Crack properties, toughness and absorption evaluation of FRCC incorporating reclaimed asphalt pavement and crumb rubber as aggregates
Bibliographic record
Abstract
The development and application of fibre-reinforced cementitious composites (FRCC) have evolved significantly over the last decade. However, there is a need to find innovative ways to improve the sustainability of composites. The production and transportation of the major binder (i.e. Portland cement) and aggregates used for FRCC consumes high energy and natural resources. Therefore, finding ways to incorporate recycled materials to replace these conventional components can be used to improve the sustainability of the FRCC. Also, the special micro silica sand used to produce certain types of FRCC is a major concern due to its limited availability and higher cost. On the other hand, there exist various waste materials that can be incorporated into FRCC as a replacement of either the binder or aggregate. Hence this current paper aims to investigate the performance of FRCC made with high volume fly ash as partial replacement of Portland cement, and reclaimed asphalt pavement/crumb rubber as replacement of the natural aggregates. The performance of the FRCC incorporating these recycled materials was evaluated in terms of its crack properties, toughness and sorption. Results from this study showed that the use of recycled crumb rubber is beneficial in terms of crack properties and lower sorption. The sorption of FRCC incorporating crumb rubber as the only aggregate was reduced by 31.8%. However, FRCC made with reclaimed asphalt pavement exhibited higher sorption and lower toughness.
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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) | 0.000 | 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".