Review of the <scp>NRC</scp> Canada studies on fire resistance of floor assemblies: Results, design guidelines and research gaps
Bibliographic record
Abstract
Abstract The National Research Council Canada conducted two major fire resistance studies on floor assemblies over the past two decades. Despite the publication of the experimental results, there is a lack of suggested guidelines for design practitioners and gaps for future research. Thus, this paper comprehensively reviews the fire resistance results of 85 full‐scale floor tests, suggests design guidelines, and identifies research gaps. These efforts aim to enhance the understanding and support the potential improvement of the fire performance of floor assemblies. The review of the results covers the impact of various design parameters on the fire resistance of floor assemblies, such as framing type and spacing, insulation type, subfloor configuration, resilient channel spacing, number of gypsum board layers, and screw spacing from the board edge. Although the interaction of these factors is complex, some of them play significant roles in determining the overall fire resistance of floor assemblies. For instance, rock and cellulose insulation outperformed glass fibre, a wider resilient channel spacing lowered fire resistance, whilst an increased distance of screws from the board edge improved the fire resistance. More importantly, detailed explanations are provided for the influences these parameters exert on fire resistance. Following this detailed examination of the results, design guidelines are provided for practitioners' consideration. A comparison is made between the experimental results and predictions from the component additive methods in the Canadian and Euro Codes, demonstrating that both methods yield conservative results. Finally, this paper concludes by identifying research gaps and providing recommendations for future investigations, including the necessity of experimental studies on floor assemblies with new design configurations and the promising role of machine learning in fire resistance evaluation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".