An analysis of long-term pile load tests in permafrost from the Short Range Radar site foundations
Bibliographic record
Abstract
During the installation of the Short Range Radar (SRR) facilities at 35 sites across the Canadian Arctic, in excess of 7 000 piles were installed for the SRR foundations. A total of 137 pile load tests were performed during the SRR construction. Only a small number of these were installed external to the foundations and were loaded well in excess of design loads to attempt to determine their ultimate load carrying capacity. Only eight of these piles were loaded for extended durations (>12 h). This paper presents the results of these long-term load tests and compares the results with design guidelines based on allowable pile deformations and creep in ice-rich, saline permafrost. In nonsaline permafrost, at high normalized displacement rates (>30 year1) the stresses on the piles at failure were less than those predicted by design guidelines for piles in ice-rich soil. At lower normalized displacement rates (<15 year1) the failure stresses on the piles were reasonably well predicted by the design guidelines. Significant reductions in pile capacity were observed in ice-rich, saline permafrost. Without detailed knowledge of the unfrozen water content in the soil, however, prediction of the behaviour of the piles could only be bounded by current design guidelines.Key Words: permafrost, field pile testing, cold-temperature grout, long-term creep.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".