An advanced rate‐dependent analytical model of lead rubber bearing
Bibliographic record
Abstract
Abstract Lead rubber bearings (LRBs) are a type of isolation bearing that have a combination of rubber and lead as the main components. These bearings are widely used in bridges, buildings, and other important structures due to their high load‐carrying capacity and excellent energy dissipation capability. However, the behavior of LRBs is complex and nonlinear, making it difficult to predict their behavior and performance under different loading conditions. The objective of this research is to develop a comprehensive analytical model of LRBs that can accurately predict their behavior under low to large levels of strain. The proposed model considers nonlinearity, hysteresis, stiffness, damping, and rate‐dependent behavior of LRBs. The model is also able to consider the effect of temperature on the rubber and lead components of the bearing. The developed model is validated using experimental results and is shown to provide accurate predictions of the LRB response under different strain levels. The accuracy of the developed LRB model is also validated using shake table test results of an LRB‐isolated bridge under low and large strain. This research provides a valuable tool for engineers and designers to predict the behavior and performance of LRBs and optimize their design.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 0.001 |
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".