Double Jointing Technology for Strain-Based Design (SBD) Pipelines
Bibliographic record
Abstract
Pipelines may experience significant longitudinal strains when subjected to large ground motions, such as seismic activity, landslides, etc. For these conditions, a strain-based design (SBD) approach can be used. The use of higher strength steels (like X80) for SBD approach can enable significant construction cost savings. Costs can be further reduced through the use of a double jointing process in order to reduce the amount of field welding. However, it is challenging to achieve adequate girth weld properties for SBD scenarios involving higher strength steels by using conventional double jointing processes such as submerged arc welding (SAW). Acicular ferrite interspersed in martensite (AFIM) has been previously identified as an advantageous high strength weld metal microstructure that can be applied in field pipeline construction. In this paper, a double jointing technology for X70+ SBD applications will be discussed. Excellent strength and toughness properties were achieved in double joint welds by using an optimized AFIM welding technology that included a tailored welding consumable wire and a high productivity GMAW-P weld process. Welding procedures are discussed along with mechanical properties achieved. Productivity comparisons suggest that a fully optimized GMAW-P welding process in the 1G-rolled welding position can have productivity comparable to a conventional SAW double jointing process.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".