Analysis and Design of Buried Pipelines for Ice Gouging Hazard: A Probabilistic Approach
Bibliographic record
Abstract
In cold environments, marine pipelines may be at risk from ice keels that gouge the seabed. Large quantities of material are displaced and soil deformations beneath a gouge may be substantial. To meet safety criteria, excessive strains are avoided by burying pipelines to a sufficient depth. In this paper, a probabilistic approach for the analysis and design of buried pipelines is outlined. Environmental actions are applied through distributions of gouge width, gouge depth, subgouge soil deformations, and bearing pressure. Three-dimensional pipe/soil interaction problem is modeled using nonlinear soil springs and special beam elements using the finite element method to estimate pipe response for statistically possible ranges of gouge depths, gouge widths, and burial depths. Relevant failure mechanisms have been considered, including local buckling and a variety of strain and stress based criteria. The methodology presented in the paper was developed and successfully used for several pipeline and electrical cable projects in ice gouge environments. Significantly reduced burial depth requirements have been demonstrated through the application of the probabilistic approach and through the use of strain-based design criteria. Because ice actions are applied through displacements of the soil, more ductile pipes are often necessary to meet reliability targets.
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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".