Evaluation of the Shear Strength Behavior of TDA Mixed with Fine and Coarse Aggregates for Backfilling around Buried Structures
Bibliographic record
Abstract
Although some discarded tires are reused in various applications, a considerable number end up in landfills, where they pose diverse environmental problems. Waste tires that are shredded to produce tire-derived aggregates (TDA) can be reused in geotechnical engineering applications. Many studies have already been conducted to examine the behavior of pure TDA and soil-TDA mixtures. However, few studies have investigated the behavior of larger TDA particles, 20 to 75 mm in size, mixed with various types of soil at percentages ranging from 0% to 100%. In this study, TDA was mixed with gravelly, sandy, and clayey soils to determine the optimum soil-TDA mixtures for each soil type. A large-scale direct shear box (305 mm × 305 mm × 220 mm) was used, and the mixtures were examined with a series of direct shear tests at confining pressures of 50.1, 98.8, and 196.4 kPa. The test results indicated that the addition of TDA to the considered soils significantly reduces the dry unit weight, making the mixtures attractive for applications requiring lightweight fill materials. It was found that adding TDA to gravel decreases the shear resistance for all considered TDA contents. On the contrary, adding up to 10% TDA by weight to the sandy or clayey soils was found to increase the shear resistance of the mixtures. Adding up to 10% TDA by weight to the clayey soil also sharply increased the angle of internal friction from 18.8° to 32.3°. Moreover, it was also found that the addition of 25% TDA by weight to the gravelly or sandy soils can reduce the lateral earth pressure on buried structures by up to 20%. In comparison, adding 10% TDA to clay resulted in a 36% reduction in the lateral earth pressure.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".