Direct generation of floor design spectra (FDS) from uniform hazard spectra (UHS) — Part I: formulation of the method
Bibliographic record
Abstract
In most current building codes, seismic design of non-structural components (NSCs) is addressed through empirical equations that do not capture NSC response amplification due to tuning effects with higher and torsional modes of buildings and that neglect NSC damping. This work addresses these shortcomings and proposes a practical approach to generate acceleration NSC floor design spectra (FDS) in buildings directly from their corresponding uniform hazard spectra (UHS). The study is based on the linear seismic analysis of 27 reinforced concrete buildings located in Montréal, Canada, for which ambient vibration measurements (AVM) are used to determine their in situ three-dimensional dynamic characteristics. Pseudo acceleration floor response spectra (PA-FRS) are derived at every building floor for four different NSCs damping ratios. The calculated roof FRS are compared with the 5% damped UHS and a formulation is proposed to generate roof FDS for NSCs with 5% damping directly from the UHS.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".