Evaluación de respuesta sísmica usando la teoría de vibraciones aleatorias en tres perfiles de suelo de Lima, Perú
Bibliographic record
Abstract
Seismic response analyses are performed using a minimum number of seismic records as input motions in order to achieve a statistically strong estimation. Unfortunately, the available information recorded from the current seismic networks is still scarce regarding events with considerable magnitude. In this context, the Random Vibration Theory (RVT) arises as an alternative tool for performing site response analyses without the need of seismic records, since it only requires adequate probabilistic seismic hazard assessment. In this study, RVT was applied to three shear-wave velocity profiles in Lima city with distinct geomorphological origin. These profiles are characteristic for gravelly, sandy and fine deposits so the influence of each soil type in their corresponding transfer function was taken into account. In that sense, the three RVT-based normalized response spectra show good agreement with the design spectra specified in the Peruvian code, despite some amplification in the short (below 0.10 s) and long (above 0.80 s) period ranges related to noise or far-field effects. Furthermore, RVT-based response spectra for La Punta and Villa el Salvador show good agreement with the time-series based analyses from a previous study. In addition, spectral acceleration values surpass those specified in the Peruvian code for a range beyond the corner period. This could suggest that the soil profile characterization based on the time-averaged shear wave velocity from the upper 30 m might be insufficient to evaluate the overall seismic behavior of a soil deposit. Therefore, additional parameters that account for the deeper soil substructure might be required.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".