Prediction of Long-Term Frost Heave of Chilled Gas Pipelines by Centrifuge Modeling
Bibliographic record
Abstract
The use of chilled gas transmission for northern pipelines has been considered an effective solution to reduce potential problems relating to permafrost preservation, as well as to provide other benefits such as increased throughput. However, the potential for frost heave as the pipe crosses areas of unfrozen ground may have implications on induced strains and therefore pipeline integrity. C-CORE, under the sponsorship of Pipeline Research Council International (PRCI), has been investigating the effects of frost heave for different soil types and varying pipe geometry and temperature. Previous studies of the effects of frost heave of chilled gas pipelines involved the construction of full-scale test sites, operated over a number of years and small scale laboratory tests. Recent advances in centrifuge testing techniques have allowed small-scale models to be constructed and tested under increased gravitational acceleration to replicate full-scale conditions. The major advantages are the reduced scale and time effects used in the modeling of the frost heave. This allows a number of sequential tests to be performed to study a range of soil types, pipe temperatures, groundwater and climatic conditions in a relatively short time period and at significantly reduced cost. Centrifuge modelling also incorporates other forms of soil deformations with development of ice lenses which includes the consolidation of unfrozen soil and deformation of both frozen and unfrozen soil. The tests performed to date have focused on the determination of frost heave behaviour for a number of soil types covering the range of frost susceptibility. This paper discusses the interpretation of the test results, and provides a comparison with common methods of analysis for prediction of frost heave. A design methodology is also proposed, which makes use of centrifuge test and analytical methods.
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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) | 0.003 | 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".