Cementitious Composites Containing Recycled Tire Rubber: An Overview of Engineering Properties and Potential Applications
Bibliographic record
Abstract
Abstract One of the major environmental challenges facing municipalities around the world is the disposal of worn out automobile tires. To address this global problem, several studies have been conducted to examine various applications of recycled tire rubber (fine crumb rubber and coarse tire chips). Examples include the reuse of ground tire rubber in a variety of rubber and plastic products, thermal incineration of waste tires for the production of electricity or as fuel for cement kilns, and use of recycled rubber chips in asphalt concrete. Unfortunately, generation of waste tires far exceeds these uses. This paper emphasizes another technically and economically attractive option, which is the use of recycled tire rubber in portland cement concrete. Preliminary studies show that workable rubberized portland cement concrete (rubcrete) mixtures can be made provided that appropriate percentages of tire rubber are used in such mixtures. Achievements in this area are examined in this paper, with special focus on engineering properties of rubcrete mixtures. These include: workability, compressive strength, split-tensile strength, flexural strength, elastic modulus, Poisson's ratio, toughness, impact resistance, sound and heat insulation, and freezing and thawing resistance. The benefits of using magnesium oxychloride cement as a binder for rubberized concrete mixtures are discussed. Various applications in which rubcrete could be advantageous over conventional concrete are described.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".