Controls on Post‐Seismic Landslide Behavior in Brittle Rocks
Bibliographic record
Abstract
Abstract Earthquakes trigger widespread landsliding in tectonically active landscapes. The effects of strong ground shaking on hillslope stability persist into the post‐seismic stage; rates of landsliding remain elevated in the years following an earthquake. The mechanisms that control the spatial pattern and rate of ongoing landsliding are poorly constrained, hindering our ability to reliably forecast how landscapes and landslide hazard evolve. To address this, we undertook a detailed geotechnical investigation in which we subjected representative rock samples to dynamic loading, simulating the effects of earthquake ground shaking on hillslopes of different configuration. Our results indicate that post‐seismic hillslope strength is not an intrinsic rock property; rather, it responds to the amplitude of imposed dynamic loads and the degree of pre‐existing shear surface formation within the rock. This path‐dependent behavior results from differences in the character of fractures generated by dynamic loads of different amplitude, and the ways in which apertures are mobilized or degraded in subsequent (post‐seismic) shearing. Sensitivity to dynamic loading amplitude is greater in shallow landslides in which shear surfaces are yet to fully form; such hillslopes can be strengthened or weakened by earthquake events, depending on their characteristics. In contrast, deeper landslides on steeper hillslopes in which shear surfaces have largely developed are less likely to display differences in behavior in response to dynamic loading because strain accumulation along pre‐existing fractures is dominant. Our results demonstrate the need to consider path‐dependent hillslope stability in numerical models used to forecast how landscapes respond to earthquakes and how post‐seismic hazard evolves.
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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".