A probabilistic approach to analysis of ice loads for the Confederation Bridge
Bibliographic record
Abstract
The main focus of the paper is the framework for analysing ice loads on the Confederation Bridge across the Northumberland Strait, using probabilistic methods. Safety targets were given as a beta factor of 4.0 for a 100-year lifetime, amounting to a probability of failure of about 3 × 10-7 per year. The ice regime comprises rafted and ridged ice, and peak loads are expected during March and April of each year. A simulation method was developed, in which loads are calculated corresponding to individual interactions associated with ridges in the ice floes that traverse the strait. The floes are driven by environmental driving forces, and the highest loads occur when these exceed the ridge failure loads. The load results from failure of the consolidated layer and rubble keel. Methods for the analysis of this are described. The determination of extreme loads depends on the number of interactions per year. Difficulties in modelling are described, together with techniques for analysis, such as updating of probability distributions given an interaction. Many of these techniques were derived from work related to the Beaufort Sea oil exploration. The results reflect a best-estimate approach to those parameters for which information was sketchy, or unavailable. They are therefore conditional on those estimates, but as the results are largely insensitive to these, the potential for error is minimal. There are a number of parameters (e.g., friction coefficient) that do have a significant effect and for which all those involved in the effort would have wished better definition. This sensitivity is reflected in the two sets of results presented in the paper.Key words: ice, forces, probabilistic, safety, bridges, modelling.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".