Numerical Study on the Impact Response of Steel Beams with Large Web Openings: Investigating Key Parameters
Bibliographic record
Abstract
The absence of specific guidelines to enhance the integrity of structural members to resist accidental loading such as impact, explosion and fire highly motivated the researchers to cover such a knowledge gap.In the current study, one of the most common structural members that used widely in structural frames named steel beams with large web openings (SBLWOs) was numerically investigated under impact load.Non-linear finite element (FE) models were created using ABAQUS software and validated against existing experimental data from the literature.The FE models developed considered the dynamic material properties in the elastic, plastic, and damage stage.Strain rate effect was also taking into account in the models.Afterwards, intensive parametric analyses of the parameters that affect the behavior of SBLWOs were performed including impact energy, impact location, and opening strengthening.The correlation outcomes of the FE and the experimental tests were in a good agreement in terms of force and displacement time histories and failure modes.The results showed that the SBLWOs were able to resist the impact with higher velocity rather than higher mass.Regarding the effect of impact location, the worst case was found to be when a cellular steel beam impacted close to the supports.Finally, the contribution of providing steel stiffeners in the impact zone resulted in a significant improvement in the shear and web buckling resistance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".