Comparison of seismic responses of a reticulated dome under stochastic uniform and spatially correlated and coherent multiple-support excitation for a scenario seismic event
Bibliographic record
Abstract
Structures with multiple supports can be sensitive to spatial coherence and spatial correlation. Since the historical recordings are insufficient for selecting records that match predefined inter-support distances of a structure, desired seismic magnitude (or intensity) and site to seismic source distance for structural analysis, such records need to be simulated. In this study, we use a procedure that is extended based on the stochastic point-source method to simulate records for scenario events. The application of the simulated records to a single-layer reticulated dome with multiple supports is presented. The application is aimed at investigating the differences between the responses subjected to spatially uniform excitation and to spatially correlated and coherent multiple-support excitation for a scenario seismic event, assessing the relative importance of the spatial coherence and spatial correlation on the responses, and evaluating the effect of the uncertainty in the spatially correlated and coherent records for a scenario event on the statistics of the seismic responses. The analysis results indicate that the spatial correlation of the Fourier amplitude spectrum has a predominant influence on the linear/nonlinear responses, and the consideration of spatially correlated and coherent excitation at multiple supports is very important. The consideration of uniform excitation severely underestimates the seismic load effects as compared to those obtained under spatially correlated and coherent multiple-support excitation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".