Effect of the Cross-Sectional Shape on the Dynamic Response of a Cantilever Steel Beam Using Three Modal Analysis Methods
Bibliographic record
Abstract
The main method for determining the vibration characteristics of engineering constructions is modal analysis.It's a way of analyzing a system's mode shapes, natural frequencies, and damping factor.The dynamic response of cantilever beams is determined in this work with different cross-sectional shapes to find the effect of eccentricity on the dynamic response of the cantilever beam.The main goal of this research is to find and detect the natural frequencies and mode shapes of a Structural Steel cantilever beam with different eccentricities and to identify flexural or torsional natural frequencies, as well as their mode shapes that could be confused with transverse natural frequencies, and to compare the results with analytical and experimental methodologies.Results showed that torsional natural frequencies remained within the transverse natural frequency.It can be shown that, the increase of eccentricity in the cross section decreases the natural frequencies and especially the torsional natural frequencies.The results were compared experimentally and numerically using ANSYS 16.1 software.There is a strong link between the mathematical, FEA, and experimental results.The latest results can be used to calculate failure loads in a variety of situations.The mathematical application of Euler's Bernoulli's beam concept was applied.The results of the three ways have been declared satisfactory.
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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 | 0.002 |
| 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".