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Record W3026322993 · doi:10.18280/ijsse.100218

PyroSim-Based Numerical Simulation of Fire Safety and Evacuation Behaviour of College Buildings

2020· article· en· W3026322993 on OpenAlexvenueno aff
Mingbiao Xu

Bibliographic record

VenueInternational Journal of Safety and Security Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicEvacuation and Crowd Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsFire safetyArchitectural engineeringEngineeringEnvironmental scienceForensic engineeringComputer scienceCivil engineering

Abstract

fetched live from OpenAlex

In colleges, fire accident poses a severe threat to the lives and properties of teachers and students, because fire evacuation is difficult due to the dense population, numerous experimental instruments, and various flammables and explosives on campus. This paper explores deep into the fire safety of college buildings, and analyses the types and hazard sources of fire on campus. Then, a building information model (BIM) was established according to the location of fire source, the type of fire growth, and the maximum heat release rate, and applied to fire process simulation on PyroSim and Pathfinder. The simulation results show that the smoke at staircases 1 and 2 on the second floor reached the critical height at 63s and 76s, respectively; despite a relatively long distance from the fire classroom, the smoke at staircase 3 on the second floor reached the critical height at 123s, making the staircase impassable. Among the four evacuation exits, exit I suffered the greatest from the fire, and became impassable since 86s. The other exits and stairways on the first floor were affected but still passable. Except the fire classroom. The average temperature variation on the second floor was 5. After the fire broke out, the total evacuation time increased by 21s. Exit I is the only evacuation exit where the evacuation is faster under fire than under no fire. The research results provide a good reference for fire analysis and fire protection of other buildings.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.362
Threshold uncertainty score0.465

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 head0.007
GPT teacher head0.228
Teacher spread0.222 · 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

Citations19
Published2020
Admission routes1
Has abstractyes

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