Structural and Mechanical Characteristics of Sustainable Concrete Composite Panels Reinforced with Pre-Treated Recycled Tire Rubber
Bibliographic record
Abstract
The escalating environmental impact of non-biodegradable waste, particularly from discarded tires, necessitates innovative recycling strategies.This study explores the potential of incorporating pre-treated crumb rubber from waste tires into concrete mixtures as a sustainable alternative to traditional coarse aggregates.A comprehensive experimental investigation was conducted to evaluate the fresh-state properties, workability, and mechanical behavior of rubberized concrete.The study focused on varying proportions of pre-treated rubber particles, substituting 10%, 20%, 30%, and 40% of conventional coarse aggregates by volume.These rubber particles were treated with cement paste to enhance bonding strength within the composite material.Standard tests were employed to assess the properties of the rubberized concrete, including density, slump, and compressive strength.The findings reveal that the incorporation of recycled tire rubber as a partial aggregate replacement notably impacts the workability and mechanical properties of the concrete.However, this reduction is mitigated when a concrete superplasticizer is utilized.Furthermore, the research indicates a decrease in the failure load of rubberized concrete composite slabs, ranging from 15% to 58%, compared to those constructed with traditional aggregates.Additionally, a significant reduction in the initial cracking load was observed.These outcomes offer critical insights into the structural and mechanical implications of using pre-treated rubber particles in concrete.While the decrease in mechanical performance poses challenges, the study illuminates pathways for enhancing the sustainability of concrete through the innovative reuse of waste materials.This approach not only addresses environmental concerns but also opens new avenues for the development of more sustainable construction materials.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.002 | 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".