Re-tuning tuned mass dampers using ambient vibration measurements
Bibliographic record
Abstract
Deterioration, accidental changes in the operating conditions, or incorrect estimates of the structure modal properties lead to de-tuning in tuned mass dampers (TMDs). To restore optimal performance, it is necessary to estimate the modal properties of the system, and re-tune the TMD to its optimal state. The presence of closely spaced modes and a relatively large amount of damping in the dominant modes renders the process of identification difficult. Furthermore, the process of estimating the modal properties of the bare structure using ambient vibration measurements of the structure with the TMD is challenging. In order to overcome these challenges, a novel identification and re-tuning algorithm is proposed. The process of identification consists of empirical mode decomposition to separate the closely spaced modes, followed by the blind identification of the remaining modes. Algorithms for estimating the fundamental frequency and the mode shape of the primary structure necessary for re-tuning the TMD are proposed. Experimental results from the application of the proposed algorithms to identify and re-tune a laboratory structure TMD system are presented.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".