Prospects of porous concrete as a plant-growing medium and structural component for green roofs: a review
Bibliographic record
Abstract
Abstract Green roof technology can partially mitigate the adverse effects of urbanization by controlling stormwater runoff, pre-filtering water, minimizing climate change outcomes and reducing heat island effects. However, improvements to current green roof systems and innovative approaches are paramount to advancing environmental benefits and consumer acceptance of this technology. Regular green roofs are hindered by high cost and mass, as well as the incorporation of large amounts of polymers. Hydroponic green roofs (HGRs) require specific setups, maintenance and frequent replacement of plant-growing substrate, with limited energy savings in the heating and cooling load of the building due to the space between the roof surface and the hydroponic setup. In this review, a comparison of regular and HGRs is provided, and research into the environmental benefits of these technologies, including stormwater control, water purification and lifecycle assessment, is summarized. Following this, the prospect of porous concrete (PC), as a combined plant-growth substrate and structural layer in a novel extensive hydroponic green roof (EHGR) design is proposed, through a compilation and analysis of recent studies reporting the feasibility of this construction material for different applications. The mechanical, hydrological and vegetative properties of PC are discussed. Finally, a new green roof system that incorporates both PC and hydroponics, termed the EHGR system, is presented. This new green roof system may help offset the effects of urbanization by providing stormwater and pollution control, runoff delay and physical and thermal benefits, while concurrently producing biomass from a reusable substrate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".