Evaluating the Use of Recycled Concrete Aggregate in Pervious Concrete Pavement
Bibliographic record
Abstract
Pervious concrete with minimal fine aggregate and a high void content is a green alternative to conventional pavements. Pervious concrete allows water to infiltrate through the pavement and thereby reduces the requirement for storm water management systems. Virgin aggregate sources within Canada are limited, and other sources need to be evaluated for use. Recycled concrete aggregate (RCA) is obtained from decommissioned curbs and gutters, sidewalks, and parking lots. Using RCA in new concrete offers several environmental advantages: reducing dumping at landfill sites, reducing gravel mining, and reducing hauling of virgin aggregate and therefore reducing emissions. The purpose of a research study was to incorporate RCA into pervious concrete to create a very sustainable concrete product for paving. The research methodology involved substituting the coarse aggregate in the pervious concrete with 15%, 30%, 50%, and 100% RCA. Cylinders were cast in the laboratory for each percentage of RCA and a control mix containing only virgin aggregate. Fresh concrete tests were done, and the cylinders were tested for compressive strength, permeability, and void content. Testing showed that pervious concrete containing 15% RCA had strength, permeability, and void content that were very similar to those of the control mix. Samples that contained 30% RCA or greater had a significant loss in strength and increase in permeability and void content. Based on the specific mix design and RCA quality used in this research, the recommendation is that the optimum percentage of RCA in pervious concrete be 15% direct replacement of virgin coarse aggregate.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".