Influence of Interparticle Separation Distance on the Fresh and Hardened Behavior of Ecoefficient Cement Pastes
Bibliographic record
Abstract
An efficient method to produce ecoefficient cementitious mixtures is using particle packing models (PPMs) combined with limestone fillers (LF). Yet, key outcomes obtained in the fresh and hardened states, including slump and compressive strength, are variable and remain mostly not fully understood. Therefore, in this work, the concept of interparticle separation distance (IPS) is employed to describe the overall performance of ecoefficient cement paste mixtures with high LF dosages [up to 81% of ordinary Portland cement (OPC) replacement]. In this scenario, twenty-one cement paste mixtures displaying three water-to-powder ratios (0.32, 0.40, and 0.50), two distribution factors (q=0.21 and 0.37), and three cement contents (100, 150, and 250 kg/m3) were studied. Next, fresh-state properties (i.e., rheological profile and slump flow over time) along with compressive strength were appraised. Densely packed systems containing moderate to high LF dosages have shown to yield better compressive strength results than pure OPC mixtures, yet the inclusion of LF negatively influenced the cement paste mixtures’ slump loss. Thus, the w/p (i.e., in mass) combined with the w/c ratio from Abram’s law was successfully observed as an important parameter to better predict the compressive strength performance of the ecoefficient cement pastes. Results suggest that the fresh state behavior of packed mixtures containing high LF content can be predicted through the IPS/dp; therefore, this parameter might be adjusted to reach targeted viscosity values when proportioning cementitious materials. Finally, the concept of IPScement is proposed to precisely describe fresh and hardened properties of cementitious mixtures with significant dosages of LF and reduced OPC 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 | 0.000 | 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) | 0.000 | 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 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".