Seismic Vulnerability of Non-Code-Compliant and Code-Compliant RC Buildings
Bibliographic record
Abstract
This study investigates the seismic vulnerability of non-code-compliant reinforced concrete (RC) buildings compared to code-based structures. The research uses linear elastic and nonlinear pushover analyses (NPA) to evaluate critical seismic performance parameters such as natural periods, mass participation, base shear, capacity curve, ductility ratio, overstrength factor, collapse mechanics, and nonlinear hysteretic damping (NHD). Structures designed following standards like NBC 205 (old), RUD 205 new, and Indian standard IS 1893 are analyzed against non-code-compliant building samples (NES1-NES6) to highlight performance gaps. The findings reveal that code-compliant buildings demonstrate significantly higher seismic resistance, greater flexibility, effective earthquake energy dissipation, higher ductility, overstrength factor, and base shear capacity. Non-code-compliant buildings often exhibit soft-story failure, with initial damage observed in the columns, highlighting their vulnerability during seismic events. Meanwhile, code-compliant RC buildings (RUD) designed with seismic principles demonstrate better seismic performance, adhering to the “strong column, weak beam” philosophy and superior strength-to-capacity ratios, higher overstrength factors, and enhanced ductility ratios, highlighting their resilience under seismic loads. The results conclude that addressing the code provisions ensures earthquake-resistant buildings with warranted ductile behavior for structural systems, enabling the achievement of the intended collapse mechanism.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".