A Probabilistic High-Pressure Zone Model for Local and Global Loads During Ice-Structure Interactions
Bibliographic record
Abstract
For temperate ice regions, guidance provided by current design codes regarding ice load estimation for thin ice is unclear, particularly for local pressure estimation. This is in part due to the broader issue of having different recommended approaches for estimating local, global, and dynamic ice loads during level ice interactions with a given structure based on region, scenario type, and a variety of other conditions. It is essential from a design perspective that these three scenarios each be evaluated using appropriate definitions for local design areas, global interaction area, and other structural details. However, the need for use of different modeling approaches for ice loads associated with each of these scenarios is not based on ice mechanics but rather has largely evolved as a result of complexities in developing physics-based models of ice failure in combination with the need to achieve safe designs in the face of limited full-scale data and the need for implementation in a probabilistic framework that can be used for risk-based design assessments. During a given interaction, the ice is the same regardless of the design task at hand. In this paper, a new approach is proposed based on a probabilistic framework for modeling loads from individual high-pressure zones acting on local and global areas. The analysis presented herein considers the case of thin, first-year sea ice interacting with a bottom-founded structure based on an empirical high-pressure zone model derived from field measurements. Initial results indicate that this approach is promising for modeling local and global pressures.
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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) | 0.000 | 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".