Green Roofs with Construction and Demolition (C&D) Waste in Urban Stormwater Retention
Bibliographic record
Abstract
Green roofs are built with different layers and variable thicknesses depending on the roof type and/or weather conditions. Basic layers, from bottom to top, of green roof systems usually consists of a root barrier, drainage, filter, growing medium, and vegetation layer. Environmental and operational benefits of green roofs are many. Vegetative roofs must be installed on existing structures to maximize their potential environmental benefits; however, their main disadvantages are cost and weight. New technology enabled the use of light materials such as: low density polyethylene and polypropylene (polymers) to promote their installation. Nevertheless, lifecycle analyses demonstrate that more sustainable products must replace current green roof materials. This paper evaluates water retention performance of green roofs built with construction and demolition (C&D) waste. Storm water retention of C&D based green roofs were assessed experimentally by comparing the rainwater retained in C&D based green roofs with standard green roofs, under the same environmental conditions. Additionally, basic water quality tests were conducted to compare the runoff quality with the rainwater quality. Results show that C&D waste, compared to plastics, reduces the water retention performance. However, the runoff water quality is better in terms of electro-conductivity, pH, and turbidity. The benefits of installing vegetative roofs in the regions with high rainfall and flood risk are potentially enormous.
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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) | 0.001 | 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".