Development of a ten‐element hybrid simulation platform and an adjustable yielding brace for performance evaluation of multi‐story braced frames subjected to earthquakes
Bibliographic record
Abstract
Summary This paper presents a ten‐element hybrid (experimental‐numerical) simulation platform, referred to as UT10, which was developed for running hybrid simulations of braced frames with up to ten large‐capacity physical brace specimens. This paper presents the details of the development of different components of UT10 and an adjustable yielding brace (AYB) specimen, which was designed to perform hybrid simulations with UT10. As the first application of UT10, a five‐story buckling‐restrained braced frame and a special concentrically braced frame (BRBF and SCBF) were designed and tested with AYB specimens and buckling specimens representing the braces. Cyclic tests of the AYB, one‐ and three‐element hybrid simulations of the BRBF, and four‐element hybrid simulations of the SCBF inside the UT10 confirmed the functionality of UT10 for running hybrid simulations on multiple specimens. The tests also indicated that AYB was capable of producing a stable hysteretic response with characteristics similar to BRBs. Comparison of the results of the hybrid simulations of the BRBF and SCBF with their fully numerical models showed that the modeling inaccuracies of the yielding braces could potentially affect the global response of the multi‐story braced frames further emphasizing the need for experimental calibration or hybrid simulation for achieving more accurate response predictions. UT10 provides a simple and reconfigurable platform that can be used to achieve a realistic understanding of the seismic response of multi‐story frames with yielding braces, distinguish their modeling limitations, and improve different modeling techniques available for their seismic response prediction.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".