Seismic response of a case study soft story frame retrofitted using a GIB system
Bibliographic record
Abstract
Summary This paper presents results from a numerical investigation into the seismic retrofit of a soft story frame using a novel gapped‐inclined brace (GIB) system. The GIB system consists of a pinned brace and a gap element that is added to the first story columns of the frame. The inclusion of GIB elements in addition to increasing the lateral capacity of columns at the first story increases the post‐yield stiffness ratio of the system and reduces the P‐delta effects on the columns, while not increasing the first story lateral resistance or stiffness. This allows for the isolating benefits of the soft story to protect the upper floors of the structure from damage while avoiding excessive deformations and reducing the propensity for collapse. A six‐story RC frame with masonry infills on all floors except for the first floor is studied. The dynamic response of the retrofitted building using the GIB system is investigated numerically and is compared with the response of the original un‐retrofitted building and the same building in which masonry infills are added to the first story to mitigate the soft story response. Results from the nonlinear time‐history analyses indicate that the GIB system could provide a reliable seismic retrofit mechanism for soft story buildings, which greatly reduces the likelihood of collapse by increasing the displacement capacity of the soft storey and by reducing P‐delta effects, while minimizing the overall damage and losses in the building by taking advantages of the isolation that is provided by the soft story to the rest of the structure located above. Copyright © 2014 John Wiley & Sons, Ltd.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".