Failure curves for predicting brittle fracture in V-notched structural components loaded under mixed tension/shear: An advanced engineering design package
Bibliographic record
Abstract
Numerous failure curves are presented in this manuscript to predict the onset of sudden fracture in V-notched brittle materials under combined tension-shear loading conditions. The curves were developed in a computational manner in terms of the notch stress intensity factors and based on the suitable failure concept of the maximum tangential stress (MTS) utilized frequently in the past by the author and his co-researchers for predicting mixed mode brittle fracture in extensive notched specimens. Three extensively used notch angles and various notch tip radii were considered in the computations. A wide range of brittle materials were also taken into account by defining and using the material critical distance. Through predicting loadbearing capacity and notch bifurcation angle utilizing only the two basic material properties namely the ultimate tensile strength and the plane-strain fracture toughness, engineers can design more rapidly and conveniently the V-notched brittle components with the aim to withstand reliably against sudden fracture. }}
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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) | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".