Uncertainty in 100 and 10,000 Year Ice Loads on Offshore Structures
Bibliographic record
Abstract
The ISO 19906 arctic structures standard specifies that ice loads be calculated at the extreme level (100 year return period) for verifying ultimate limit states and at the abnormal level (10,000 years) for accidental/abnormal limit states. Since ice load measurements on structures have only been made over much shorter time periods, concerns are often expressed about the accuracy to which 10,000 year values can be estimated. In this paper, the uncertainties in 100 and 10,000 year loads are considered through examples based on experience with calculations of loads on structures in different iceberg and sea ice environments. For icebergs, it is necessary to consider the size distribution of icebergs (including the potential presence of extremely large icebergs and ice islands) as well as drift velocities and shapes that can govern high return- period loads for fixed structures. With sea ice, abnormal-level loads can be governed either by the presence and geometrical properties of large discrete features (e.g. first-year ridges and stamukhi, or in the arctic, multi-year floes with thick ridges), or by very thick ice as a result of thermal growth. It is demonstrated how errors in key contributing ice parameters can influence extreme-level loads, and the relationship between level/rafted ice loads at the abnormal level and the factored (1.35) extreme-level values, and how these uncertainties might be considered in the design process.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".