Practical method to predict the axial capacity of RC columns exposed to standard fire
Bibliographic record
Abstract
Purpose Existing analytical methods for the evaluation of fire safety of reinforced concrete (RC) structures require extensive knowledge of heat transfer calculations and the finite element method. This paper aims to propose a rational method to predict the axial capacity of RC columns exposed to standard fire. Design/methodology/approach The average temperature distribution along the section height is first predicted for a specific fire scenario. The corresponding distribution of the reduced concrete strength is then integrated to develop expressions to calculate the axial capacity of RC columns exposed to fire from four faces. Findings These expressions provide structural engineers with a rational tool to satisfy the objective-based design clauses specified in the National Code of Canada in lieu of the traditional prescriptive methods. Research limitations/implications The research is limited to standard fire curves and needs to be extended to cover natural fire curves. Originality/value This paper is the first to propose an accurate yet simple method to calculate the axial capacity of columns exposed to standard fire curves. The method can be applied using a simple Excel sheet. It can be further developed to apply to natural fire curves.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".