Pervious Concrete: Mix Design, Properties and Applications
Bibliographic record
Abstract
Current climatic changes are occurring due to various human and industrial activities. In particular, the effects of urbanisation and growing threat of global warming have likely caused increasing precipitation in many geographic regions. For many years, portland cement pervious concrete (PCPC) has been making an important contribution, as a sustainable urban drainage system (SUDS), on improving environmental conditions. This type of porous concrete can help minimizing flooding risks, recharging ground water, reducing run off and peak flows, alleviating the precipitation load on overstressed drainage systems and improving water quality by capturing pollutants. The benefits of using PCPC in order to attenuate stormwater problems are quite essential mainly in urban areas where most surfaces typically consist of relatively impervious concrete or asphalt pavements, causing elevated levels of surface runoff. In addition, PCPC can reduce the absorption of solar radiation and urban heat storage potential which can lead to temperate urban conditions, and thus protecting the environment and health and safety of living things. However, PCPC requires regular maintenance to prevent any clogging of the pores by sediments and vegetation. This article provides an overview on pervious concrete mix design, key properties, durability and applications. Also, it touches on practical and scientific challenges of PCPC.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".