Implementation and Verification of Real-Time Hybrid Simulation for the Dynamic Performance Assessment of Base-Isolated Structures
Bibliographic record
Abstract
Real-time hybrid simulation (RTHS) is an economical and reliable method for the evaluation of structural dynamic performances, and the fixed analytical substructure model is often used in RTHS which may affect the accuracy of results. In this study, a real-time hybrid simulation platform (RTHSP) developed by configuring a generic National Instruments (NI) controller with hybrid programming strategy is presented in detail. The dynamic performances of a scaled base isolated structure, where the unscented Kalman filter (UKF) was used to update the analytical substructure Bouc–Wen model during the RTHSs was evaluated by presented RTHSP. RTHS of a base-isolated structure was performed where a lead rubber bearing (LRB) was tested physically as the experimental substructure of a part of the isolation layer and the superstructure with the rest of the isolation layer model updated by UKF was considered as the analytical substructure. Under the excitation of three natural earthquakes, the RTHSs with and without UKF updating were compared and analyzed the differences between the two. The results indicated that the displacements of experimental substructure generated by RTHS with UKF updating are the largest, while the relative displacements and acceleration of superstructure are the smallest overall, and the dynamic characteristics of the isolation layer of the analysis substructure updated by UKF are different from that without updated, which reflected the more authentic dynamic mechanical performance of the base-isolated structure under earthquake excitation. In addition, the RTHSP and the hybrid programming strategy are verified to be reliable in tests and experiments, and the components and implementation of a RTHSP for base-isolated structures is described in detail, providing a reference for research on RTHS method.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".