A Generalized Framework for Risk-Based Extreme Load Analysis in Offshore System Design
Bibliographic record
Abstract
Abstract The primary aim of this research is to consider the correlation among environmental factors in calculating 100 and 1000 years of extreme load design criteria. This is done by considering load as energy transferred from external environment to the offshore system. Also, incorporating spatial and temporal dependence of environmental variables in the context of offshore design. A bivariate extreme value distribution and a conditional joint return level function are developed using the Gumbel–Hougaard copula. The offshore design risk criteria are developed for the finer grid locations (0.1 deg × 0.1 deg latitude/longitude grid) considering joint extreme wind and wave energy. The developed approach is tested using data for the Flemish Pass basin off the east coast of Canada. Along with the primary aim, the impact of climate change is investigated (time and space variability) by implementing the proposed methodology in two cases: the periods from 1959 to 1988 and 1989 to 2018. This study observed that climate change has caused 30% less correlation between wind speed and wave height in recent years (1989–2018) compared to the period of 1959–1988. The proposed extreme design wind speed is 39.7 m/s, and significant wave height is 16.4 m; their joint exceeding probability is 5.80 × 10−5 over an annual basis for a scenario of 100-year.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".