Hybrid fire testing of concrete-filled steel tube columns: A large-scale experimental and numerical investigation
Bibliographic record
Abstract
Hybrid Fire Testing (HFT) integrates experimental fire testing with numerical simulations to better capture the interaction between a physical sample in a furnace fire and the structure that would surround it in real-world conditions. Unlike traditional fire testing, HFT enables a more accurate analysis of complex structural behaviours by simulating the effect of adjacent structural elements, thus providing a realistic assessment of a structure's fire performance. This paper presents a full-scale experimental hybrid fire test of a concrete-filled steel tube (CFST) column using an advanced HFT framework developed in previous research. A three-story, four-bay structure with the steel moment-resisting frame was selected for the validation. One column of the structure was physically represented in the laboratory, while the remainder of the structure was modelled numerically through finite element software. The physical specimen was heated following a standard fire curve. The experimental results are compared with numerical predictions and fire resistance tests of a similar single column to validate the performance of the developed method in full-scale applications. This comparison also provides insight into the performance of the column when acting as part of the larger structural system. The test results confirmed the proposed method can accurately simulate the complicated behaviour of a CFST column at high temperatures and subsequent failure. • HFT provides realistic, cost-effective fire safety insights compared to standard methods. • Tested CFST column experienced 80 % load increase during the HFT, no failure after 120 min. • Residual column strength was 3840 kN. • Load redistribution led to zero column force 40 min after the fire was stopped. • HFT closely matched numerical predictions, validating the hybrid testing framework. Abstract.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".