Development of an Eco-Acoustic Absorber Based on Local Recycled Granular Materials
Bibliographic record
Abstract
Enhancing the acoustic absorption performance across a wide frequency band can be processed through improving the selection of raw materials. In the field of acoustics, there is a growing shift towards eco-friendly resources to mitigate environmental and economic impacts. The use of recycled granular materials of local origin requires special attention to the granulometry and the optimal combinations of grain sizes. This study focuses on two key aspects: investigating the consolidation of granules as well as creating and testing monodispersed and bi-dispersed granular test specimens using different proportions of granules. The diameter range of particles is fixed between 250 μm and 2 mm. The properties of the samples are characterized using direct measurement methods, which enables the assessment of various properties such as porosity, resistivity, bulk density and absorption coefficient. The results show promising improvements in acoustic absorption performance, with the exception of anti-resonant dips. To remedy these dips and achieve broadband absorption, an experimental approach is proposed where the relationship between resistivity ratios, cementitious binder content and particle diameters are optimized. This approach aims to fine-tune the parameters and achieve a more balanced acoustic absorption profile.Keywords: sound absorption, granular material, recycled material, eco-friendly material, granulometry, experimental approach, material properties.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".