Assessment of Material Strength Implications on Seismic Design of Tall Buildings through Collapse Analysis
Bibliographic record
Abstract
High-strength materials are widely utilized in multi-story buildings with shear walls to effectively utilize floor areas and control lateral drifts. To investigate the impacts of high-strength concrete on the seismic design coefficients of shear wall-supported structures, five different designs of 60-story buildings with varying concrete strength are considered. The reference structures are designed and detailed such that they have very close periods of vibration. The large number of inelastic analyses performed at different intensity levels using twenty earthquake records and detailed fiber-based simulation models enabled the effective verification of the seismic design coefficients. The results reflect the enhanced profits and safety margins of shear wall-supported structures with increasing concrete strength. There is a possibility for increasing the design coefficients, which has several economic advantages. This systematic study provides practical insights into the seismic response of high-strength shear wall-supported tall buildings at different performance limit states and enables the verification of essential coefficients used in seismic 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 | 0.000 | 0.002 |
| 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".