Effect of torsion and non-structural components on seismic floors accelerations: A case study building in Montreal
Bibliographic record
Abstract
The seismic design of acceleration sensitive non-structural components (NSCs) requires the computation of accelerations at the building floor levels on which they are installed.It was shown in recent studies that these accelerations depend on the building dynamic properties that are in turn affected by the presence of NSCs.Unlike regular buildings, very few studies focused on the seismic behavior of NSCs located in irregular buildings.In this paper, the effect of non-structural components on seismic floor acceleration amplification (FAA) was assessed in a torsionally irregular 6-story building named "maison des étudiants" (MDE) and located on the campus of the École de technologie supérieure (ETS) in Montreal.FAAs were computed by performing seismic simulations on two calibrated building models implemented in the Finite Element Software (ETABS) and subjected to 12 earthquakes calibrated to match Montreal's uniform hazard spectrum.The two models were calibrated using ambient vibration measurements performed at two construction stages: bare-frame without NSCs and full-frame with NSCs including masonry walls, curtain walls and secondary beams.The results show that the computed FAAs corresponding to both construction stages are higher than the FAAs prescribed by the National Building Code of Canada (NBCC) for NSCs attached to the periphery of the irregular building.In addition, less FAAs were observed at the fullframe stage when compared to the bare-frame stage
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".