Prediction of Tensile Strain Capacity of Spiral-Welded Pipes With Varying Initial Crack Sizes Using Extended Finite Element Method
Bibliographic record
Abstract
Abstract Structural integrity assessments are essential for the reliability and safety of energy transportation pipelines. Predicting the fracture resistance of pipes with flaws and defects generated during manufacturing and installation procedures is necessary for improving pipeline designs’ structural integrity. This study employs the Extended Finite Element Method (XFEM) to analyze mixed-mode fracture behavior and tensile strain capacity of API X70 spiral-welded pipe. Single-edge notched tension (SENT) specimens were used to calibrate XFEM parameters due to their fracture toughness similarity to full-scale spiral-welded pipes. The behavior of pipe under operational conditions was studied, focusing on mixed-mode fracture mechanisms specific to the helical geometry of spiral welds. In addition, the impact of initial flaw sizes on the tensile strain capacity and fracture behavior of spiral-welded pipes were evaluated. Results demonstrate that larger flaw sizes reduce fracture toughness and influence mixed-mode crack propagation, as reflected by the significant reduction in remote strain at failure (εfailure) for flaws with larger sizes. These findings provide valuable insights for improving pipeline design, assessing operational reliability, and enhancing structural integrity under real-world 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) | 0.001 | 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".