Experimental and numerical characterization of a circular unbonded fiber-reinforced elastomeric isolator with high-damping
Bibliographic record
Abstract
Fiber Reinforced Elastomeric isolators (FREis) represent a novel category of elastomeric seismic isolators. in contrast to conventional Steel-Reinforced Elastomeric isolators (SREis), FREis incorporate slender layers of fibers instead of steel laminates to provide vertical reinforcement. These isolators can be utilized in various configurations, including bonded, unbonded, and partially bonded setups. in the unbonded configuration, the isolator is positioned between the superstructure and the foundation without bonding or fastening. The shear load transfer relies on the friction generated between the isolator and the structure. This application is characterized by rollover deformation, which reduces horizontal stiffness and enhances the damping capacity of the isolator compared to its bonded counterpart. This study proposes a combined numerical and experimental approach to characterize the lateral behavior of FREis. A preliminary numerical model, based on simple material tests, is presented to predict the horizontal characteristics. Subsequently, a prototype is fabricated and subjected to experimental tests, with the results compared to the numerical predictions. The outcomes of this research underscore the robustness of the numerical model, which can be employed both for predicting lateral behavior and anticipating future structural applications.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".