Large-Scale Triaxial Testing of TDA Mixed with Fine and Coarse Aggregates
Bibliographic record
Abstract
The number of scrap tires discarded worldwide is increasing annually. Stockpiling these tires is not a viable option due to environmental concerns and space limitations. Landfilling is likewise unacceptable and is not permitted in many areas. Recycling these tires is the best alternative. Shredding scrap tires to create a product known as tire-derived aggregate (TDA) is one of the most environmentally friendly methods of recycling scrap tires. In the past few decades, TDA and TDA-soil mixtures have been used increasingly in civil engineering projects. Nevertheless, only limited research has so far been conducted on TDA and TDA mixed with soil. In addition, the majority of past research has focused on TDA particles that do not have steel wires and are small in size. In the present research, triaxial tests were performed on various mixtures of TDA with sand or gravel. Each sample was subjected to three different confining pressures. The results of the tests are presented and discussed, and empirical equations are proposed to match the laboratory results.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".