A Procedure for Non-Linear Structural Collapse Analysis
Why this work is in the frame
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Bibliographic record
Abstract
It is a normal practice nowadays in structural engineering, including ships and offshore industry, to perform non-linear finite element analysis to assess the structure’s capacity for design or evaluation purposes. However, experience has shown that the quality and accuracy of the non-linear FE analysis results are highly dependent on the skill of the person performing the analysis and the analysis procedure used. The difference between results obtained by different people can be significant. In some cases, the results can be misleading. It is considered that a unified procedure is necessary. This paper is moving a step further and trying to develop a standard procedure which can provide a guideline for structural collapse analysis of stiffened panels under any load combinations. The paper provides the technical background on the analysis procedure and the key steps such as model extent, mesh density, initial imperfections, and boundary conditions. Analysis examples are provided in the paper for reference and discussions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it