Properties and Behavior of Rubberized Concrete Enhanced with PVA Fibers
Bibliographic record
Abstract
Due to growing populations, approximately one billion scrap tires are generated annually worldwide. This is a problem particularly in more developed countries where the per-head share of scrape tires is much higher than the global average. The adverse environmental impacts associated with landfilling scrap tires made it imperative to promote eco-friendly solutions such as utilizing them in civil engineering applications. This paper explores the use of tire-derived aggregates (TDAs) with large particle sizes that require less energy to produce as a substitute for traditional aggregates in concrete production. A comprehensive experimental program was conducted to study the effects of the TDA content on the density, compressive strength, elastic modulus, strain at failure, splitting tensile strength, and flexural strength of rubberized concrete at 28 days. Furthermore, with the aim of improving the tensile and flexural properties of rubberized concrete, the use of polyvinyl alcohol (PVA) fibers was also investigated in this study. A total of 126 specimens, half of them containing PVA fibers, were prepared from fourteen different concrete mixtures with varying percentages of TDAs replacing coarse aggregates. Results indicate that a reasonable TDA content of less than 20% can be used to produce concrete with comparable or even superior properties for specific applications requiring moderate strength and higher deformability while reducing waste tires in landfills. In addition, adding 1% PVA fibers to the mixtures was found to enhance the specimens’ compressive, tensile, and flexural strengths and reduce the observed loss of strength rate in rubberized concrete, especially at higher TDA contents. Overall, this research suggests that TDAs can be a sustainable and cost-effective solution for applications that do not require great concrete compressive strength but a more accommodating plastic behavior.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".