Structural optimization of vertical isolated rocking core-moment frames
Bibliographic record
Abstract
Dual earthquake-resistant archetypes suffer residual damage following a severe earthquake leading to socio-economic drawbacks. A vertical isolated rocking core-moment frame (VI-RCMF) provides a resilience technique to curtail the seismic vulnerability using the isolation of subsystems. Viscous dampers, installed at each floor level of the VI-RCMF archetype, separate moment frame (MF) from rocking-core (RC). The self-centering RC performs as a passive strong-back core, which can uplift on its toes. Although the VI-RCMFs have exhibited superior performance compared to non-isolated dual frames, there are analytical challenges to come up with the optimal design due to the complex interaction of subsystems. This paper presents an optimization framework for quantifying the optimal design parameters of VI-RCMFs. Accordingly, the simultaneous perturbation stochastic approximation (SPSA) method is employed as the optimization algorithm. The objective function is defined to minimize the displacement of MF and the design vector includes mass, stiffness, and damping ratios. The SPSA optimization analyses are conducted for a set of archetypes using OpenSees software. Optimal normalized responses of subsystems are computed for 44 far-field ground motions and aleatory uncertainties are quantified for optimal design variables. The results demonstrate the effectiveness of the proposed procedure for optimizing VI-RCMFs. The derived optimal design vector can be used for the preliminary design of low-to mid-rise archetypes.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".