Parametric study on load ratio effect on the flexural bending behaviour of axially-restrained HSS steel beams subjected to fire
Bibliographic record
Abstract
Purpose In fire condition, the limiting temperature of a restrained steel beam depends on a few parameters, e.g. temperature distributions along and across the beam, beam’s load ratio and span length. The purpose of this study is to investigate the structural fire behaviour of axially restrained steel beams under different beam’s load ratios, taking into consideration the effect of the beam’s end connections configuration. Design/methodology/approach A three-dimensional finite element (FE) computer model has been developed to simulate the structural fire behaviour of axially restrained steel beams and their end connections. After successfully validating the developed model against the outcomes of the available large-size fire resistance experiments, the FE model has been used in a parametric study to investigate the beam’s load ratio effect on the behaviour of the axially restrained steel beams and their end connections. Findings The parametric study showed that increasing the beam loading level significantly increased the beam deflections at elevated temperatures; where, increasing the beam’s load ratio from 0.5 to 0.9 reduced the beam fire resistance by about 100 s. In contrast, decreasing the beam’s load ratio from 0.5 to 0.3 allowed the beam to easily achieve a 30-min fire resistance rating with no fire protection applied. Originality/value Experimental parametric studies are difficult to control in a laboratory setting and are also expensive and time consuming. Therefore, the reasonable accuracy of the validated FE model in reproducing the experimental fire behaviour of steel beams and their end connections makes it a very useful tool for both numerical and analytical studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".