Implementation and verification of real-time hybrid simulation (RTHS) using a shake table for research and education
Bibliographic record
Abstract
In this study, as a state of the art testing method, real-time hybrid simulation (RTHS) is implemented and verified with a shake table for education and research. As an application example, the dynamic behavior of a tuned liquid damper (TLD)-structure system is investigated. RTHS is a practical and economical experimental technique which complements the strengths of computer simulation with physical testing. It separates the test structure into two substructures where part of the structure for which a reliable analytical model is not available is tested physically (experimental substructure) and coupled together with the analytical model of the remaining structure (analytical substructure). The implementation of RTHS involves challenges in accurate control of the experimental substructure as well as the synchronization of the signals. The details of the hardware and the software developed and the steps taken to improve the controller are discussed in this paper so that the implementation of RTHS is properly introduced. The accuracy has been verified using tracking indicators as well as using the response obtained from a spring-mass oscillator and TLD system. The shake table used in this study is available in over 100 universities around the world. In this paper, the implementation of RTHS is provided with sufficient details to enable easy introduction of this testing method wherever a similar shake table is available. This additional functionality will not only provide a new research tool, but it will also facilitate classroom demonstrations to improve how students understand new concepts in structural dynamics and earthquake engineering.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".