Wind design considerations for base-isolated post-disaster steel buildings in moderate seismic regions
Bibliographic record
Abstract
This paper presents a performance-based design methodology applied to proportion post-disaster steel braced frame buildings equipped with base isolation under recurring winds and earthquakes. The case study is a 15-storey hospital steel braced-frame building located in Montreal, Canada, where both wind and earthquake loads are critical. The base isolation system is composed of 16 lead rubber bearings placed at the base of the braced frame columns and 29 friction sliders under the gravity columns. Finite element models of the fixed-base and base-isolated lateral force resisting systems that account for geometrical and material nonlinearities , are developed in OpenSees. The building models are subjected to a set of spectrum-compatible ground motions, as well as a set of wind time-history series that are generated randomly from wind tunnel data available at the Tokyo Polytechnic University aerodynamic database . Nonlinear wind and earthquake response history analyses provide estimates of the structural performance in terms of floor accelerations, interstorey drifts, residual drifts, and isolator shear demands at the linear and nonlinear-near collapse range. The findings demonstrate the effectiveness of base isolation to mitigate damage under lateral loads . The paper also reveals the need to integrate multiple hazards in design, relax the stringent service and strength wind criteria, account for the inherent system overstrength and allow for limited ductility in displacement-controlled members in wind design.
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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 | 0.000 |
| 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 | 0.000 | 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".