A Performance-based approach for fire-resistance test of reinforced concrete columns
Bibliographic record
Abstract
This paper describes a simple performance-based approach for estimating fire-resistance of reinforced concrete columns, within a building frame, by considering the effect of the structural system and thermal expansion phenomenon. The attempt was made to develop an analytical tool for application of the new performance-based design philosophy, using evaluation of structural performance in fire, considering the entire structural system rather than individual elements. This method describes how to determine fire performance of a reinforced concrete column when it is part of a structural frame. In this approach, the entire frame is simplified into a single equivalent spring and coupled with the column. The column is then exposed to fire and, according to the column deformation and the equivalent frame spring stiffness, the test load, restraint force, is determined. Frames with different numbers of stories and height were selected for the analysis. These were modeled using a structural analysis program, the SAFIR program, and the outcomes were compared with those of the simplified approach, developed through this study, resulting in a consistent agreement. An example including the analytical process is presented for one of the reinforced concrete column tests. The results from this study show that the simple performance-based approach is a suitable and easy to use tool for fire-resistance assessment of reinforced concrete columns. This research report provides theoretical concept and formulation of the new test process. For application in practice, the model needs to 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".