Permeable Pavement Hydrological Model to Assess the Long-Term Efficiency of Maintenance Using High-Resolution Temperature and Rainfall Data
Bibliographic record
Abstract
Permeable pavements (PP) are one of the most flexible stormwater facilities to mimic pre-development flow conditions in urbanized areas. They provide multiple benefits as (1) runoff storage and slowly release, (2) pollutant treatment, and (3) reduce heat island effect due to evaporative cooling. Assessing PP long-term efficiency requires a continuous model to simulate flow routing, evaporation, clogging, and maintenance using high-frequency rainfall and temperature data. The purpose of this article is to develop a PP continuous model and assess the trade-offs among permeable asphalt (PA), permeable concrete (PC), and permeable interlocking concrete pavers (PICP) using the observed climate of San Antonio, Texas, for 1975–2000. The objective functions to assess the long-term effectiveness were the (1) month average treated volume, (2) month average evaporated volume, and (3) drainage layer cost. Solutions were assessed for different scenarios of diameter and number of underdrains and drainage layer depth (i.e., cost), as well as the type of the pavement. Results indicate that PA and PC have nearly the same long-term efficiency, and PICP typically provides less evaporation but can provide more treated volume for more expensive solutions. A PP design with a drainage layer of 30 cm and a 4-in. underdrain was assessed, and results indicate that it can provide, in average, 45 mm of treated volume in October. For this design, no significant difference in seasonality performance was found varying the type of the pavement, indicating that it may only provide marginal benefits for designs with relatively small drainage layer depths.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.003 |
| 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 teacher head, 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".