Ice stress‐strain curve prediction in uniaxial compression loading in the objective of atmospheric icing risk evaluation
Bibliographic record
Abstract
Abstract Atmospheric icing is a major cause of damage to electric power networks. During ice storms, “ice shedding off” cables and conductors can lead to major damage to power systems, mostly by mechanical failure. The brittleness of ice might be its most important and critical property possibly leading to hazardous events in urban regions, and involving equipment such as electrical power lines. To avoid or reduce the risks from this phenomenon, it is necessary to understand the mechanical behavior of ice, including ice strain, as well as its maximal strength and residual strength. The behavior of ice depends mainly on the applied stress and strain rate, as well as temperature, salinity, porosity, and particle size. As a fundamental subject on the deformation behavior of ice, the simple case of polycrystalline isotropic ice was chosen in this study. A methodology is developed to determine the stress‐strain curve for ice as a function of temperature and several uniaxial compression loading conditions. Mathematical relations are developed for several loading conditions and temperature. The results of this research allow to predict the maximal and residual strength of ice, as functions of strain rate and temperature.
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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) | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".