Condition assessment of structure with tuned mass damper using empirical wavelet transform
Bibliographic record
Abstract
Tuned mass damper (TMD) has been one of the most commonly used passive vibration control devices over the past few decades. While an optimally designed TMD can significantly suppress the structural vibration, detuning often occurs due to various reasons such as change in operating conditions or variation in primary structure properties, resulting in degradation of TMD’s performance. In order to restore its performance, it is necessary to estimate the modal properties of the primary structure, and perform the re-tuning process. Such an exercise requires powerful signal processing methods to successfully extract the structural modes in the presence of closely-spaced modes. This study focuses on the identification of modal frequencies and damping of the structure installed with a TMD. In view of the advantages and limitations of existing modal identification methods, this paper provides a new technique that combines the second-order blind identification (SOBI) method with the empirical wavelet transform (EWT) to delineate closely-spaced frequencies. While the SOBI method does not guarantee the separation of closely-spaced modes and suffers from the limitation of generating mode-mixed modal responses, the EWT operates on the modal responses estimated by the SOBI and yields the closely-spaced natural frequencies. The proposed method is illustrated using a six-story simulation model with a wide range of detuning cases. An experiment on a three-story bench-scale model equipped with a TMD is also conducted to validate the applicability of the proposed method.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".