On the inclusion of travelling fire scenarios in the fire safety design process of a building
Bibliographic record
Abstract
Abstract Travelling fires (TFs) have been increasingly observed in large-scale fire events, prompting the development of analytical models to characterise their behaviour based on experimental studies and computational fluid dynamics (CFD) simulations. However, they are not commonly considered in a fire safety design process to date. This study describes a three-stage methodology to systematically do so, after reviewing the limitations of TF models. The first stage involves determining whether the scenario of a TF is more probable than that of a fully-developed fire, which would alter the predicted thermal exposure of the structure, and depends on a series of key parameters. These include geometric factors such as floor area, ceiling height, opening factor, and fuel load density within the compartment. In this study, these values are selected based on insights from existing literature. The second stage focuses on characterising the potential worst-case travelling fire (WCTF), which is influenced by the same parameters. The final stage would be to employ CFD models to more accurately assess fire behaviour. This paper addresses the first two stages of the design methodology, highlighting its contribution in discovering the WCTF with practical analytical methods for fire safety engineering.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".