Impacts of ground motion characteristics on liquefaction triggering and lateral displacement of a sloping ground
Bibliographic record
Abstract
Subduction motions typically exhibit lower amplitudes, longer durations, and lower frequencies compared to crustal motions. This paper initiates the assessment of how these ground motion characteristics impact liquefaction-induced responses in sloping ground by presenting a numerical study that isolates these effects. In an initial endeavor to dissect and understand the effects of these variables, single-frequency sinusoidal ramp waves with varying amplitudes, durations, and frequencies are used in this study for fully coupled nonlinear dynamic analysis of a mildly-sloping liquefiable soil column. The analyses are carried out in OpenSees using the SANISAND-MSf v2 soil constitutive model. The simulation results indicate that increasing the maximum amplitude of the base excitation leads to a decrease in the number of cycles required to trigger liquefaction, while simultaneously increasing the end-of-motion surface lateral displacement. Increasing the number of cycles with the maximum amplitude would not affect the pre-liquefaction response if the triggering happens in the early loading cycles, but can increase the surface lateral displacements as the soil remains in the post-liquefaction stage for a longer duration. Both pre- and post-liquefaction soil responses under various motion frequencies can exhibit distinct patterns depending on the intensity of the motion and the natural frequency of the soil column.
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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 | 0.000 | 0.000 |
| 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".