Assessment of the prediction of ground motion parameters in 1D ground response analysis using data from seismic arrays and centrifuge experiments
Bibliographic record
Abstract
Peak ground acceleration (PGA), peak ground velocity (PGV), and spectral acceleration are among the most widely used metrics to represent seismic hazard characteristics in practice. Several simplified seismic design procedures to evaluate liquefaction triggering, slope stability, or structural response have also proposed other ground motion parameters (GMPs) to represent a ground motion’s intensity, duration, and frequency content. To account for soil effects, these parameters can be obtained from the results of one‐dimensional (1D) ground response analyses. Few studies have systematically evaluated the prediction of these additional parameters from the results of ground response analyses. This study presents an exhaustive review of the accuracy and precision of the prediction of 19 common GMPs from the results of 1D ground response analyses using vertical seismic arrays and centrifuge tests. Total stress nonlinear analyses are conducted using the software DEEPSOIL along with equivalent‐linear analysis. A reference dataset composed of 10 sites and 306 ground motion recordings representing varying conditions of seismic excitation is employed. The findings of this study showed that while the models produced a reasonable approximation of spectral accelerations, a general tendency toward the over‐prediction of most parameters was revealed. The results identified that the mean period ( T m ) yielded the lowest bias, and other parameters, such as the predominant spectral period ( T o ), PGA, and PGV, also offered some improvements over the other parameters included in this study.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".