Ultimate strength assessment of stiffened panel using non-linear mechanical behavior of an equivalent single layer: grillage FE model used for analysis
Bibliographic record
Abstract
This example shows how the ESL can be applied in the ultimate strength structural analysis in Abaqus finite element software. In other words, ESL methodology is applied only in some parts of the structure while larger structural supporting components like girders and webframes are still modeled explicitly. FIles include also the Full_3D_FEM model used for validating the ESL model. Dataset includes following files: 1. ESL_nonlinear_grillage.inp - this is Abaqus input file for running the ESL nonlinear grillage model. 2. ugensFINALv_master.for - this defines the nonlinear stiffness or ABD matrix. This is called by input file (ESL_nonlinear_grillage.inp ). 3. Full_3D_FEM.inp - Full_3D_FEM model used for validating the ESL model.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.048 |
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".