Enhancing the seismic performance of mid‐rise wood‐frame buildings with rigid spine columns
Bibliographic record
Abstract
Summary Wood‐frame buildings (WFBs) are the most commonly used structural systems for residential applications in North America. Past researches have shown that WFBs are susceptible to soft‐story mechanisms. In this study, a simple, efficient, and economical retrofit strategy, which utilizes continuous pinned‐supported rigid spine columns (RSCs) over the height of the building, is proposed. The retrofit aims to provide uniform deformation through the height of the building to prevent soft‐story mechanisms. This retrofit strategy was applied to a six‐story WFB located in Vancouver, British Columbia, Canada. A three‐dimensional nonlinear finite element model of the prototype building was developed using OpenSees. A comprehensive parametric study was conducted to identify the most economical RSC design. Seismic performance of the prototype building, with and without the most economical RSC design, was assessed through extensive nonlinear time history analyses. Results showed that the proposed retrofit strategy can effectively improve the seismic behavior of WFBs to prevent soft‐story mechanisms.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".