Heat Transfer Behavior of Green Roof Systems under Fire Condition: A Numerical Study
Bibliographic record
Abstract
Currently, green roof fire risks are not clearly defined. This is because the problem is still not well understood, which raises concerns. The possibility of plants catching fire, especially during drought periods, is one of the reasons for necessary protection measures. The potential fire hazard for roof decks covered with vegetation has not yet been fully explored. The present study analyzes the performance of green roofs in extreme heat conditions by simulating a heat transfer process through the assembly. The main objective of this study was to determine the conditions and time required for the roof deck to reach a critical temperature. The effects of growing medium layer thickness (between 3 and 10 cm), porosity (0.5 to 0.7), and heating intensity (50, 100, 150, and 200 kW/m2) were examined. It was found that a green roof can protect a wooden roof deck from igniting with only 3 cm of soil coverage when exposed to severe heat fluxes for at least 25 minutes. The dependency of failure time on substrate thickness decreases with increasing heating load. It was also found that substrate porosity has a low impact on time to failure, and only at high heating loads.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".