Effect of the Particle Size on the TDA Shear Strength and Stiffness Parameters in Large-Scale Direct Shear Tests
Bibliographic record
Abstract
The increase in the number of discarded tires every year is becoming a major issue all over the world. Tires stockpiles and landfills have become a critical issue as they are considered a fertile environment for the breeding of rats and insects, a real fire hazard that may take up to months to extinguish and occupy a valuable, large area of land. One of the safest effective ways of recycling tires is that to use them as backfilling material, among different usages, in civil engineering projects due to their low unit weight and specific gravity. However, to use any material in the construction industry, several material properties must be evaluated, including the shear strength and stiffness parameters. Many factors control the measured parameters. One main factor that is known to have a significant effect is the particle size. This paper focuses on evaluating the effect of the particle size on the shear strength and stiffness parameters of six tire-derived aggregate (TDA) samples having particle sizes range between (9.5–101.6 mm) using a large-scale direct shear machine. The tests were conducted under three normal stresses: 50.1, 98.8 and 196.4 kPa using a constant shearing rate of 0.5 mm/min. The results of this study showed an increasing angle of internal friction as the maximum particle size increases. Moreover, the secant shear modulus also exhibited an increase by increasing the maximum particle size. Furthermore, equations to estimate the stress-strain curves of Type A-TDA for different confidence levels were developed, and their predictions were compared with experimental results to assess their suitability.
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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 | 0.000 | 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".