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Record W4224100252 · doi:10.1061/9780784484180.028

Investigation of Fire Incidents and Associated Damage to Buildings

2022· article· en· W4224100252 on OpenAlexaffabout
M. Mehdi Mirzazadeh, Mark P. Milner

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFire dynamics and safety research
Canadian institutionsConcordia University
Fundersnot available
KeywordsRoofForensic engineeringJoistFire safetyFire protectionFire resistanceArchitectural engineeringEngineeringCivil engineeringEnvironmental science

Abstract

fetched live from OpenAlex

Among different types of man-made and nature-made disasters, fire constitutes a significant threat to both people and structures. This study provides fire incident data including the origin and cause, as well as the extent and severity of real fire incidents that occurred in around 60 wood–frame residential buildings in the province of Ontario, Canada. Analysis of the data showed that most of the fires in the residential building were electrical fires; however, the causes for many of the fires were reported as “undetermined.” The primary places of origin of the fires were found to be the garages and kitchens. Sunday was the day of the week with the highest number of fire incidents. It was observed that floor joists and roof framings were the most vulnerable structural elements, and poor fire performance of I-joists often resulted in total loss. To protect floor framings from fire, it is recommended that the exceptions to the provisions of section R302.13 of the 2015/2018 IRC be eliminated so that all types and sizes of floor framings be covered with gypsum boards. Similar provisions shall be added to Part 9 of NBC as well as OBC and other Canadian provincial building codes. It is also recommended that I-joist floor systems and roof framings be sealed with a fire-protective coating after installation.

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 score0.595
Threshold uncertainty score0.150

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 head0.011
GPT teacher head0.222
Teacher spread0.211 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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