Correlation of strength, rubber content, and water to cement ratio in rubberized concrete
Bibliographic record
Abstract
Waste rubber tires that cannot be processed for useful applications are numbered in the millions around the world. The build up of old rubber tires in landfills is commonly considered a major threat to the environment, and it is unquestionably a burden on landfill space. This research project was an investigation into the possibility of using fine rubber particles in concrete mixtures. The experimental testing program was designed to study the effect of the addition of crumb rubber, as replacement of a portion of fine aggregates (sand), on the strength of concrete. Rubber was added to concrete in quantities of 5%, 10%, and 15% by volume of the mixture. Three different water/cement ratios were used: 0.47, 0.54, and 0.61. A total of 180 concrete cubes were made. The cubes were tested in compression at 1, 7, 14, 21, and 28 d with the load continuously and automatically measured until failure. The load values were used to calculate compressive stress as related to different rubber contents and water/cement ratios. Compression test results were used to develop several plots relating rubber content and water/cement ratio to compressive stress of concrete. Test results gathered in this research project indicated that the addition of crumb rubber to concrete results in a reduced strength as compared with that of conventional concrete. Based on the experimental results, correlations have been developed to estimate the reduction in concrete strength as a function of the rubber content in the mix.Key words: compressive strength, concrete, crumb rubber, rubberized concrete.
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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) | 0.000 | 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) | 0.001 | 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".