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Record W3041586772 · doi:10.3390/buildings10070126

Flammability Characteristics of Green Roofs

2020· article· en· W3041586772 on OpenAlexafffund
Nataliia Gerzhova, Pierre Blanchet, Christian Dagenais, Sylvain Ménard, Jean Côté

Bibliographic record

VenueBuildings · 2020
Typearticle
Languageen
FieldEngineering
TopicFire dynamics and safety research
Canadian institutionsUniversité du Québec à ChicoutimiFPInnovationsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCone calorimeterFlammabilityRoofGreen roofEnvironmental scienceFire performanceAsphaltWaste managementMaterials scienceEnvironmental engineeringComposite materialEngineeringCoalCivil engineering

Abstract

fetched live from OpenAlex

Assessing the fire risk of vegetated roofs includes the determination of their possible contribution to fire. Green roof components such as plants and growing media are organic materials and present a fuel that can catch and support the spread of fire. The flammability characteristics of these components were analyzed and compared to a typical roof covering. Growing media with 15% of organic matter were tested using cone calorimeter apparatus. The fuel load and heat release rate of the growing media were measured in both moist (30%) and dry conditions. It was observed that growing media in a moist condition do not present a fire risk, reaching a maximum heat release rate of 33 kW/m2. For dry substrates, a peak heat release rate of 95 kW/m2 was recorded in the first minute, which then rapidly decreased to 29 kW/m2 in the second minute. Compared to a typical bitumen roof membrane, the green roof showed a better fire performance. The literature data report more severe results for plant behavior, reaching peak heat release rates (HRRs) of 397 kW/m2 for dried and 176 kW/m2 for a green material. However, a rapid decrease in HRR to much lower values occurs in less than 2 min. The results also show that extensive and intensive types of green roofs present 22% and 95% of the additional fire load density when installed on a modified bitumen membrane, 19.7 and 85.8 MJ/m2, respectively.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.757
Threshold uncertainty score0.274

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.209
Teacher spread0.197 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2020
Admission routes2
Has abstractyes

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