A Direct Assessment of Failure Pressure of High-Strength Steel Pipelines with Considerations of the Synergism of Corrosion Defects, Internal Pressure and Soil Strain
Bibliographic record
Abstract
Abstract In this work, a new, finite element analysis-based model, the CX model, was developed to investigate the effect of corrosion defect, internal pressure and soil-induced strain on the local stress distribution and corrosion reaction on pipelines. The relevant calculations and analysis were also conducted on three industry models. Results demonstrated the predicted failure pressures of various grades of pipelines by the industry models are conservative when small defects are present, while overestimation of failure pressure occurs with the increase of the steel grade and the corrosion depth. The prediction reliability decreases with the increasing corrosion depth and the steel grade. The geometry of corrosion defect affects remarkably the local stress distribution, and plays a critical role in the failure pressure prediction of pipelines. Furthermore, while elastic deformation affects the steel corrosion slightly, usually at an undetectable level, plastic deformation increases corrosion of the steel significantly. The CX model is capable of simulating the distributions of corrosion potential and corrosion current density at corrosion defect, and thus providing an essential method to predict the defect propagation.
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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 | 0.000 | 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".