Influence of the Mix Proportion and Aggregate Features on the Performance of Eco-Efficient Fine Recycled Concrete Aggregate Mixtures
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
Most of the previous research on recycled concrete aggregates (RCA) has focused on coarse RCA (CRCA), while much less has been accomplished on the use of fine RCA particles (FRCA). Furthermore, most RCA research disregards its unique microstructure, and thus the inferior performance of concrete incorporating RCA is often reported in the fresh and hardened states. To improve the overall behaviour of RCA concrete advanced mix design techniques such as equivalent volume (EV) or particle packing models (PPMs) may be used. However, the efficiency of these procedures to proportion eco-efficient FRCA concrete still requires further investigation. This work evaluates the overall fresh (i.e., slump and rheological characterization) and hardened states (i.e., non-destructive tests, compressive strength and microscopy) performance of sustainable FRCA mixtures proportioned through distinct techniques (i.e., direct replacement, EV and PPMs) and incorporating different types of aggregates (i.e., natural and manufactured sand) and manufacturing processes (i.e., crusher fines and fully ground). Results demonstrate that the aggregate type and crushing process may influence the FRCA particles' features. Yet, the use of advanced mix design techniques, particularly PPMs, may provide FRCA mixes with quite suitable performance in the fresh (i.e., 49% lower yield stress) and hardened states (i.e., 53% higher compressive strength) along with a low carbon footprint.
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