Assessment of Tensile Strain Capacity of Girth Welds in X70 and X100 Pipeline With Surface Cracks Under Bending
Bibliographic record
Abstract
Abstract This paper presents a detailed analysis of the tensile strain capacity (TSC) in X70 and X100 girth weld pipelines, each featuring external semi-elliptical surface cracks in the heat-affected zone (HAZ), under remote bending conditions. The objective of this study is to explore the effects of internal pressure, and HAZ softening levels on TSC for both steel types. First, comprehensive finite element analyses were performed to assess the crack driving force, quantified by the J-integral. The relationships between the crack driving forces and the applied remote strains on the pipeline under bending were established. Then, TSC were obtained using both crack initiation and ductile tearing criteria. Results indicate that the differences in TSC between the initiation and ductile tearing criteria are minimal, suggesting that the more convenient initiation criteria could be sufficient for practical assessments of TSC. The study then further demonstrates that increased internal pressures and HAZ softening reduce TSC. Comparisons of the TSC pipeline welds, obtained previously for remote tension loading, were also performed. The current results emphasize the significant impact of the factors on the structural integrity and safety of pipeline systems, highlighting opportunities to refine design practices and advance research into pipeline performance under complex loading conditions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".