Editorial: Special Issue on Fatigue and Fracture Mechanics
Bibliographic record
Abstract
The study of fatigue and fracture behaviors in engineered components remains a critical area across multiple disciplines. Limits on materials behavior are among the most significant technical challenges to enhancing the safety and reliability of engineering systems. Thus, accurately defining recent advancements in analytical methods and testing techniques within fatigue and fracture mechanics for engineered structures, components, and materials is essential. This analysis encompasses both experimental research and recent developments in modeling approaches. Key areas of interest include applications of emerging analytical tools and novel experimental techniques to assess and improve durability and damage tolerance using multiscale or multiphysics-based approaches; studies on the effects of additive manufacturing processes on fatigue and fracture properties; and implications of improved modeling and experimental capabilities on fatigue life forecasting and structural health monitoring strategies. In this context, a special issue in Materials Performance and Characterization offers readers valuable insights for their research. The call for papers attracted high-quality contributions from leading scientists and engineers resulting in 11 full-length manuscripts. The main topics covered include fatigue crack growth fracture, propagation, toughness fatigue, and damage failure inspection repair. The editorial teams extend their heartfelt thanks to the authors and reviewers for their dedication and to the ASTM staff for their support in bringing this issue to publication. We hope these papers provide valuable contributions to ongoing research efforts in fatigue and fracture mechanics.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.028 | 0.025 |
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".