Hybrid Simulation Framework for Multi-hazard Testing
Bibliographic record
Abstract
The design of complicated structures which, under accidental actions, have to fulfill a certain performance level, has been a scientific challenge with social and economic implications, particularly in the field of earthquake engineering. Experimental testing on structures would shed light to the deriving issues, however the full-scaling requirements of the specimens and the most out of date existing laboratory facilities do not facilitate it. For that reason, it is generally proposed the testing structure to be decomposed in its components and the part of scientific interest can be laboratory tested, whereas the other substructures are analytically modelled. That approach is known as hybrid simulation method (HS) and lends itself as an efficient tool in unveiling the nonlinear response of structural systems, especially when testing in full-scale is sought. The present research aims to evaluate the technical aspects of implementing a robust, advanced hybrid simulation (HS) platform, based on technological advancements and combining user friendliness and effectiveness. In addition, the capabilities of the advanced platform pave the way to future research extensions towards studying multi-physics problems beyond the field of earthquake engineering. The good performance of the updated hardware configuration of the new platform was evaluated via a series of verification tests on a pinned steel cantilever column subjected to lateral loading in its elastic and inelastic response region and finally, making use of the advanced application platform as a whole, a hybrid simulation test was carried out on an industrial piping system under earthquake excitation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".