An Implementation of ISO 19906 Formulae for Global Sea Ice Loads within a Probabilistic Framework
Bibliographic record
Abstract
Abstract The ISO 19906 standard provides guidance for the calculation of characteristic ice loads on offshore structures in arctic and cold regions. Ice failure is a complex process and the development and improvement of ice load models can be challenging, in large part because of difficulties obtaining full-scale data and scaling issues when extrapolating small-scale test data. Many of the ice load models referenced in ISO 19906 were developed during arctic exploration in the 70's and 80's. Typically, simplified geometries are assumed for both the structure and ice features in order to obtain analytic solutions; other simplifications may be incorporated appropriate for the specific applications considered and information available. A significant proportion of referenced models provide the maximum load during an interaction, rather than the development of the load over time. This can be a limitation were penetration into a thick ridge is limited by available driving force and kinetic energy. Given the large variety of ice conditions to which a structure may be subjected and the apparent randomness in ice fracture and damage mechanisms, there can be considerable variation in loads. Ice strength may be set to a characteristic fixed value, the ISO model for global sea ice loads is based on a relationship that considers ice thickness and contact width and is based on upper envelop fits to failure data. When determining the appropriate characteristic load on a structure, consideration should be given to exposure (i.e., the number and durations of ice interactions). Loads based on characteristic values for parameters such as ice thickness and ice strength could be inaccurate for scenarios where the exposure is significantly different than that on which the characteristic values were based. The application of probabilistic methods can be used to account for differences in exposure. While ISO 19906 references such methods, guidelines on implementation is limited. This paper examines issues in implementing available formulae for ice loads on fixed structures within a probabilistic framework and shows how characteristic ice loads differ depending on the model and assumptions used. The Sea Ice Loads Software (SILS), a probabilistic framework developed by C-CORE for calculating characteristic ice loads using the methods referenced in ISO19906, is used for the analyses and comparisons.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".