Improved Equations for the Actions of Thick Level Ice on Sloping Platforms
Bibliographic record
Abstract
Abstract In ISO19906 (2010) (Arctic Offshore Structures) specific algorithms are provided for level ice loads on sloping structures; they are based on the separate work of Ralston and Croasdale. These methods were developed decades ago and comparisons with full scale data, especially from Confederation Bridge, suggest that certain idealizations can be improved; more importantly that they may be over-predicting the measured loads. For these reasons it was decided to critically review the existing Croasdale et al algorithm (as specified in ISO) and update it based on learnings from Confederation Bridge, other experience and new ideas. During the study, over 50 ice interaction events at Confederation Bridge were chosen as geometrically similar to thick ice acting on an Arctic structure. The interaction process and relevant parameters (such as ride-up height) were documented in detail and the measured loads compared with predictions for each event. The model, as currently specified in ISO, generally over-predicted by a factor of about 1.6. The model was improved in the course of the work; especially the physics of breaking and ride-up. The new model is capable of matching the Bridge measurements without bias. This paper presents the final methodology and equations which resulted from the study which was conducted over several years and resulted in an extensive report and documentation. The equations are closed form and can be applied relatively simply. Examples of using the method are provided. A more comprehensive description of the complete study is given in KRCA (2014) and Croasdale et al. (2016a).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".