Direct generation of floor design spectra (FDS) from uniform hazard spectra (UHS) — Part II: extension and application of the method
Bibliographic record
Abstract
This paper extends the methodology presented in the companion paper to study the effects of non-structural components’ (NSCs) damping ratio and their location in the building on the pseudo-acceleration floor response spectra (PA-FRS) of reinforced concrete buildings, and propose equations to derive floor acceleration design spectra (FDS) directly from the uniform hazard design spectra (UHS) for Montréal, Canada. The buildings used in the study are 27 existing reinforced concrete structures with braced frames and shear walls as their lateral load resisting systems: 12 are low-rise (up to 3 stories above ground), 10 are medium-rise (4 to 7 stories), and 5 are high-rise (10 to 18 stories). Based on statistical and regression analysis of floor acceleration spectra generated from linear dynamic analysis of coupled building–NSC systems, two sets of modification factors are proposed to account for floor elevation and NSC damping, applicable to the experimentally-derived FDS for roof level and 5% NSC damping. Modification factor equations could be derived only for the low-rise and medium-rise building categories, as insufficient correlation in trends could be obtained for high-rises given their low number. The approach is illustrated in detail for two typical buildings of the database, one low-rise (Building #4) and one medium-rise (Building #18), where the proposed FDS/UHS results show agreement with those obtained from detailed dynamic analysis. The work is presented in the context of a more general methodology to show its potential general applicability to other building types and locations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".