Theory and implementation of switch‐based hybrid simulation technology for earthquake engineering applications
Bibliographic record
Abstract
Summary Hybrid simulation (HS) is a novel technique to combine analytical and experimental sub‐assemblies to examine the dynamic responses of a structure during an earthquake shaking. Traditionally, HS uses displacement‐based control where the finite element program calculates trial displacements and applies them to both the analytical and experimental sub‐assemblies. Displacement‐based HS (DHS) has been proven to work well for most structural sub‐assemblies. However, for specimens with high stiffness, traditional DHS does not work because it is difficult to precisely control hydraulic actuators in small displacement. A small control error in displacement will result in large force response fluctuations for stiff specimens. This paper resolves this challenge by proposing a force‐based HS (FHS) algorithm that directly calculates trial forces instead of trial displacements. The proposed FHS is finite element based and applicable to both linear and nonlinear systems. For specimens with drastic changes in stiffness, such as yielding, a switch‐based HS (SHS) algorithm is proposed. A stiffness‐based switching criterion between the DHS and FHS algorithms is presented in this paper. All the developed algorithms are applied to a simple one‐story one‐bay concentrically braced moment frame. The result shows that SHS outperforms DHS and FHS. SHS is then utilized to validate the seismic performance of an innovative earthquake resilient fused structure. The result shows that SHS works in switching between the DHS and FHS modes for a highly nonlinear and highly indeterminate structural system. Copyright © 2017 John Wiley & Sons, Ltd.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".