Bibliographic record
Abstract
The American Association of State and Highway Transportation Officials (AASHTO) and the Canadian Standards Association (CSA) codes provide design guidelines for determination of ice loads due to ice crushing and ice bending failures. Both organizations' bridge codes are based upon in-situ field measurements on two bridges in Alberta, Canada. Design formulas provided by both organizations (AASTHO and CSA) rely only on the effective crushing strength of ice to determine ice loading due to crushing failures and ice bending failures. Equations provided for bending failures rely on an assumed linear relationship between effective ice crushing strength and ice bending strength. Ice bending strength is apparently assumed to be 0.84 times the effective crushing strength using these codes. Recent in-situ testing of fresh water river ice on the North Slope of Alaska indicates that the ice bending strength is closer to 0.5 times the effective ice crushing strength for this location. Additionally, numerous studies have identified that ice crushing strength is highly dependent upon temperature, while ice bending strength remains relatively constant through changing temperatures. Results of the in-situ bending tests and other associated research indicate that calculations to determine ice forces due to bending failure should consider ice flexural or tensile strength rather than a direct correlation to effective ice crushing strengthen. This paper will present the testing methods and procedures, results and conclusions of flexural and compressive ice testing performed on river ice on the North Slope of Alaska. Comparisons between AASHTO and CSA predicted and actual bending strength will be made. An alternative calculation method determining the ice sheet bending strength will be presented. The authors recommend that the presented, alternative methods be adopted within the CSA and AASHTO codes to better predict ice loads associated with ice bending behavior.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".