Real-Time Dynamic Substructuring Testing of a Bridge Equipped with Friction-Based Seismic Isolators
Bibliographic record
Abstract
This paper presents a real-time dynamic substructuring (RTDS) test program that was carried out on a bridge structure equipped with seismic isolators with self-centering and friction energy dissipation capabilities. The structure studied also included bearing units with sliding interfaces providing additional energy dissipation capacity. In the RTDS tests, the seismic isolator was physically tested in the laboratory by using a high performance dynamic structural actuator imposing, in real time, the displacement time-histories obtained from numerical simulations that were run in parallel. The integration scheme used in the test program was the Rosenbrock-W variant and the integration was performed by using the MathWorks’s Simulink and an XPC target computer environment. The numerical counterpart included the bridge piers and the additional energy dissipation properties. The nonlinear response of these components was accounted for in the numerical models. The RTDS tests were performed in the direction parallel to the length of the bridge. The effects of various ground motions and the influence of modeling assumptions such as friction and column stiffness were investigated. Finally, the test results were compared to the predictions from dynamic time-history analyses performed by using commercially available computer programs. The results indicate that simple numerical modeling techniques can lead to an accurate prediction of the displacement response of the bridge seismic protective systems studied.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".