Effect of the Particle Size on TDA Shear Strength Parameters in Triaxial Tests
Bibliographic record
Abstract
Tire recycling and reuse in North America and worldwide have increased considerably, intending to reduce the harmful effects of scrap tires on the environment. Accordingly, the use of tire derived aggregates (TDA) as backfill material in civil engineering applications is on the rise at an unprecedented rate. However, to use TDA in the construction industry, its strength and stiffness parameters properties must be evaluated. One key factor that is known to influence the strength and stiffness of backfill material is the particle size of the used material. Hence, in this paper, a series of large-scale triaxial tests on five TDA samples with different maximum particle size, Dmax, of 19.05, 25.4, 38.1, 50.8 and 76.2 mm were conducted to investigate the effect of the particle size on the obtained results. The tests were done under consolidated drained conditions using three confining pressures of 50, 100, and 200 kPa. The results showed that the shear strength of TDA increase by increasing the maximum particle size while the cohesion did not show a specific trend. Moreover, the samples exhibited an increase in the secant elastic modulus by increasing the particle size.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".