Bibliographic record
Abstract
Abstract Processed waste tires mixed with soils are applicable in lightweight fills for slopes, retaining walls, and embankments that may be subjected to seismic loads. Rubber's high damping capacity permits consideration of granulated rubber/soil mixtures as part of a damping system to reduce vibration. The dynamic properties of granulated rubber/soil mixtures are essential for the design of such systems. This research investigates the shear modulus and damping ratio of granulated rubber/sand mixtures using a torsional resonant column. Specimens were constructed using different percentages of granulated tire rubber and Ottawa sand at several different percentages. The maximum shear modulus and minimum damping ratio are presented with the percentage of granulated rubber. It is shown that reference strain can be used to normalize the shear modulus into a less scattered band for granulated rubber/sand mixtures. The normalized shear modulus reduction for 50% granulated rubber (by volumme) is close to a typical saturated cohesive soil. Empirical estimation of maximum shear modulus of soil/rubber mixtures can be achieved by treating the volume of rubber as voids.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".