Impact of loading rate during hybrid simulation on seismic response of steel structures
Bibliographic record
Abstract
Abstract This study investigates the influence of loading rates on the seismic response of steel structures. A series of coupon tests under typical earthquake‐induced loading rates are carried out. In addition to the conventional monotonic tension test, the specimens are also tested under a discontinuous loading protocol that is analogous to the ramp‐and‐hold scheme employed in the pseudo‐dynamic hybrid simulations. The test results are used to develop a uniaxial steel material model to account for the strain rate effect and stress relaxation. The material model facilitates the reuse of existing steel hysteresis models and can be used to model different structural components, such as columns, beams, braces, etc. The material model is then used for a parametric study to evaluate the impact of various loading rates on the global seismic response of a single degree of freedom structural system with different structural periods, and types and intensities of ground motions. It is found that the impact of loading rates is more significant for structures with a period of 0.5 s or less and it thus requires a fast control of the physical specimen in pseudo‐dynamic hybrid testing. In addition, the material model is applied to a five‐storey buckling restrained braced frame (BRBF) building with a structural period of 1.2 s. It is found that the earthquake‐induced loading rates have a minor impact on the global response of the structure.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".