A study on the effects of torsional component of ground motions on seismic response of low‐ and mid‐rise buildings
Bibliographic record
Abstract
Summary The torsional component of ground motion is a potential factor to excite the torsional response of buildings during earthquake, which is not explicitly considered in seismic design codes. Building codes have proposed accidental eccentricity to consider the effect of torsional component and other unpredicted factors, which may contribute to torsion in buildings. This study investigated the effects of torsional component on the buildings' responses and the adequacy of the accidental eccentricity. For this purpose, the torsional component of some selected ground motions was generated using single‐station procedure. Subsequently, 5, 10, and 15‐story buildings with different ratios of rotational to translational frequencies were analyzed; first, by translational components only, and second, by simultaneous application of translational and torsional components. Also, the role of mass eccentricity in the effects of torsional component was studied. Furthermore, all models were reanalyzed by applying the 5% accidental eccentricity, and the effects of torsional component and accidental eccentricity were compared accordingly. Results indicated that torsional component has significant impact on the buildings' responses and can increase the displacement and drift ratio up to 36% and 41%, respectively. However, the 5% accidental eccentricity is not sufficient to take account the torsional component effects, and leads to unreliable responses.
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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 | 0.000 | 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".