Probabilistic investigation of the seismic displacement of earth slopes under stochastic ground motion: a rotational sliding block analysis
Bibliographic record
Abstract
Earthquakes frequently induce landslides and other natural disasters that would have a huge impact on human life and properties. In geotechnical engineering, evaluation of the seismic stability of earth slopes has been attracting a substantial amount of research interest. In this regard, the Newmark permanent displacement provides a simple yet effective index of slope co-seismic performance. The traditional Newmark method involves many assumptions and the displacement results thereby calculated are subject to various degrees of uncertainty. In this paper, a modified rotational sliding block model considering depth-dependent shear strength and dynamic yield acceleration is established. The seismic critical slip surface is analysed through a pseudo-static approach, where the failure volume is larger than that in the static condition. The dynamic yield acceleration is updated by considering the instantaneous movement of the sliding mass in each time-step. The parametric sensitivity of soil shear strength, slope geometry, and Arias intensity to the seismic displacement is also analysed. Results show that the internal friction angle and the cohesion have equal effects on the permanent displacement. On a logarithmic scale, the displacement approximately linearly correlates with Arias intensity. Furthermore, the underlying uncertainty of the ground motion is introduced to obtain the probabilistic distribution of the seismic slope displacement. The uncertainty of earthquake time history details has considerable influence on the permanent displacement results. Under the specific allowable displacement, the probability of failure increases exponentially with seismic intensity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".