Determination of Seismic Performance Factors for Buildings with Concentrically Braced Frame Systems under the Excitation of Near- and Far-fault Records
Bibliographic record
Abstract
Performance coefficients are widely used in seismic design codes to achieve performance objectives. The values of these coefficients have significant importance in achieving pre-specified performance goals. Studies have shown that near-fault earthquakes decrease the ductility and increase the risk of failure in the structures; however, the current codes use the same performance coefficients to design structures against near- and far-fault records. In the present study, 1-, 5-, 10- and 15-story special concentrically braced frame (SCBF) structures designed in the region with high seismic hazard have been evaluated. Non-linear static, linear dynamic, and incremental non-linear dynamic analyses under the influence of two sets of near- and far-fault records extracted from FEMA-P695 have been used to calculate the performance coefficients. Furthermore, the fragility curves are calculated for three performance levels (IO, LS, CP) using a probabilistic assessment of the results derived from incremental dynamic analysis to investigate the relationship between obtained factors with the probability of exceedance from a specified level. According to the mean results of all records, the behavior factor for the steel special concentrically braced frame is 5.92. The mean behavior factor obtained for the near-fault records is 35% less than the far-fault records. Differences in the obtained behavior factor for structures under excitation of two types of earthquake records (near- and far-fault) are observed in the fragility curves related to the probability of exceedance from CP level. However, there is no significant correlation between the resulted behavior factors and the probability of exceeding IO and LS levels.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.000 | 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".