Investigation on the Properties and Distribution of Air Voids in Porous Asphalt with Relevance to the Pb(II) Removal Performance
Bibliographic record
Abstract
In this study, one type of porous asphalt (PA) mixture was designed at two different air void (AV) contents. Direct information regarding both the dimensional properties and AV distribution within two PA specimens in their natural states were observed, compared, and analyzed based on the X-ray CT (computed tomography) and digital image analysis techniques and fractal geometry theory. The characteristics of AV distribution, including AV content, AV number, and AV volume were quantified by comparing pore structure at different specimen heights. The dimensional properties of voids at different specimen heights were characterized by parameters in terms of equivalent diameter, specific surface area, and fractal dimension. The Pb(II) removal performance of the two PA mixtures was examined by an artificial rainfall event. The relationship between the PA microstructure and Pb(II) removal rate was then determined. It is observed from this study that the overall distribution pattern of AV inside PA specimen is independent of AV content. The distributional parameters including AV content, AV number, and AV volume show good correlations with three dimensional parameters, respectively. The Pb(II) removal in general appears to be independent of AV content because both PA mixtures have the same level of performance in removing Pb(II) during the rainfall period. The removal rate of Pb(II) shows good correlation with AV content, AV number, AV volume, and equivalent diameter of AV, indicating that a perfect distribution of AV within PA mixture is more important than the complex geometries of voids for getting a higher Pb(II) removal during rainfall event and without providing a temporary storage of stormwater in PA mixture. These findings provide an effective evidence to reveal that the AV distribution and properties characteristics have significant implications for pollutant removal in field PA pavement system.
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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) | 0.000 | 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".