3D effects in seismic liquefaction of stochastically variable soil deposits
Bibliographic record
Abstract
The natural variability of soil properties within geologically distinct and uniform layers has been proven to greatly affect soil behaviour and to induce significant variability in the predicted response. Previous studies concluded that small-scale heterogeneity greatly affects the liquefaction potential of saturated soil deposits, and provided geotechnical design guidelines to account for the effects of various characteristics of spatial variability. Those studies were based on two-dimensional analyses of soil liquefaction (in a vertical plane) assuming plane strain behaviour. Therefore the correlation distance of soil variability in a direction normal to the plane of analysis was implicitly taken as infinite (i.e. no variability in the third direction). In this study, a Monte Carlo simulation approach involving generation of sample functions of non-Gaussian, multivariate, multidimensional random fields and non-linear finite element analyses is used to investigate the effects of soil heterogeneity on the liquefaction potential of a ‘stochastically heterogeneous’ soil deposit subjected to seismic loading. To assess the 3D effects, Monte Carlo simulation results obtained for a 3D soil deposit are compared with corresponding results from 2D plane strain analyses. The calculations are performed for a range of seismic acceleration intensities, and the results are presented in terms of fragility curves expressing the probability of exceeding various thresholds in the response as a function of earthquake intensity.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".