Systematic literature review of the application of extended finite element method in failure prediction of pipelines
Bibliographic record
Abstract
Ground movements caused by continuous freezing and thawing of the ground in arctic regions can potentially lead to pipeline failures. There are many factors such as internal pressure, pipeline geometry, pre-crack, corrosion, and pre-existing dents that can expedite the failure processes. There exist several methods to predict the failure in pipelines and study the abovementioned factors and their influences. These methods include experiments, analytical models, finite element method (FEM), and extended finite element method (XFEM). For predicting crack propagation in pipelines, XFEM has recently been proposed by researchers as the most efficient among the available methods. The purpose of our work is to conduct a systematic literature review of the available studies that attempted to use XFEM to predict failure due to crack propagation in pipelines and the effect of the abovementioned factors on failure. Articles are summarized according to the performed experiments, the pipeline material grade, failure material model, the investigated effect of various parameters on failure such as internal pressure or defect size, and methods for results verification. The number of articles in the literature using the XFEM for prediction of failure in pipelines was 23 to the best knowledge of the authors. However, in this systematic literature review, all these articles are categorized and investigated. The reviewed articles in general agree that XFEM simulations compare well with experiments, can accurately predict crack propagation in pipelines, and can be used efficiently to study the effect of various parameters on pipeline crack propagation for a wide range of materials.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".