Fatigue life prediction of low‐alloy steel samples undergoing uniaxial random block loading histories based on different energy‐based damage descriptions
Bibliographic record
Abstract
ABSTRACT Fatigue damage of low‐alloy steel samples tested earlier under uniaxial random loading spectra was evaluated using energy‐based models of Smith–Watson–Topper, Macha (M), Ellyin and Varvani‐Farahani with different descriptions in damage assessment. Damage over peak‐valley events of block loading histories was accumulated by means of these models. Smith–Watson–Topper approach involved stress and strain components on the maximum principal plane to evaluate fatigue life. M model related the life of samples to damage values calculated from the applied stress and strain histories. Ellyin model assessed damage of samples on the basis of dissipated hysteresis energy generated over fatigue cycles. Varvani‐Farahani damage approach assessed fatigue life on the basis of tensile and shear energies acting on critical plane over peak‐valley events of block histories. The predicted lives based on these approaches were compared with those of experimental data reported by M and coworkers. The choice of energy‐based models in damage assessment of steel samples was discussed on the basis of model description and terms of damage models.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".