Bond of Reinforcement in Concrete Incorporating Recycled Concrete Aggregates
Bibliographic record
Abstract
Using recycled concrete aggregates (RCAs) as coarse aggregate in concrete has the potential to supplement current natural aggregate reserves, divert construction and demolition debris from landfills, and promote the adoption of sustainable civil infrastructure. As many of the design equations used to calculate structural concrete properties are based on empirical data for natural aggregate concrete, using these equations for RCA concrete may not be applicable. This study examined the bond of reinforcement in concrete produced using RCA as coarse aggregate. One natural aggregate (NA) and three RCA sources were evaluated and used as coarse aggregate in 14 separate concrete mixtures with four compressive strength levels. Various concrete mechanical properties, including compressive strength, splitting tensile strength, modulus of rupture, and fracture energy, were tested, and correlations between these properties and reinforcement bond were studied. The reinforcement bond was measured using 48 beam-end specimens incorporating several bonded lengths. The results showed that bond strength of RCA concrete was reduced by up to 21% in comparison to NA concrete, and that there was a strong correlation between bond strength and coarse aggregate crushing strength. A regression model was developed to relate bond strength to coarse aggregate strength, concrete compressive strength, and the bonded length. Using this model, experimentally predicted development lengths were calculated to be up to 9% greater for RCA concrete members in comparison to NA concrete members. Overall, this study was directed at providing guidance on the evaluation of multiple RCA sources and their respective impact on the bond of reinforcement in structural 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 | 0.001 |
| 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".