Studying and Analyzing the Seismic Performance of Concrete Moment-Resisting Frame Buildings
Bibliographic record
Abstract
Nowadays, due to environmental changes, the condition of buildings can be in danger. In order to protect the performance of existing buildings, it is important to investigate the seismic behaviour of building structure subjected to earthquake excitation. In addition, it is important to study the advanced level of ductile design recommended by current codes. In the immediate future, lateral load resistance needs to be evaluated precisely. For the purpose of analyzing the seismic responses of reinforced concrete (RC) buildings in this project, 6- and 12-story frames of representative buildings are modeled in this research paper. The response spectrum analysis is deployed for a multi-degree-of-freedom system exposed to seismic waves (earthquake) in the City of Vancouver. Hence, the seismic nature of Vancouver City is emulated by the resulting response spectra using ground motion records (GMR). Ultimately, for concluding the average displacement as well as the base shear of the structures, a time-history analysis is investigated. In this regard, the SAP2000 and ETABS software are utilized for analyzing seismic performances. In addition, a comparison is presented between previous studies that used the IDARC2D software and the proposed results that used the ETABS software. It is found that the applied cases are not overlapping the limit of the NBCC 2015 Code. Consequently, after investigation, it is evident that the 3D software is much more accurate than the 2D software.
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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 | 0.001 | 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".