Yielding Steel Dampers as Restraining Devices to Control Seismic Sliding of Laminated Rubber Bearings for Highway Bridges: Analytical and Experimental Study
Bibliographic record
Abstract
The performance of concrete shear keys as restraining devices for laminated rubber bearing–supported highway bridges was examined in past earthquakes such as the 1999 Chi-Chi and the 2008 Wenchuan earthquakes, where the widely observed shear key failure and bearing sliding significantly increased the risk of span unseating. To avoid such scenarios, economical yielding steel dampers are proposed to replace conventional shear keys as restraining devices on bridges. If designed properly, the steel dampers are expected to control bearing displacement within limit without imposing much additional demand on the substructure. The primary objective of this study was to develop a simplified procedure for designing the yielding steel dampers to control sliding displacement of the laminated rubber bearings to a specified value for the considered earthquake hazard. By treating the global bridge system as a serial-parallel combination of different components, the correlations of various parameters were investigated. On that basis, a simple formulation was developed, followed by a series of nonlinear time history analyses and a shake table test as verifications. The outcome of this study highlights the cost-effectiveness of using yielding steel dampers and laminated rubber bearings as an earthquake-resistant system for highway bridges compared with other popular isolation systems. The proposed design procedure was also verified to be quite efficient in properly designing the yielding steel dampers for a satisfactory bridge seismic performance.
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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 | 0.000 |
| 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) | 0.001 | 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".