Axial Loading Tests and Simplified Modeling of Sandwich Panels with GFRP Skins and Soft Core at Various Slenderness Ratios
Bibliographic record
Abstract
Experimental and analytical studies were conducted to investigate the axial strength and failure modes of sandwich panels with glass fiber reinforced polymer (GFRP) skins and soft polyurethane core. A total of 45 specimens of 78×150 mm cross-section and of lengths Le varying from 500 to 2,400 mm [i.e., slenderness ratio (KLe∶r) of 15–70, where r is the radius of gyration] were tested concentrically using pin-ends (K=1). The effects of skin thickness and an internal GFRP rib on axial behavior were studied. The study first assessed the level of out-of-straightness and concluded that all specimens generally fall within an acceptable limit of span/1,000. A model based on sandwich panel theory, accounting for excessive shear deformations of the soft core, was used to predict axial strength at a wide range of KLe∶r and then used in a parametric study. It was shown that short panels with KLe∶r of 15–17 experienced local failure, outwards skin wrinkling in non-ribbed panels, or skin crushing in ribbed panels. Slender panels with KLe∶r of 41–70 experienced global buckling followed by secondary local failure. Panels with intermediate slenderness (17–41) showed mixed failure modes. The load at which local failure governs at low KLe∶r appears to be a constant, independent of KLer, whereas the global buckling load decreases significantly with increasing KLe∶r. Increasing skin thickness appears to be more effective at high KLe∶r whereas increasing core shear modulus appears to be more effective at low KLe∶r.
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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 | 0.000 | 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) | 0.002 | 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".