Influence of residual strength on progressive collapse of GFRP composite panels under transverse loading
Bibliographic record
Abstract
Abstract Collapse of panels under transverse loads has been reported dependent on formation of some unstable subregions surrounded by failure lines (hinges) within the panels, however such a collapse mechanism has been less investigated for the composite panels. In this paper, the significance of residual strength in the flexural failure lines around the unstable subregions through the collapse of GFRP panels was investigated. In the experimental program, the panels made of E-glass/vinylester composite laminates with various layups and aspect ratios were examined. To observe progression of failure lines in the panels due to large deflections, by application of a support composed of tubular components, the restraining influence of support on the panels was minimized. Based on well-known criteria, initiation and evolution of damage in the composite panels were simulated using the FE models. By a good estimation from the compressive residual strength in the failure lines based on the results of uniaxial compressive and three-point-bending tests, the FE models could simulate softening of the composite panels prior to collapse and temporary stability prior to complete loss of load carrying capacity. Using the FE models, contribution of various strain components in the flexural failure of the laminates and the influence of residual strength on the energy absorption capacity was probed for various configurations of the laminates.
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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 | 0.000 |
| 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 | 0.000 | 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".