Condition Assessment of an In-Service Pendulum Tuned Mass Damper
Bibliographic record
Abstract
The performance of passive tuned mass dampers (TMDs) has generally been assessed by parametric studies using numerical models, primarily with the purpose of selecting optimal damper parameters. Alternatively, limited full-scale and experimental verifications of the performance of TMDs have been undertaken; however, these have the inherent shortcoming of not being able to accurately quantify the performance of the TMD to the same excitation time history. Therefore, a hybrid approach is proposed, where the modal characteristics of a full-scale structure equipped with a pendulum TMD (PTMD) are determined through a measurement program and system identification. Subsequently, the responses of a numerical model with natural frequencies, primary mode shapes, and modal damping ratios consistent with the full-scale structure are found using excitation data gathered from a boundary layer wind tunnel by the high frequency base balance (HFBB) method, commonly used to predict the responses of structures through wind tunnel studies. Using a Lagrangian approach, the equations of motion for a three-dimensional PTMD coupled with a flexible main structure are determined, and the time-domain HFBB method is adapted to accommodate the directional coupling of the non-linear, three-dimensional PTMD, where both the planar and spherical motion of the auxiliary mass is considered. The results are compared to the responses predicted using simplistic models excited by broadband white noise, where motion of the tuned mass is linearized to the planar direction. The findings demonstrate the value of passive PTMDs, shed new light on the effect of using simplistic models to predict their performance, and demonstrate a practical approach to quantifying PTMD performance using full-scale measurement data.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".