Distinct element modeling of the dynamic response of a rocking podium tested on a shake table
Bibliographic record
Abstract
Abstract A blind prediction contest was organized to evaluate the ability of different modeling approaches to simulate the seismic rocking response of a full‐scale four‐column podium structure. The structure was tested on a shake table, and was subjected to two bidirectional ground motion ensembles comprising 100 synthetic records each. This short communication presents the main assumptions and results from the model, developed using the distinct element method, which provided the second‐best prediction of the experimental results. A comparison of the model predictions and the experimental results demonstrates that the numerical model was generally able to reproduce the large displacements induced by the more intense ground motion ensemble, while tending to overestimate the displacements of the less intense earthquake ensemble. This overestimation of the response was reduced through the inclusion of damping in the system. However, the addition of damping greatly increased the solve time, which is problematic for a competition, and in the case of the more intense ground motion ensemble also resulted in an underprediction of the maximum response of the structure.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".