Determination of Assumed Strain Hardening Relationship from Shear–Compression Model and Data Analysis
Bibliographic record
Abstract
A new method for obtaining equivalent stress–strain curves of bilinear and Swift law material models from the numerical results of shear–compression test (SCT) is developed. This task is conducted on the basis of the approximate analytical relations for the compression and shear conditions at the gauge section and by the application of correction factors. Logarithmic correction models are developed and used to generalize the geometry of an optimum shear–compression specimen (SCS) that is calibrated by one-factor-at-time method for a reference material. Furthermore, a dataset created from 125 finite element simulations is analyzed by data analysis techniques, and a universal nonlinear strain hardening relationship is determined. This model is used to predict the stress–strain curves directly from the force–displacement curve of the SCS. The input and output stress–strain curves are in good agreement with an average error of approximately 3%. The numerical findings of this study provide a foundation to develop a general quantitative relationship to study the behavior of materials by SCT.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".