Implementation, Verification, and Validation of an Impact Model for Lateral Numerical Modeling of Unbonded Fiber-Reinforced Elastomeric Isolators
Bibliographic record
Abstract
Unbonded fiber-reinforced elastomeric isolators (UFREIs) are a potentially low-cost and viable alternative for application as base isolators in low-rise buildings due to their adaptive characteristics. The behavior is denoted adaptive because the device exhibits well-defined lateral softening and subsequent substantial stiffening responses depending on the loading level. Since adaptive characteristics could have a significant effect on the seismic response of base-isolated structures, proper modeling of adaptive devices is crucial. There are several numerical modeling techniques for considering the adaptive characteristics of UFREIs. However, to date, none accurately fit the experimental hysteresis loops of UFREIs at large displacements where there is more dissipated energy due to full rollover. In this paper, an impact model is added to the leading numerical models of UFREIs (i.e., the Bouc–Wen model with a fifth-order polynomial and the algebraic model) to accurately capture the force-displacement hysteresis in lower and larger displacement amplitudes. The proposed impact model is then validated using prior experimental cyclic loading tests for square and rectangular specimens and shake table tests for different earthquake records. The effect of the impact model was also investigated through comparison with the response of the existing phenomenological models (e.g., the Bouc–Wen model with a fifth-order polynomial, the algebraic model, and the elastomeric bearing (Bouc–Wen) element). The results show that incorporating the impact model will improve the ability of the current numerical models to capture the behavior of UFREIs, particularly at larger amplitudes.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".