Performance of eco-composite sandwich panels manufactured by vacuum bagging infusion
Bibliographic record
Abstract
The construction industry uses a complex combination of materials associated with a high carbon dioxide footprint. Therefore, several new building systems and solutions have emerged with higher sustainability and improved energy efficiency over their service lives, with the development of customizable lightweight sandwich structures, being one of the most promising strategies. The common processes for manufacturing fibre-reinforced polymer (FRP) composites, with large dimensions, are both hand layup-assisted vacuum bagging and vacuum bagging infusion. Besides, composite sandwich panels are traditionally composed by glass fibres and thermoset resins. However, basalt fibres can be an interesting substitute, since they are more sustainable, and have a higher performance. Despite the large amount of research work carried out on advanced composite sandwich panels, only a few studies were focused on the manufacturing of these structures based on basalt fibres assisted by vacuum infusion. Therefore, this work aims to design, manufacture and compare the mechanical, physical, thermal and environmental performance of these composite sandwich panels, comprising either glass or basalt fibres, which will be further applied in modular construction. The results revealed that it is feasible to produce composite sandwich panels through this method, and the most promising solution attained through this study is based on basalt fibres.
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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) | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".