Seismic Performance of Hybrid Self-Centering Braces with Structural and Nonstructural Damage Control Functions: Validation Tests, Computational Modeling, and Benefits Evaluation
Bibliographic record
Abstract
This paper presents a comprehensive study of validation tests, computational modeling, and system-level benefits evaluation of an innovative hybrid self-centering brace (HSB) with structural and nonstructural damage control functions. The HSB is constructed by combining shape memory alloy (SMA) U-shaped dampers and rate-dependent viscoelastic dampers in parallel. Validation test results confirmed the desired rate-dependent hysteretic behavior under dynamic loadings. A new SMA material model was proposed and developed in OpenSees for modeling the hysteretic behavior of shape memory alloy U-shaped dampers. A new ViscoelasticDamper material model, based on the nine-element Maxwell model, was proposed and developed in OpenSees to capture the amplitude- and frequency-dependent hysteretic behavior of viscoelastic dampers associated with cyclic deterioration caused by peak deformation and hysteretic energy-dissipation. The computational model of HSBs was proposed by combining the newly developed SMA and ViscoelasticDamper material models, and the model was validated using the test results. Advanced steel frames using HSBs as the lateral force-resisting systems (denoted HSBFs) were designed. The benefits of HSBFs were investigated by comparing the peak and residual interstory drifts, absolute floor accelerations, and floor spectra of a six-story HSBF with those of another emerging SMA-based self-centering braced frame (SMABF) under earthquakes. The results confirm that the HSBF can achieve better performance than the SMABF in reducing base shear demands, absolute floor accelerations, and floor spectra, and they have comparable capacities in controlling peak and residual interstory drifts.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".