Development of Pipeline Sleeve End Fillet Weld Stress Intensity Factor and Reference Stress Solutions for Fatigue and Failure Assessment
Bibliographic record
Abstract
Pipeline defects such as cracks, dents and corrosion often require permanent pressure retaining repairs. Full encirclement metallic repair sleeves with fillet-welded end connections to the pipeline are often used for this purpose. In-service failures have occurred at pressure retaining sleeves as a result of defects associated with the sleeve welds, such as hydrogen-induced cracks, undercut at the fillet welds and inadequate weld size. At present, accurate quantitative fitness for service assessments for circumferential defects in a sleeve fillet welds are difficult to carry out due to a lack of detailed stress intensity factor (SIF) solutions for finite length cracks. The primary objective of the project presented in this paper [1] was to develop flaw acceptance criteria which will fill gaps in the available Engineering Critical Assessment procedures for metallic sleeve repairs on all grades of pipelines. SIF solutions for finite length sleeve-end fillet weld toe and root cracks were generated and used to develop parametric equations suitable for carrying out defect assessments. These equations can be used in the assessment of fatigue crack growth and/or fracture using failure assessment diagram (FAD) methods at sleeve end fillets alongside the results developed for other structural geometries in national standards. The equations were developed based on detailed finite element (FE) analyses of a wide range of sleeve end fillet weld cracking scenarios to estimate the SIFs at both the deepest point and the surface breaking point along the crack front.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".