Probabilistic Fracture Mechanics Applied to Compressive Ice Failure
Bibliographic record
Abstract
During interactions between ice and engineered structures, ice is highly prone to brittle fracture for all but the slowest interaction rates. Under compressive loading conditions local fractures (spalls) near the interaction zone regularly propagate from either pre-existing or newly precipitated cracks that initiate from naturally occurring internal flaws in the ice. These spalling events result in the localization of contact into high-pressure zones through which the majority of the load is transmitted. During a continuous interaction, successive failure events contribute to random variations in the ice-edge geometry and contact conditions at the interaction interface. At the same time, localized microstructural modification to the ice in the highly damaged layer adjacent to the contact zone has considerable effect on the state of stress in the ice. For a given contact geometry and state of damage, the contact pressure required to trigger a fracture will depend on the size, location and orientation of flaws in the ice. Since in nature there will be random variability in the flaw structure and contact conditions, and the state of damage will depend on prior stress history, a probabilistic framework is believed to be most appropriate for modeling spalling fracture. In the present analysis a probabilistic fracture mechanics model has been used to illustrate the link between probabilistic aspects of fracture and the observed scale effect for compressive ice failure, whereby pressure is observed to decrease for increasing area. The influence of factors, such as variations in ice edge geometry associated with successive failure events, on the probability of local spalling fracture are also explored. From this work it is concluded that the mechanics of compressive ice failure are well explained through the competing yet complementary processes of brittle spalling fracture and pressure softening due to microstructural damage. Recommendations for future work are provided.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".