Effect of Recycled Concrete Aggregate Properties on Mixture Proportions of Structural Concrete
Bibliographic record
Abstract
This study focuses on characterizing several recycled concrete aggregate (RCA) sources, developing concrete mixture proportions that incorporate RCA as coarse aggregate, and investigating the effect of coarse aggregate properties on the main mixture proportion parameters [i.e., cement content, water demand, and water–cement (w/c) ratio]. Four aggregate types were investigated: one control virgin aggregate source and three RCAs produced from the crushing of hardened concrete. Numerous aggregate tests, including density, absorption, abrasion resistance, adhered mortar content, and crushing value, were performed. Fourteen mixture proportions were developed with the use of three mixture proportion scenarios (control, direct replacement, and strength based) and two compressive strength levels (40 and 60 MPa). The effect of RCA on compressive strength and workability was evaluated by replacement of natural coarse aggregate with RCA. Contrary to numerous studies, one of the RCA concretes (RCA-1) had compressive strengths up to 12% higher than the equivalent control mixture. Mixture proportions (water, cement, and w/c ratio) were later adjusted to ensure that the RCA concretes had compressive strength and slump values similar to the control concretes. Variations in water demand, cement content, and w/c ratio could then be directly attributed to the properties of the RCA source. RCA-1 concrete required less cement (and a higher w/c ratio) to achieve strengths and slumps similar to the control concrete. The findings and recommendations of this research will assist concrete producers, engineers, and field technicians involved in the selection of RCA sources in developing mixture proportions for structural-grade RCA 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".