Lateral Torsional Buckling Resistance and Design of High Strength Steel Beams Subjected to Nonuniform Temperature
Bibliographic record
Abstract
The mechanical characteristics of high-strength steel (HSS) at high temperatures vary significantly from those of mild steel, so fire design regulations derived from mild steel cannot be transferred to HSS structures. Therefore, this paper investigates the fire resistance of beams made of HSSs, which will facilitate the design and application of HSS structures. Due to the heat dissipation of the concrete floor, steel beams are generally heated from three sides, and the temperature distribution in the steel beam cross-section is nonuniform. For such cases, the fire resistance of HSS beams is assessed using finite element models (FEMs) with nonuniform temperature distribution. A Ramberg–Osgood model was used to predict stress–strain relationships of Q460, Q690, and Q960 HSSs at elevated temperatures, and the model was verified by material tests. In the FEM, the proposed stress–strain relationships of HSSs at elevated temperatures, initial geometric imperfections and residual stress are considered for better accuracy. After verifying the established model against experimental results, the influence of nonuniform temperature distribution, load pattern, steel grade, and slenderness ratio on the resistance of HSS beams is investigated by conducting parametric analyses using the verified FEM. It is found that the critical bending moment of steel beams at elevated temperatures associated with lateral-torsional buckling highly depends on the reduction of elastic modulus of steel. By comparing FEM results with EN 1993-1-2, it is evident that EN 1993-1-2 is unsuitable for assessing the fire resistance of HSS beams. Therefore, the expression specified in EN 1993-1-2 is modified by fitting the finite analysis results to more accurately evaluate the fire resistance of HSS beams considering the nonuniform temperature distribution.
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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) | 0.001 | 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".