Analysis of Anti-Blast Performance of Lightweight Steel Columns Subjected to Elevated Temperatures
Bibliographic record
Abstract
The lightweight steel column (LSC) plays an important role in resisting the combined action of fire and blast for lightweight steel structures. The material performance of LSC is more sensitive to these combined effects than other forms of steel structures. Thermal softening of the steel material under elevated temperatures makes the steel weaker under blasting loading. However, very limited researches have been conducted to analyze the anti-blast performance of LSC subjected to elevated temperatures, though these two issues have been systematically investigated separately. In this paper, anti-blast performance of LSCs with different cross-sections, namely H-type, rectangle and round sections, are numerically analyzed using ANSYS/LS-DYNA. Isothermal property is assumed under elevated temperatures and the blasting loading is applied on the side surface of the LSCs when establishing the finite element analysis (FEA) models. The failure mode and severity of the LSCs are investigated from the numerical results to study the performance of LSC due to different temperatures, blast loadings and cross-sections. Results show that the LSC with short side facing the blast, has the best anti-blast capacity in high temperatures while the LSC with circular section is the worst. The results from this study may provide useful reference for the design of the structures under the combined effect of elevated temperatures and blast loading.
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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 | 0.001 | 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".