Performance-based design of RC beams using an equivalent standard fire
Bibliographic record
Abstract
Purpose The design of buildings for fire events is essential to ensure occupant safety. Supplementary to simple prescriptive methods, performance-based fire design can be applied to achieve a greater level of safety and flexibility in design. To make performance-based fire design more accessible, a time-equivalent method can be used to approximate a given natural fire event using a single standard fire with a specific duration. Doing so allows for natural fire events to be linked to the wealth of existing data from the standard fire scenario. The purpose of this paper is to review and assess the application of an existing time-equivalent method in the performance-based design of reinforced concrete (RC) beams. Design/methodology/approach The assessment is established by computationally developing the moment-curvature response of RC beam sections during fire exposure. The sectional response due to natural fire and time equivalent fire are compared. Findings It is shown that the examined time equivalent method is able to predict the sectional response with suitable accuracy for performance-based design purposes. Originality/value The research is the first to provide a comprehensive evaluation of the moment-curvature diagram of RC beams using time-equivalent standard fire scenarios that model realistic fire scenarios.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".