A new testing capability for seismic resistance assessment of structures damaged due to a fire
Bibliographic record
Abstract
This paper provides results of a hybrid structural test that simulates residual lateral load resistance of a structure after fire damage. The simulation was carried out at the National Research Council of Canada (NRC) using the NRC’s furnace facilities. The hybrid test included two substructures; a structural element/assembly specimen, here a column specimen, and a model component, here the remaining of the structure. Using this method, first, fire damage was imposed to a structure, a 6-storey reinforced concrete building, and six days later, after the entire structure had cooled down to the ambient temperature, the building was subjected to a lateral load, which was determined based on a design seismic load. The test results showed a reduction of both residual lateral stiffness and residual lateral load capacity of the structure after the fire damage. This paper will present the hybrid test, its application and the results for the 6-storey building.
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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".