Reinforcement of ice covers for transportation: beam and preliminary plate testing
Bibliographic record
Abstract
Winter road networks generally comprise segments that run over land and/or over floating ice expanses (rivers and lakes). The latter commonly are weak links in these operations, because they rely on cold temperatures to achieve a thickness that is safe enough for the intended traffic. These are thus vulnerable to warmer air temperatures. Risks of breakthroughs are also a serious consideration. One option is to increase the predictability of these over-ice segments as well as their ability to support a load and resist failure. This may be achieved if the ice cover is artificially reinforced. A laboratory study, building on past investigations by other research groups, was conducted to provide additional information on this topic and also to guide a follow-up plate testing program. Four-point beam bending tests were conducted on freshwater ice without and with reinforcement, for comparison purposes. Threaded steel rods, a steel mesh and a polypropylene geogrid were used as reinforcement material. In all tests, the load and the loading rate increases with time up to a peak load. For the non-reinforced ice, there is a sudden drop in load at which point the tests end. For the reinforced ice, a drop in load also follows the peak load, but the load climbs again up to another peak in a repetitive fashion, so as to produce a saw tooth pattern, extending to a significant amount of time. The ice reinforced with the threaded rods had the highest resistance (exceeding the load cell capacity). Thin section observations showed that the crystal structure was forming around the material. However, cleavage surfaces along the ice/material interface after beam failure indicate that the interface could be a strength reduction factor.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".