Experimental Evaluation of Slurry Infiltrated Fibrous Concrete with Waste Tire Rubber Fine Aggregate
Bibliographic record
Abstract
Slurry-infiltrated fibres concrete (SIFCON) represents a specialized variant of highperformance steel fibres reinforced concrete (HPFRC), celebrated for its superior strength and ductility characteristics.In the ongoing quest for more sustainable construction materials, an opportunity arises to harness the massive quantities of waste tires generated globally by the burgeoning automotive industry.This study investigates the effects of replacing fine aggregate in SIFCON with treated waste tire rubber at various replacement rates (5%, 10%, and 15%) on the resultant compressive strength, flexural strength, and split tensile strength.This approach not only contributes to waste reduction but also aids in preserving natural aggregates.A novel method of introducing strong polarity groups to the rubber surface was employed in this study to foster robust chemical interactions between the rubber and the cement matrix, aiming to enhance the concrete's mechanical properties.Despite the observed deterioration in the mechanical characteristics of SIFCON as the rate of sand replacement with rubber powder increased, the incorporation of waste rubber demonstrated significant benefits in terms of reduced the density and cost.This research underscores the potential for treated recycled chopped rubber as a partial substitution for fine aggregate in SIFCON, simultaneously supporting sustainable construction practices and contributing to global waste management efforts.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".