Optimizing parameters of tuned mass damper subjected to critical earthquake
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Summary Tuned mass damper (TMD) has been proposed as one of the vibration control methods for rehabilitation of buildings. Because the parameters of TMD can significantly affect the seismic performance of structures, many researches focused on finding the optimum parameters. Because earthquakes are random phenomena and future earthquakes in comparison with past earthquakes may be more destructive, the optimum design of TMD subjected to selected earthquakes can be nonconservative. Hence, the main contribution of this paper is to present the optimal design of TMD for the seismic vibration control of a structure subjected to a critical earthquake that produces the most severe response of a structure. In order to achieve this purpose, the parameters of TMD are optimized through minimizing the maximum displacement of the roof. First, three optimization methods are used to obtain the optimal parameters of TMD for a 10‐story shear building subjected to the critical earthquakes. Finally, the responses of the controlled and uncontrolled buildings such as the roof displacement, strokes, transfer function, and different forms of energy are compared. Results show that the optimum designs of TMD not only effectively reduce the roof displacement but also improve the seismic performance of the building.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it