Case Study and Computational Modelling of the Impact of Fire Retardant on Fire Spread for Metal Building Insulation
Bibliographic record
Abstract
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.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".