Application of High-Grade Steels to Onshore Natural Gas Pipelines Using Reliability-Based Design Method
Bibliographic record
Abstract
Two example onshore gas pipelines were designed using a reliability-based approach. The first example (1219 mm, 17.2 MPa) represents a high-pressure large-diameter pipeline; the second example has a smaller diameter (762 mm) and lower pressure (9.9 MPa). Three steel grades (X70, X80 and X100) were used to develop three design solutions for each example. The wall thickness-related life cycle costs of the designs were evaluated. The design outcomes show that the reliability targets for both examples can be met using X100 steels and high equivalent design factors (0.93 for the first example and 0.9 for the second example). Moreover, ruptures and excessive plastic deformation of a defect free pipe were found to be insignificant integrity threats even when the design uses X100 and relatively high equivalent design factors such as 0.85 and 0.9. The economic assessment results show that the X100 design is the most economical option for the high-pressure large-diameter example. However, using X100 does not show a clear economic advantage over using X80 for the second example mainly because the wall thickness for the design using X100 is governed by the maximum D/t ratio constraint. The study also demonstrates the advantages of the reliability-based approach as a valuable tool in assessing the feasibility and potential benefits of using high-grade steels on a pipeline project.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".