Ice keel load distribution on cylindrical structures
Bibliographic record
Abstract
The ability to reliably predict ice forces generated by ridged ice features is very important for the design of offshore structures in many cold regions. Analytical models have been developed for predicting the loads on a structure due to interaction of an ice ridge keel or rubble, but few data exist for validating these models. In the present paper, the ice keel load distribution across the face of a vertical cylindrical structure is assessed. Ice load data collected in 2002 as part of the STRICE project at Norströmsgrund lighthouse were examined. Events for which the instrumented part of the lighthouse was responding only to ice keel loading were analysed to quantify horizontal ice keel pressures. The results have been compared with predictions of a numerical model. Numerical modeling is also used to predict the global ice forces and ice failure behavior for ridges of different size. From both full scale field data and numerical simulations of ridge interaction with the lighthouse, a trend of higher forces with increasing keel depths can be seen. In addition to generating larger global loads, larger ridges appear to create greater local loads around the waterline of the cylindrical structure. The load distribution across the lighthouse is not uniform. For some events, the forces on the load panels show a parabolic-type distribution while for others a different load distribution with two maxima is seen.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".