Probabilistic assessment of seismic acceleration demands of ductile light NSCs in moderately ductile RC frame buildings
Bibliographic record
Abstract
This study investigates how the seismic demands of non-structural components (NSCs) are influenced by both their attachment ductility and the nonlinear behavior of supporting structures. The research focuses on acceleration demands at various building elevations and evaluates component damage states according to Hazus guidelines. Incremental dynamic analysis (IDA) was applied to evaluate both linear and nonlinear structural responses of four archetype reinforced concrete moment-resisting frame buildings. Ground motions, consisting of historical and synthetic records, were scaled to match Montreal’s uniform hazard spectrum for Site Class 'C' with a 2 % probability of exceedance per 50 years. NSC responses were assessed using an uncoupled analysis approach, implemented through iterative Newmark integration. Key findings demonstrate that increasing the ductility of NSC attachments reduces their acceleration demands by up to 140 % in elastic structures. When accounting for structural nonlinearity, acceleration demands decrease by 110 %, highlighting the conservative nature of elastic analysis assumptions commonly used in current design practices. These reductions are most pronounced for components with periods corresponding to the structure's fundamental mode, with the effect diminishing for higher modes. The research provides practical design implications by quantifying the relationship between attachment ductility, structural behavior, and component damage thresholds. The results indicate that a moderately ductile (μ≈1.5) NSC attachment provides optimal performance benefits while minimizing the risk of NSC damage, offering valuable insights for the performance-based design of NSCs. • Iterative Newmark approach to simulate ductility of NSCs for fragility analysis. • Higher periods of NSCs show less damage risk as the performance of NSCs enhances. • Structural nonlinearity enhances the performance of NSCs in all damage states. • NSCs show higher vulnerability at elevated building levels, especially at the roof. • Higher ductility of NSCs improves energy dissipation but raises damage risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".