Full-Scale In-Situ Four-Point Beam Bending Field Tests on Sea Ice
Bibliographic record
Abstract
Abstract The flexural strength of ice is not a basic material property, but rather is an estimate of the maximum stress in the outermost fiber of an ice specimen when it fails in bending. Such conditions correspond to a number of important engineering applications, such as interactions between ice and a sloping structure or between ice and ships. Ice flexural strength is therefore highly important for calculating ice pressures and forces of interest for engineering design. While there has been considerable discussion in the literature regarding scale effects related to ice crushing against a vertical structure, scale effects in relation to bending failure have received much less attention. To this end, more flexural strength data for large, full-thickness sea ice beams are needed. To address these data gaps, a field data collection program was carried out in Pistolet Bay, Newfoundland over two field seasons (2017–2018). During this program, large sea ice beams were tested in-situ using a custom four-point bending apparatus, which was comprised of several main subsystems (e.g., the ram loading system, the platen, the ubrackets, and the hydraulic system). The sea ice beams were completely cut free from the ice cover and loaded at four points, such that the center load is parallel, but opposed to, the loads at the ends of the beam. All tests were done in-situ so that no brine drainage took place and the temperature gradient remained consistent. Tests were carried out for several combinations of beam geometry, which were scaled relative to the ice thickness. In addition to flexural strength, during the Pistolet Bay field program, the physical properties of the ice were measured (temperature, salinity, density). In this paper, a description of the field apparatus, test program and results from the full-thickness in-situ four-point beam bending tests are presented, along with a discussion of practical implications and future work.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".