Numerical Studies of Pipeline Uplift Resistance in Frozen Ground
Bibliographic record
Abstract
A buried pipeline is subject to a variety of internal and external loads, one of which is the load induced by relative movement between the pipeline and the surrounding soils. Frost heave is one of the potential mechanisms that induce the relative movement for buried pipelines of chilled gas. The magnitude of the loads due to frost heave depends upon the amount of heaving and the load-displacement characteristics of the surrounding frozen soils, i.e., the uplift resistance of the soils. Under the sponsorship of Pipeline Research Council International (PRCI), laboratory uplift tests have been carried out to study the load-displacement characteristics of a frozen soil. In parallel, a series of laboratory geo-mechanical tests were conducted to define stiffness, tensile strain limits and time-dependent features of the frozen soil. A numerical model, using the geo-mechanical properties of the frozen soil as input parameters, has been developed. The numerical model is intended to be used as a tool primarily for sensitivity analyses and scaling of the results of the laboratory uplift tests to field operations, which are anticipated to have pipe diameters in a range of 5 to 10 times of the laboratory tests. A description of the numerical model is provided in the paper. The load-displacement relationships and failure mechanisms represented in the numerical model are compared with the measurements and observations made during the laboratory uplift tests (quantitative data on uplift resistance are considered proprietary and will not be presented, but detailed data may be obtained from technical publications of PRCI). After being calibrated, the numerical model can be used for sensitivity analyses, and also potentially used as a design tool for pipelines in discontinuous permafrost.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".