The Effects of Soil Properties on the Fracture Speeds of Propagating Axial Cracks in Line Pipe Steels
Bibliographic record
Abstract
The ductile fracture resistance of newer line pipe steels is of concern for higher grade/strength steels and higher-pressure pipeline designs. Although there have been several attempts to make improved ductile fracture arrest models, the model that is still used most frequently is the Battelle Two-Curve Ductile Fracture Arrest Model, which incorporates the gas-decompression behavior with the fracture toughness of the pipe material to predict the minimum Charpy energy required for crack arrest. For this model, the effect of backfill on the propagating crack fracture speeds is lumped into one empirically based “backfill coefficient,” which does not distinguish different soil types or strengths. Some modifications to this backfill coefficient have been proposed for frozen soil as a function of moisture content, and for water backfill for offshore applications, but no attempt has been made to quantify the effects of soil type, total density or strength on the fracture speeds of propagating cracks in line pipe steels. This paper presents the results from a series of small diameter pipe burst tests that were conducted with different soil backfills. The soils’ moisture content, density, and strength were fully characterized in situ and in the laboratory. In addition, fracture speed data in both unbackfilled and backfilled conditions were recorded. The comparison of the change in fracture speed as a function of soil type, moisture and strength gives valuable insight into the effects of soil on the arrest of running ductile fractures in line pipe materials.
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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 | 0.001 | 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".