Experimental Study on Compressive and Flexural Strengths of High-Strength Pervious Concrete Using Various Binding Materials
Bibliographic record
Abstract
This paper examined the properties of high-strength pervious concrete to investigate the applicability to highways.The properties included porosity, compressive strength, and flexural strength.The high-strength pervious concrete were produced using ordinary Portland cement, silica fume premixed cement, and geopolymer as binding materials.Consequently, the experiment confirmed that the relationship between the compressive strength and flexural strength of pervious concrete using high-strength binding materials (compressive strength of 150 N/mm² or higher) such as silica fume premixed cement (water-cement ratio = 0.15) and geopolymer (solution-powder ratio = 0.5).In addition, it confirmed that their porosity can be approximated by an exponential function, similar to that of pervious concrete using ordinary Portland cement as a binding material.Furthermore, it was observed that pervious concrete using silica fume premixed cement (water-cement ratio = 0.15) as a binder could achieve a compressive strength of 22.5 N/mm² and a flexural strength of 4.5N/mm² up to 20% porosity, emphasizing water permeability.In addition, it was established that pervious concrete using geopolymer as a binder could achieve a compressive strength of 22.5 N/mm² in the high-porosity range of 20-25% and partially meet the flexural strength requirement of 4.5 N/mm², also in the region emphasizing water permeability.These results demonstrate the potential of high-strength pervious concrete for use in highways.
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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 | 0.000 |
| 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".