A hybrid fire-resistance test method for steel columns
Bibliographic record
Abstract
This paper describes a simple hybrid approach for estimating fire-resistance of steel columns within a building frame. This approach includes the effects of the structural system and thermal expansion phenomenon. In this technique, a steel column is tested in fire in a furnace while the structural system is modeled using computer software. Response of the steel column from the fire test and response of the structural system from the analysis are coupled according to the compatibility and equilibrium condition using a sub-structuring method. A real time interaction is implemented between the structural system response and the steel column response. Two analytical approaches are described in this report for evaluation of the structural system response; a simplified method and a full-structural analysis. In the first method, the entire frame is simplified into a single equivalent spring coupled with the column. The spring is an analytical model which is defined in the form of a load-displacement curve. The column specimen is then exposed to fire using a column furnace test facility and loaded according to the obtained load-displacement curve. The second method uses structural analysis software to determine the load-displacement relation. More efforts were extended to the simplified method in this study, since it is more applicable for practice. Frames with differing numbers of stories and heights were selected for the analysis. A comparison was undertaken between the results of the simplified method and that of the full-analysis approach resulting in a consistent agreement. This research report provides the theoretical concept and formulation of the simple hybrid test approach. Before application in practice, the model should be verified through a future experimental program.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".