Behaviour of Reinforced Concrete Frames under Various Load Conditions with Pre-Applied Elevated Temperature
Bibliographic record
Abstract
The structural stability of reinforced concrete buildings exposed to fire is gaining very high significance in the design process, as there is increased demand and requirement for fire safe solutions.In a reinforced concrete building, the most critical section is the beam column joint and enhancing its structural behavior was found to be of great significance at ambient and elevated temperatures.This paper reports the behavior of a simple (single bay single storied) reinforced concrete frame under various loading conditions after exposure to elevated temperature (28°C to 800°C).Various loading conditions were applied on the frame specimens after exposing them to higher temperatures to stimulate the actual behavior of the frame after a real fire exposure.The specimens were cast with three different grades of concrete (M20, M45 and M60), representing the normal, standard and high strength concrete as per IS 456: 2000.Performance of the specimens are evaluated and presented in terms of load-deflection hysteresis loop.The results and observations of the study reveal that the frames made with high strength concrete are more prone to earlier failure than the frames cast with standard and normal strength concrete.At a temperature of 400⁰C, the load carrying capacities of the frame cast with M20, M45 and M60 grades of concrete are 80%, 71% and 58% of the capacity at 28⁰C respectively.
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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 | 0.000 |
| 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) | 0.002 | 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".