Static and Fatigue Behavior of Sandwich Panels with GFRP Skins and Governed by Soft-Core Shear Failure
Bibliographic record
Abstract
Sandwich panels composed of glass fiber–reinforced polymer (GFRP) skins and low-density polyurethane core are investigated for potential structural applications such as lightweight insulated roofing or cladding systems. Ten 1,220×318×78-mm panels, including three control specimens, were tested under high-cycle fatigue at maximum load levels of 45–70% of the ultimate static load. Fatigue tests were carried out to failure, thereby enabling the development of the complete fatigue life (S-N) curve and stiffness degradation. Digital imaging correlation was used to map shear deformations and showed that shear contributes about 90% of the deflection. Both static and fatigue failure modes were due to core shear failure. The study showed that fatigue threshold was 37% of the ultimate static capacity, and that to sustain at least 2 million cycles, service load should be limited to 45% of ultimate. The maximum design service live and snow loads, as well as wind load based on the most critical conditions in Canada, were all below the fatigue threshold of the panel. The panels had a strength safety factor of 2.1 relative to the factored maximum design live and snow load, and 6.5 relative to the factored maximum wind load. Deflection limits may govern for some service load conditions, but is not critical when only wind load considered. Stiffness degradation reached a maximum of 15–20%, but residual deflections upon unloading were insignificant.
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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".