Hybrid fire testing of building structures
Bibliographic record
Abstract
A performance-based fire resistance evaluation method, called hybrid fire testing (HFT), was developed and carried out at the Fire Research Program of the National Research Council of Canada (NRC). HFT offers a more cost-effective approach to the assessment of the full structures performance in fire than that of a full-scale test and provides more reliable results than prescriptive single component testing. In the HFT method, the whole building is divided into two substructures; 1) the test specimen and 2) the model component. Then, fire performance of the whole structure is evaluated based on coupling the performance of the two substructures and by including their interactions in real time during the simulation. In this study, the HFT was applied for a 6-storey reinforced concrete building structure with a fire compartment scenario on the first floor. The test specimen was a worst-case scenario column in the compartment of fire origin in the building. The specimen was physically tested in a full-scale furnace, and the remaining of the building structure was modeled using numerical analysis software. In the HFT, the real time interactions between the two substructures are based not only on including the experimental results obtained from the test specimen into the numerical model component but most importantly also including feeding the output of the model component back into the test specimen environment (e.g. vertical and lateral loading changes). In other words, both equilibrium and compatibility conditions are satisfied between the test specimen and the model component. The results of this study show that the HFT is achievable and can be performed successfully for fire performance evaluation of a building structure.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".