e., 200 mm), it dominated determination of the failure pressure, while the dent-corrosion interaction was negligible. When the corrosion feature was relatively short (i.e., 15 mm), the dent became predominant in failure pressure determination.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".