Experiments and nonlinear analysis of the impact behaviour of sandwich panels constructed with flax fibre-reinforced polymer faces and foam cores
Bibliographic record
Abstract
As the effects of climate change become more apparent, it is necessary that environmental impact is considered in every aspect of our society, including the design of new infrastructure. The use of natural materials for building construction is one way to improve the sustainability of infrastructure and therefore it is important that the behaviour of structures made with natural materials be investigated extensively and well understood. In this study, the performance of sandwich panels constructed with flax fibre-reinforced polymer faces and foam cores under impact loading is studied experimentally and analytically. The parameters of the tests were facing thickness (1, 2 and 3 layers of flax fabric) and core density (32, 64 and 96 kg/m 3 ). Each specimen was 1220 mm long, 152 mm wide and approximately 80 mm thick and was tested by a 10.41 kg drop weight impact at mid-span. Each specimen was tested multiple times starting at a drop height of 100 mm and increasing the height by 100 mm for each subsequent test until ultimate failure. The results indicate that the ultimate impact energy increases with both core density and face thickness. The four main failure modes observed were: compression face crushing, compression face wrinkling, core shear and tension face rupture. The failure modes observed generally matched those observed during similar quasi-static testing. Additionally, a nonlinear incremental iterative model was developed based on the conservation of energy during an impact event and the nonlinear mechanical behaviour of both the fibre-reinforced polymer faces and foam cores. This novel model accurately predicts the total deflection and face strains based on the energy of an impact.
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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) | 0.002 | 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".