Effects of inclusion of granulated rubber tires on the mechanical behaviour of a compressive sand
Bibliographic record
Abstract
Drained triaxial shearing tests were performed on a well-graded compressive sand (completely decomposed granite, CDG) and its mixtures with granulated rubber tires to investigate the effects of rubber size and content on their mechanical behaviour. Three sizes of rubber particles, GR1, GR2, and GR3, were used with size ratios to CDG (D 50,rubber : D 50,CDG ) of 0.9, 3.5, and 7.2, respectively, and the rubber content ranged from 0% to 30%. The results show that for CDG–GR1 mixtures, the strength decreases with increasing rubber content, while for CDG–GR2 and CDG–GR3 mixtures, the strength decreases only at 10% rubber content and then increases markedly with increasing rubber content. The increase of strength is mainly because the inclusion of large rubber particles widens the particle size distributions of the mixtures, resulting in denser packings. The denser packings also lead to a decrease in compressibility. At larger size ratio and higher rubber content, the CDG–rubber mixtures show higher shear strength and lower compressibility than pure CDG, which indicates the CDG–rubber mixtures are very suitable to be used as filling materials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".