Generation of floor and tertiary response spectra of structures under seismic excitations at multiple supports
Bibliographic record
Abstract
Abstract A direct spectra‐to‐spectra method is developed for generating floor response spectra (FRS) for structures under earthquake excitations at multiple supports in terms of ground response spectra (GRS). Only GRS, “t‐response spectra” (tRS), and basic modal information of primary structures, which can be readily obtained from modal analyses, are needed. FRS are separated into dynamic part and quasi‐static part, which are combined by a new combination rule FRSMS‐CQC developed using random vibration theory. FRSMS‐CQC can account for the correlations between various components affecting FRS, that is, the correlation between the responses of oscillators excited by any two vibration modes, the correlation between the response of an oscillator excited by a vibration mode and the response of an oscillator mounted directly on a support, and the correlation between the responses of oscillators mounted on two different supports. In particular, two special cases, that is, excitations in the same direction at two supports being fully correlated and excitations at two supports being uncorrelated, are considered. The direct method can also be applied to generate tertiary response spectra (TRS) from FRS at multiple supports of secondary structures. Numerical example of a piping system mounted on different buildings, which are subjected to tridirectional seismic excitations at the foundation level, is presented to demonstrate the superiority of the proposed method. It is shown that FRS/TRS determined by time‐history (TH) analysis have large variabilities, particularly at FRS/TRS peaks. The proposed direct method, which avoids the deficiencies of time history methods, is of excellent accuracy, efficiency, and simplicity.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".