Evaluation of the Incorporation of Tire-Derived Aggregates (TDA) in Rigid Pavement Mix Designs
Bibliographic record
Abstract
Owing to the extensive worldwide generation of solid wastes, such as rubber tires, and the resulting adverse environmental impacts, the incorporation of these waste materials in construction projects has become a widespread aim. However, concerns have arisen regarding the effects of rubber waste on the mechanical properties of Portland cement concrete (PCC) mixes. Thus, this study investigates the effects of replacing natural coarse aggregates with tire-derived aggregates (TDA). In PCC mixes, natural aggregates were replaced by 0, 10, 20, 40, 60, 80, and 100% TDA by volume, and the properties of these specimens were tested in the laboratory. The results obtained were then used as inputs for the KENPAVE software, to evaluate induced stresses, deflections, and cracking indices in rigid pavement slabs, with eleven different thicknesses, ranging from 200 to 300 mm in 10 mm increments. Stresses under different loading conditions decreased as PCC slab thickness and TDA content increased. Increased deflection and cracking indices resulting from adding TDA could be counteracted by increasing the PCC slab thickness by 10 mm. Moreover, environmental impacts and cost analyses were examined via PaLATE 2.0, which showed that the use of TDA could reduce energy consumption, harmful emissions, and material costs. Overall, this study indicates that the use of TDA in PCC mixes has benefits that can make it a good candidate for sustainable, ecofriendly rigid pavement construction projects.
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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 | 0.000 |
| 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".