Probabilistic Assessment of Multi-Year Sea Ice Loads on Upward Sloping Arctic Structures
Bibliographic record
Abstract
Abstract Multi-year (MY) ridge and level ice interactions with sloping and conical structures involve complex ice feature shapes and ice failure mechanisms. The limited available field data makes calibration of associated load models difficult. To account for associated randomness and uncertainty, models may tend to be on the conservative side. New deterministic algorithms were recently developed to calculate loads more accurately for interactions of MY level ice and MY ridges with an upward sloping structure. This paper presents the application of these recently developed formulations in a probabilistic framework using SILS. SILS is a Monte-Carlo type simulator developed by C-CORE following the general procedures outlined in ISO 19906. Ice and metocean input parameters are defined by the user either as a fixed value (e.g. friction coefficients) or a random distribution (e.g. ice drift speed, floe size). The yearly encounter frequency is first estimated for these ice features for the site of interest. The ice loads are then determined for each of simulated interaction event occurring over the model timespan, using the deterministic load formulations. By simulating a large number of years of ice interactions, design ice loads can be determined that correspond to various low annual probability of exceedances. This paper demonstrates how complex loading scenarios, modelled in terms of idealized deterministic models, can be incorporated within a Monte-Carlo framework to provide design level loads. During the model implementation and analysis of results, significant improvements were identified and implemented in the deterministic model, resulting in a more robust model and better design estimates. The results provide valuable insights regarding model inputs and behaviour corresponding to the extreme design ice loads. An example of a full probabilistic analysis is presented in the paper to illustrate the models. Here the probabilistic framework of SILS is used to assess a Base Case scenario and a number of sensitivity cases using different environmental inputs and model parameters.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".