Fracture assessment of post-buckled offshore pipeline using eXtended finite element method
Bibliographic record
Abstract
Shallowly buried offshore pipelines operating under high pressure and high-temperature conditions are susceptible to upheaval buckling. Such pipelines may contain pre-existing defects, including fabrication- or operation-induced cracks. If an offshore pipeline with an initial defect experiences vertical movement due to upheaval buckling, the crack can propagate in the tensile stress region, leading to fracture. This study presents a numerical modelling technique using an eXtended Finite Element Method (XFEM) to analyze the initiation and propagation of tensile fractures in a post-buckled pipeline. Conventional fracture mechanics commonly employ damage initiation criteria based on maximum principal stress (MAXPS) or maximum principal strain (MAXPE) with fixed values. However, these criteria have limitations when considering crack-tip constraints (stress triaxiality and Lode angle) during the numerical analysis. A modified Mohr-Coulomb (MMC) fracture criterion is implemented in the finite element program, Abaqus, using a user-defined subroutine to address this limitation. The MMC criterion considers shear slip and ductility, providing a more realistic representation of ductile materials than MAXPS and MAXPE models. This study also examines the influence of various fracture parameters under different damage degradation models. The findings provide practical insights for assessing crack initiation and propagation in post-buckled offshore pipelines.
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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 | 0.001 |
| 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".