A catastrophic flowslide overridden on liquefied substrate: The 1983 Saleshan landslide, China
Bibliographic record
Abstract
A flowslide overriding liquefied substrate can vastly enhance its disaster after failure initiation, due to rapid velocity and long-runout distance during landslides mobilized into flows. It is crucial to provide improved understanding to the mechanism of these catastrophic flowslides for hazard mitigation and risk assessment. This study focuses on the Saleshan landslide of Gansu in China, which is a typically catastrophic flowslide overrode a liquefied sand substrate. Geomorphologic and topographic maps along with analysis of seismic signals confirm its dynamic features and mobilized behaviors. ERT surveying detected abundant groundwater in the landslide, which is fundamental to its rapid long-runout distance. Particle size distributions and triaxial shear behaviors affirmed more readily liquefied behavior of superficial loess and underlying alluvial sand than red soil sandwiched them. We also examined the liquefaction susceptibility of the alluvial sand under loading impact at undrained and drained conditions. The alluvial sand is readily liquefied in the undrained condition while it is difficult at drained condition due to rapid water pore pressure dissipation. The results showed that the landslide experienced a sudden transformation from slide on the steep slope where it originated to flow on a nearly flat terrace with abundant groundwater that it overrode. This transformation can be attributed to the liquefied alluvial sand substrate enhancing the whole landslide body mobility. Along with recent, similar findings from landslides worldwide, substrate liquefaction may present a widespread, significant increase in landslide hazard and consequent mobility and our study reveals conditions necessary for this phenomenon to occur.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".