Properties of self-consolidating rubberised concrete reinforced with synthetic fibres
Bibliographic record
Abstract
The aim of this study was to develop and optimise self-consolidating rubberised concrete (SCRC) modified by different types and volumes of synthetic fibres (SFs). Steel fibres were also tested in this investigation for comparison. The investigation particularly sought to utilise the beneficial effect of fibres to alleviate the reduction in splitting tensile and flexural strengths resulting from adding crumb rubber (CR) to SCRC mixtures. The results indicated that the addition of SFs reduced the fresh properties, which limited the maximum percentage of CR that could be used in SCRC mixtures to 20%, compared to 30% maximum percentage of CR used in developing successful SCRC mixtures without SFs. However, the use of SFs beneficially contributed to alleviating the reduction in the splitting tensile strength and flexural strength of SCRC mixtures resulting from adding CR. Although increasing the length of SFs further reduced the fresh properties of the mixtures, this also enhanced further the splitting tensile and flexural strengths of SCRC mixtures. The results also showed that when self-compactability was disregarded, it was possible to use a higher combination of CR (30%) and fibre (1% volume fraction) in the development of vibrated rubberised concrete, resulting in mixtures with further improvements in splitting tensile and flexural strengths, and reduced self-weight.
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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 | 0.000 | 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".