Energy-based framework for designing pinned rocking systems with dissipative devices
Bibliographic record
Abstract
Traditional seismic design has primarily aimed to prevent structural collapse, often accepting significant damage that compromises building functionality and necessitates extensive repairs. In response, a low-damage design philosophy has emerged, prioritizing structural safety, minimizing residual damage, preserving operational continuity, and reducing post-earthquake costs. Among the various strategies, rocking systems – such as pinned rocking configurations – offer an effective balance between high performance and ease of post-event restoration. However, the comparative evaluation of dissipation mechanisms and layout configurations within these systems remains limited, and a systematic methodology for optimizing their design while minimizing material usage has yet to be developed. This study presents a comprehensive iterative energy-based design methodology for pinned rocking systems, potentially enhanced with supplementary dissipative devices. The investigated designs include rocking systems equipped with either viscous dampers or self-centering mechanisms at the base, as well as configurations incorporating diagonal dampers that link the rocking system to the primary structural frame. A multi-stripe analysis is conducted to evaluate the benefits and limitations of the proposed rocking system configurations and to assess the effectiveness of the energy-based design approach.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".