Investigation of the use of continuous particle packing models (PPMs) on the fresh and hardened properties of low-cement concrete (LCC) systems
Bibliographic record
Abstract
Concrete, the major construction material used in the construction industry worldwide, presents a huge environmental impact producing about 7% of the global carbon dioxide. Given the concerns related to global warming, studies have been focusing on distinct approaches aiming to reduce the amount of Portland cement (PC), which is the least sustainable ingredient of the mixture, by adopting alternative mix-design strategies such as the use of particle packing models (PPMs). However, there is currently a lack of data on the efficiency of the use of continuous PPMs to reduce PC while maintaining or improving concrete fresh and/or hardened properties. This work aims to investigate the impact of current and modified versions of continuous PPMs on the fresh (rheological behaviour) and hardened (compressive strength, modulus of elasticity, porosity, and permeability) state of mixtures designed with low and moderate amounts of PC. Results demonstrate that eco-efficient concrete may be produced through current and modified continuous PPMs without compromising the fresh and hardened properties of the material. Nevertheless, further durability and long-term investigations must be performed on systems with reduced PC contents.
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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 | 0.001 |
| 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".