Hybrid testing of capacity designed RC structural walls for the determination of nonlinear seismic shear amplification
Bibliographic record
Abstract
Abstract Hybrid testing is an effective experimental method to study the dynamic behavior of structural elements under various types of loads. The hybrid testing method enables the seismic responses of large structural elements to be reproduced, such as the responses of reinforced concrete (RC) shear walls in a laboratory environment without needing to include the large masses typically encountered in multistory buildings. In this study, a test specimen corresponding to the base plastic hinge zone of an eight‐story RC structural wall was tested in a laboratory environment, whereas the remainder of the wall was modeled numerically to evaluate the seismic shear amplification due to nonlinear higher mode effects. A method is presented for controlling three degrees of freedom of a specimen with high axial stiffness and high lateral stiffness at the beginning of the test and the substantially reduced stiffness after cracking and yielding of the specimen. Because the duration of an uninterrupted test would have been very long, a method was used to stop and restart the test at will so that the whole test could be performed over several days. Seismic shear amplification due to nonlinear higher mode effects in a structural wall with yielding occurring at the base is measured for the first time in a hybrid test . The test results clearly present seismic shear amplification that is larger than building codes recommended values.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".