Optimal Design and Portfolio Risk Management for Groups of Structures
Bibliographic record
Abstract
The present paper addresses the problem of optimal design of portfolios of fixed offshore structures. A new framework for design is developed where the effect of dependency in the performance of structures subject to common extreme load events is taken into account in the design by inclusion of the follow-up consequences resulting from the simultaneous failure of several structures in the portfolio. First the special aspects of optimal design subject to follow-up consequences are addressed from the perspective of structures portfolio risk management. Thereafter the problem of optimal design of groups of structures is defined with special considerations to the assessment of the relation between the design, the probability density function of the life cycle benefits and the number of structures considered (in a group). Using this model basis the optimum design of fixed steel offshore platforms where the capacity of the structures against extreme wave loads can be expressed as function of the Reserve Strength Ratio (RSR) is considered. Thereafter parametric studies are conducted to illustrate the significance of the number of structures considered in a group, the correlation between the extreme loads acting on the different structures and the significance of including the follow-up consequences into the design optimization problem.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".