Methodology to Evaluate Sea Ice Loads for Seasonal Operations
Bibliographic record
Abstract
Offshore structures designed for operation in regions where sea ice is present will include a sea ice load component in their environmental loading assessment. Typically ice loads of interest are for 10−2, 10−3 or 10−4 annual probability of exceedance (APE) levels, with appropriate factoring to the required safety level. The ISO 19906 standard recommends methods to determine global sea ice loads on vertical structures, where crushing is the predominant failure mode. Fitted coefficients are proposed for both Arctic and Sub-Arctic (e.g. Baltic) conditions. With the extreme ice thickness expected at the site of interest, an annual global sea ice load can be derived deterministically. Although the simplicity of the proposed relation provides quick design load estimates, it lacks accuracy because the only dependencies are structure width, ice thickness and provided coefficients; no consideration is given to site-specific sea ice conditions and the corresponding exposure. Additionally, no term is provided for including ice management in the design load basis. This paper presents a probabilistic methodology to modify the deterministic ISO 19906 relations for determining global and local first-year sea ice loads on vertical structures. The presented methodology is based on the same ice pressure data as presented in ISO 19906, but accounts better for the influence of ice exposure, ice management and site-specific sea ice data. This is especially beneficial for ice load analyses of seasonal operations where exposure to sea ice is limited, and only thinner ice is encountered. Sea ice chart data can provide site-specific model inputs such as ice thickness estimates and partial concentrations, from which corresponding global load exceedance curves are generated. Example scenarios show dependencies of design loads on season length, structural geometry and sea ice conditions. Example results are also provided, showing dependency of design loads on the number of operation days after freeze-up, providing useful information for extending the drilling season of MODUs after freeze-up occurs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".