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Record W2324943694 · doi:10.1007/s10694-016-0581-7

Case Study and Computational Modelling of the Impact of Fire Retardant on Fire Spread for Metal Building Insulation

2016· article· en· W2324943694 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueFire Technology · 2016
Typearticle
Languageen
FieldEngineering
TopicFire dynamics and safety research
Canadian institutionsnot available
Fundersnot available
KeywordsFire retardantCone calorimeterFlammabilityFlame spreadCombustibilityMaterials scienceFire performanceForensic engineeringEnvironmental scienceComposite materialWaste managementCombustionCharEngineeringPyrolysisFire resistance

Abstract

fetched live from OpenAlex

This paper reviews a large fire loss that occurred at a seasonally operated Canadian food-processing facility. The fire occurred when the facility was not in production and started near a work area where employees had been previously unloading a trailer. The origin and cause investigation revealed different metal building insulation (MBI) products were used throughout the building on walls and ceilings. It was suspected that MBI material contributed to the rapid fire spread to otherwise empty parts of the building and that this material did not meet the relevant Building Code requirements. The facility used MBI product consisting of a polypropylene moisture barrier over fiberglass insulation. A detailed analysis of recovered MBI materials found that some of the material was flame retardant and some was not flame retardant. Additional testing of the materials was used to calibrate computational fire model inputs in order to estimate the behavior of MBI coatings by simulating fire scenarios in the full building. The intent of the analysis was to evaluate the relative propensity of the two MBI insulation products to facilitate flame spread from the area of fire origin in a comparative, qualitative framework. Test results showed that flame retardant MBI material substantially reduced fire spread compared with the non-flame retardant material. The ignition temperatures derived from cone calorimeter testing were higher (407°C compared with 226°C) and the peak heat release per unit area was lower for the flame retardant MBI coatings. The non-flame retardant MBI had a measured flame spread rating of 120, which was greater than the maximum flame spread rating of 25 permitted by the Building Code for ceiling finishes. Computational modeling correlates with non-flame retardant coated insulation (noncompliant) being present in the area where the fire originated, facilitating significant fire spread. The model predicted that the presence of non-flame retardant MBI on the ceiling facilitated flame spread across a significant distance from the area of origin within the first 300 s to 400 s, while the flame retardant MBI product yielded minimal flame spread beyond the incident area over a 20 min exposure.

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.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score0.232

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 head0.030
GPT teacher head0.286
Teacher spread0.256 · 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