Influence of Recycled Concrete Aggregate Type on Rheological Behaviour of Mixtures Proportioned Using the Equivalent Volume Method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The ever-growing urgency to combat climate change has led the civil construction industry to develop and adopt sustainable construction materials and methods. The so-called recycled concrete aggregate (RCA) emerges as an alternative to decrease the carbon footprint of new concrete construction, the disposal of waste concrete, and the use of non-renewable natural resources such as cement and aggregates. RCA can be produced from crushing waste concrete; yet challenges remain when using RCA in concrete especially its fresh state behaviour due to its distinct multi-phase nature and microstructure (i.e., presence of residual mortar (RM)/residual cement paste (RCP)). In this context, this work presents a comprehensive study of the rheological behaviour of recycled concrete mixtures through the use of a planetary rheometer (IBB). The recycled mixtures were proportioned using the Equivalent Volume (EV) method, a mixture proportioning technique that accounts for the RM and RCP, respectively, and improves the recycled mixture's hardened state properties, incorporating distinct: 1) coarse RCA having various inner qualities (i.e., 25 MPa, 35 MPa and 45 MPa) and mineralogy (i.e., limestone and granite) and 2) fine RCA made from natural or manufactured sand while having different degrees of processing (i.e., crushed once vs continuously crushed). All recycled mixtures produced in this study present shear-thinning profiles, suggesting that these mixtures are suitable for applications under high torque regimes such as vibrated or pumped concrete. Additionally, they were produced with 100% recycled concrete aggregate (either fine or coarse RCA), classifying them as low embodied energy mixtures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 it