Seismic performance evaluation of reinforced concrete moment resisting frames
Bibliographic record
Abstract
During the last few decades, the concepts and procedures of seismic design of buildings have evolved significantly. Traditionally, seismic design was based on providing the structure with the strength it needs to resist equivalent lateral forces that represent the earthquake induced forces. This methodology has proven to be inefficient in providing the target level of seismic performance in all case scenarios. The new methodology in seismic design, performance-based design, aims at designing the structure to achieve the expected performance levels under different levels of seismic hazard. Although this goal is still not fully attained, several developments have taken place towards it. The latest edition of the National Building Code of Canada (NBCC 2005) requires the use of dynamic analysis for the seismic design, except in the case of short to medium height simple buildings with regular geometry where the use of the equivalent static load method is still allowed. In this study, three moment resisting frame buildings assumed to be located in Vancouver, British Columbia are designed based on the equivalent static load method of the NBCC 2005 and their performance is evaluated using nonlinear dynamic time-history analysis. Multiple ground motion records corresponding to different seismic hazard levels are used in the analysis. The effect of infill panels on the seismic response is studied as well. The results show that the design is satisfactory and, in some cases, conservative. The results also show that infill panels, in most cases, enhance the dynamic response of the buildings, while in some cases they may have a negative impact on it.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".