Effect of Carbonation-Treated Recycled Aggregates on the Properties of Permeable Geopolymer Concrete
Bibliographic record
Abstract
To address technical challenges such as the high water absorption of recycled aggregates (RA) and the lowmechanical performance of permeable concrete, this study systematically investigates the optimization of carbonationmodification processes for RA and their coupling mechanisms with geopolymer binder systems. By adjusting COconcentration and environmental humidity, the densification of RA was enhanced, resulting in a 24.6% reduction in waterabsorption and a 15.49% decrease in crushing value, significantly improving their applicability in green permeable concreteExperimental results demonstrated that carbonation treatment induced in situ deposition of calcium carbonate on aggregatesurfaces, effectively sealing microcracks and enhancing interfacial properties. The geopolymer concrete exhibited excellentpermeability and mechanical strength, with a maximum compressive strength of 52.1 Pa, while maintaining performanceconsistency under controled porosity (minor deviation from desien valuesl. Further analvsis revealed that ontimizecbinder compositions mitigated strength degradation caused by increased porosity and compensated for interfacial bondingdeficiencies in RA. Freeze-thaw cycle tests indicated that RA-based specimens exhibited poor durability, with accelerateddeterioration at higher porosity, while optimized binder formulations significantly enhanced frost resistance. A regressionmodel was established to quantify the effects of porosity and aggregate type on durability.
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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 | 0.000 | 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) | 0.001 | 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".