Fire Risk Analysis of Combustible and Non-combustible Mid-rise Residential Buildings using CUrisk
Bibliographic record
Abstract
Life safety and property protection are the two main objectives of a performance-based design.At Carleton University in Canada, a quantitative fire risk analysis computer model CUrisk is being developed to evaluate the fire risk levels in mid-rise buildings.By using this model, this thesis evaluated the fire safety in multi-storey buildings with different building construction materials, heights and floor areas.The CUrisk Evacuation submodel is compared with other methods including the Pathfinder models and the Society of Fire Protection Engineers (SFPE) analytical calculations.Case studies were performed by applying these models to different building design conditions and the results were compared.The comparisons showed that the CUrisk Evacuation submodel produce results comparable to those of the other models.CUrisk was applied to evaluate the fire risk level in buildings of non-combustible frame and combustible frame.Fire development in concrete, unprotected CLT, protected CLT and light-frame timber were compared and the performance of different fire protection systems was evaluated.Finally, the fire risks in buildings with higher building height or larger building area were compared.The results of this study show that CUrisk is an effective model to assess fire risks in multi-storey buildings.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".