Development of a Synergistic Damage Mechanics-Based Model for Predicting Multiaxial Effects in Progressive Failure of Composite Structures
Bibliographic record
Abstract
A major benefit of advanced fiber-reinforced polymer composites is that they can be tailored and optimized to suit a particular structural application by orienting the reinforcing fibers along multiple directions. For practical load-bearing structural components manufactured from multidirectional laminates, predicting their mechanical behaviour is quite complex. This is specifically the case for progressive failure analysis of these materials when subjected to quasi-static or fatigue loading since local cracks will initiate and evolve in multiple directions simultaneously. The difficulty of the problem increases further when these laminates are subjected to complex multiaxial stress states. This is due to the fact that the multidirectional crack state will be subjected to additional crack driving stress components, which will ultimately alter the crack evolution characteristics. A synergistic damage mechanics (SDM) methodology has recently been developed to address these issues in progressive damage analyses of composite laminates containing multiple damage modes and subjected to uniaxial loading [1]. By combining micromechanics and continuum damage mechanics, the SDM methodology provides a rigorous and practical tool for accurate prediction of progressive damage behaviour in composite structures. This is essential for accurately predicting the integrity and durability of practical structures, which will lead to safer and more efficient designs.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".