A MPM framework for large-deformation seismic response analysis
Bibliographic record
Abstract
Landslides are often triggered by earthquakes and can cause immense damage due to large mass movements. To model such large-deformation events, the material point method (MPM) has become increasingly popular in recent years. A limitation of existing MPM implementations is the lack of appropriate boundary conditions to perform seismic response analysis of slopes. In this article, an extension to the basic MPM framework is proposed for simulating the seismic triggering and subsequent collapse of slopes within a single analysis step. Original implementations of a compliant base boundary and free-field boundary conditions in the MPM framework are presented, enabling the application of input ground motions while accounting for the absorption of outgoing waves and the free-ground movement at the lateral boundaries. An example slope is analysed to illustrate the proposed procedure and to benchmark it against the results obtained using an independent simulation technique, based on a three-step finite element (FE) analysis. The comparison generally shows a good agreement of the results obtained from the two independent procedures and highlights advantages of the presented “all-in-one” MPM approach, in particular for long duration strong motions.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".