Nonlinear Seismic Analysis of Steel Concrete Composite Structures
Bibliographic record
Abstract
Reinforced concrete (R.C.) buildings are highly vulnerable to seismic failures due to soft story mechanisms and low ductility. Steel-concrete composite frames offer improved ductility, lateral load resistance, and energy absorption under seismic forces. This study investigates the seismic performance of steel-concrete composite buildings with and without masonry infill walls using a probabilistic fragility-based approach. A fifteen-story composite frame is analyzed in bare and infill configurations through non-linear static pushover analysis, and fragility curves are developed to assess damage probabilities at various performance levels.The results demonstrate that composite infill frames significantly improve lateral stiffness, base shear capacity, and seismic resilience compared to bare frames. Incorporating masonry infill substantially reduces the probability of failure across all damage states. Additionally, seismic performance assessments of low-rise, mid-rise, and high-rise composite buildings using Incremental Dynamic Analysis (IDA) reveal that low-rise structures exhibit reduced dispersion and more predictable seismic responses.This research highlights the effectiveness of masonry infill and composite construction in enhancing seismic safety and provides a valuable framework for performance-based seismic design in earthquake-prone regions.
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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 | 0.000 | 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".