Model updating method for substructure pseudo‐dynamic hybrid simulation
Why this work is in the frame
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Bibliographic record
Abstract
SUMMARY Substructure hybrid simulation has been actively investigated and applied to evaluate the seismic performance of structural systems in recent years. The method allows simulation of structures by representing critical components with physically tested specimens and the rest of the structure with numerical models. However, the number of physical specimens is limited by available experimental equipment. Hence, the benefit of the hybrid simulation diminishes when only a few components in a large system can be realistically represented. The objective of the paper is to overcome the limitation through a novel model updating method. The model updating is carried out by applying calibrated weighting factors at each time step to the alternative numerical models, which encompasses the possible variation in the experimental specimen properties. The concept is proposed and implemented in the hybrid simulation framework, UI‐SimCor. Numerical verification is carried out using two‐DOF systems. The method is also applied to an experimental testing, which proves the concept of the proposed model updating method. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.000 |
| 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 it