Cement Dosage and Granular Class as Key Factors in the Properties of Pervious Concrete: A Comprehensive Study
Bibliographic record
Abstract
This study explores the impact of varied cement doses (250, 275, 300, 325, and 350 kg/m³) and granular classes (Dmax of 8, 10, 12.5, and 20 mm) on pervious concrete characteristics.The concrete's fresh and hardened states are examined to identify the ideal cement dosage and granular class for optimal properties.Workability in the fresh state is measured using the slump test and air content analysis.In the hardened state, performance is assessed through water permeability, porosity, density, compressive strength, and electrical resistivity tests.The research reveals that granular class Dmax significantly affects pervious concrete properties.A smaller Dmax and lower cement dosage enhance workability, while in the hardened state, a smaller Dmax combined with higher cement dosage reduces porosity and water permeability and increases mechanical strength and density.The ideal combination of cement dose and granular class varies depending on the specific property under consideration.This study emphasizes the importance of carefully selecting granular class and cement dosage to achieve desired pervious concrete qualities.These findings provide valuable insights for practitioners aiming to enhance the sustainability and resilience of urban infrastructure using pervious concrete.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".