Simplified geostatistical analysis of earthquake-induced ground response at the Wildlife Site, California, U.S.A
Bibliographic record
Abstract
Almost all natural soils are highly variable and rarely homogeneous. In this study, the seismic response of the Wildlife Site, Imperial Valley, California, U.S.A., has been analysed to assess the effect of ground heterogeneity on liquefaction assessment in a probabilistic analysis framework. Cone penetration test (CPT) data recorded at the site have been used to identify different lithologies and to estimate elements of soil inherent spatial variability. Monte Carlo simulation has been utilized to obtain several realizations of CPT data that were then implemented into empirical approaches to examine the liquefaction susceptibility of the site. In addition, stochastic analysis of liquefaction-induced surface damage has been carried out through the application of these realizations into damage criteria, such as total liquefaction damage potential and surface settlement. These stochastic analyses have indicated that using mean values in deterministic analysis can be on the unsafe side. As a result, attempts have been made to obtain meaningful representative soil parameters that can be used in simplified deterministic analysis, while continuing to honor detailed ground heterogeneity. In addition, an empirical technique has been developed to compare ground variability of potentially liquefiable sites on a qualitative basis.Key words: liquefaction, spatial variability, stochastic analysis, cone penetration test, damage, characteristic parameters.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".