A Parametric Study of Fire Risks of Green Roofs to Adjacent Buildings
Bibliographic record
Abstract
The susceptibility of plants to burn raises concerns about fire hazard that green roofs may pose to buildings. Main concerns relate to cases when such roofs are poorly maintained or stressed by drought conditions which leads to drying out of plants and the accumulation of dead organic material, greatly increasing the availability of fuel load. Existing standard safety measures aim to prevent the spread of fire through the vegetation cover. However, fire spread by thermal radiation is not considered. In this study, fire risk of exposure of adjacent buildings to radiant heat flux produced by fire on green roofs was assessed. Based on generally accepted maximum tolerable radiant heat flux to exposed facades of 12.5 kW/m2, the minimum safe separation distances were obtained for different conditions. Wildland fire behavior model was used to determine flame lengths which is the necessary parameter for a radiation model. Several vegetation types, moisture content scenarios and wind speeds were taken as variables. It was found that by providing the vegetation with reasonably high moisture content the fire risk can be greatly reduced, especially for grass-covered roofs. Since wind also has a strong effect on flame size, considering the exposure of a green roof to wind can bring better understanding of fire risk to adjacent buildings. At no-wind condition and at extremely low moisture content separation distances are as short as 3.1 m for dense shrubs and 2.4 m for tall dense grass.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".