A framework for multi‐element hybrid simulation of steel braced frames using model updating
Bibliographic record
Abstract
Abstract This paper presents a framework for seismic response assessment of steel buckling‐restrained braced frames (BRBFs) by integrating data‐driven techniques into the conventional hybrid simulation to facilitate multi‐element hybrid simulation for structural systems with several potential critical components. The data‐driven brace model incorporates Prandtl‐Ishlinskii hysteresis model into a prediction algorithm to reproduce cyclic nonlinear response of the brace under random earthquake excitations. A two‐storey steel BRBF is selected to illustrate and verify the proposed framework using a set of virtual hybrid simulations. In the BRBF, the first‐storey BRB is virtually simulated, representing the test specimen, while the second‐storey BRB enjoys the data‐driven model trained based on past experimental data. The model parameters are updated in real‐time during hybrid simulation using the data received from the virtual test specimen. The results confirm that the proposed hybrid simulation technique can offer a viable solution to address the shortcomings of conventional seismic hybrid simulation by taking advantage of model updating and multi‐element simulation platforms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".