Thermal-Mechanical Model for Predicting the Wind and Seismic Response of Viscoelastic Dampers
Bibliographic record
Abstract
This paper presents a simplified coupled thermal-mechanical model for the modelling of viscoelastic dampers with temperature- and frequency-dependent properties subjected to long-term and short-term dynamic loading due to wind and earthquakes. In this numerical model, the self-heating effect caused by molecular-level friction in the viscoelastic (VE) material is captured explicitly using a finite volume thermal diffusion model based only on the physical properties of the steel and VE material. The thermal model is coupled to a single degree of freedom (SDOF) mechanical model, which produces an efficient computational scheme for time-history analyses of VE dampers under both long-term and short-term loading scenarios. The predictions from the proposed model are compared with full-scale experimental results of long-duration wind loading ranging from design level events to extremely rare events, and for shorter but more intense seismic loading scenarios. It is shown that results obtained using this proposed simplified model with idealized geometry predicted well the dynamic response of the damper for all ranges of experimental results thus validating a robust physics based tool for predicting the response of VE dampers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".