Effect of Incorporating Shredded and Crumbed Rubber in Pavement-Grade Concrete on Elasticity and Toughness Moduli
Bibliographic record
Abstract
Used car tires are generally recycled at the end of their lifecycles to make useful products.However, the phenomenon of dumping used tires in Kuwait has reached significant levels, with a "tire graveyard" containing over 7 million tires being formed in a remote area of the country.This landfill is a major environmental hazard and poses a major risk to the public health that wouldn't be allowed in other parts of the world.To mitigate the environmental impact, the tires must be recovered and recycled at a large scale.This study aims to quantify the impact of incorporating repurposed rubber products on the toughness and modulus of elasticity of concrete.The rubber products were incorporated into concrete individually and tested to examine their properties and effects on a benchmark mix before creating a hybrid mix that contains both materials.The concrete was tested for its slump, compressive strength, split tensile strength, modulus of elasticity, toughness, and stress-strain behaviour.The use of shredded and crumbed rubber had a detrimental impact on most concrete properties examined in this study; however, the crumbed rubber improved the toughness of the concrete.Additionally, the hybrid mix displayed similar behaviour to its constituent replacement materials, with the most notable observation being a sharp drop in the mix's toughness.Overall, the rubberized concrete displays suitable properties (compressive strength, modulus of elasticity, and toughness) for use in paving structures.Further studies could evaluate the long-term effects of using this concrete in a hot weather climate.
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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 | 0.001 |
| 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 | 0.001 |
| 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".