Design and development of ten-element hybrid simulator and generalized substructure element for coupled problems
Bibliographic record
Abstract
The University of Toronto's ongoing work towards the improvement and further development of multi-platform simulation methods is presented in this paper. These developments include a ten-element hybrid simulator and a generalized OpenSees substructure element. Hybrid simulation which encompasses development of new integration algorithms, simulation frameworks, and applications has been an active research area in the past decade. Yet, unless the physically tested element significantly contributes to the overall lateral response of the structure in terms of stiffness, strength and energy dissipation, the improvement in the accuracy of results from the use of hybrid simulation may only be marginal. In most cases, the number of physically tested elements in the hybrid simulation is limited by the availability of experimental resources such as actuators, controllers, and laboratory space. As a step towards overcoming this limitation, a novel experimental apparatus, the UT10 Hybrid Simulator, is being developed at the University of Toronto. The UT10 is being developed to allow up to ten elements, such as braces and hysteretic dampers, to be concurrently tested and integrated into a hybrid simulation. The system can test up to ten physical specimens with peak force capacity ranging between 800 kN or 1,600 kN per specimen depending on the total number of tested specimens. The main design requirements, current development status, and potential applications of the UT10 Hybrid Simulator are presented in this paper. To integrate the potential numerical or physical substructures into distributed multi-platform simulations such as hybrid simulations, a generalized substructure element is being developed for OpenSees. The main research focus in this development is to standardize the data exchange format and communication protocol such that any other potential experimental and numerical substructure modules can be readily integrated into the simulation. The data exchange format is defined such that the number of degrees of freedom, data type, and error checks can be communicated in a seamless manner between modules. Designing a versatile data exchange format and a communication protocol is expected to facilitate simulation of coupled systems including diverse substructure modules and other loading scenarios such as thermal loading. The data exchange format and example implementations will be made available to the research community in the near future. To illustrate the current developments, an example of multi-platform simulation with a numerical substructure is presented in this paper.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".