Self‐centering seismic‐resistant structures: Historical overview and state‐of‐the‐art
Bibliographic record
Abstract
This state‐of‐the‐art review provides an overview of the evolution of self‐centering structures from early historical structures that inherently exhibited a recentering response to modern systems engineered for enhanced seismic resilience. From the early research investigations that were conducted since the 1960s, to the sharp increase of interest in this topic over the last two decades, self‐centering seismic‐resistant structures that can mitigate both damage and residual drifts following major earthquakes have seen significant advances. These systems achieve the intended self‐centering response by either allowing for the rocking of primary structural elements in a controlled manner, commonly coupled with mechanical restraints and energy dissipation devices, or by including self‐centering devices as main structural or supplemental structural members. To better explain the concepts and the underlying mechanics governing their seismic response, detailed schematic illustrations were developed in this article, highlighting the fundamentals behind each of these systems. This article covers a historical overview, presents the state of the research and of the art, discusses general design challenges and practical considerations, and concludes with future research needs to advance the development and broader application of self‐centering systems in real structures.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".