Runout simulation of seismic landslides using discontinuous deformation analysis (DDA) with state-dependent shear strength model
Bibliographic record
Abstract
The reliable numerical simulation of the landslide process contributes to the establishment of evidence-based disaster mitigation measures in seismically active zones. To achieve this goal, a simple and unified state-dependent shear strength model of discontinuities is presented to describe the shear strength degradation in a seismic landslide process. The proposed model establishes a relationship between the shear strength parameters and the global safety factor of the slope by assuming that the slope instability (or landslide initiation) is accompanied by an instantaneous shear strength degradation of discontinuities. To realize the model numerically, the algorithms for the computation of global safety factor and the modification of shear strength parameters were incorporated into the discontinuous deformation analysis (DDA). Subsequently, the kinematic accuracy of the improved DDA method was validated by comparisons with theoretical solutions for the dynamic sliding of a block on an inclined plane. Numerical simulations of the Daguangbao landslide triggered by the Wenchuan earthquake were performed using the improved DDA method. The results illustrate that the shear strength degradation of discontinuities affect the evolution process, travel distance, and post-failure shape of the seismic landslide significantly.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".