Optimal passive vibration control of Timoshenko beams with arbitrary boundary conditions traversed by moving loads
Bibliographic record
Abstract
Passive control of vibration of beams subjected to moving loads is studied in which, an optimal tuned mass damper (TMD) system is utilized to suppress the undesirable beam vibration. Timoshenko beam theory is applied to the beam model having three types of boundary conditions, namely, hinged-hinged, hinged-clamped, and the clamped-clamped ends, and the governing equations of motion are solved using the Galerkin method. For every set of boundary conditions, a minimax problem is solved using the sequential quadratic programming method and the optimum values of the frequency and damping ratios for the TMD system are obtained. To show the effectiveness of the designed TMD system, simulations of an actual railway bridge traversed by the S.K.S. Japanese high-speed train are carried out and the dynamic performance of the bridge before and after the installation of the TMD system are compared.
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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".