Failure Process Analysis of Landslide Triggered by Rainfall at Volcanic Area: Fangshan Landslide Case Study
Bibliographic record
Abstract
The Fangshan landslide was a rainfall-induced landslide that occurred in a volcanic area in the Fangshan scenic spot, Nanjing, Jiangsu, China. On 25 October 2016, after approximately 10 days of continuous rainfall, a shallow landslide rapidly developed, which triggered slow movement of deep mudstone rock. According to the characteristics of the landslide body, measures such as anti-slide piles, anchor cables and drainage were used to reinforce the landslide. Active drainage measures included arranging plant growth zones at the trailing edge of the landslide, and passive drainage measures included arranging pumping wells at the trailing edge of the landslide. It is worth emphasizing that the Fangshan landslide was the first example of a landslide in Jiangsu Province, China that was treated by actively lowering the water pressure. After landslide treatment from 16 May 2017 to 21 January 2018, the Fangshan landslide tended to be stable. However, the stable landslide was reactivated by the rise in groundwater level caused by rainfall and pumping well damage and underwent accelerated downward sliding in July 2020. The Fangshan landslide has caused great damage to the roads and buildings of Fangshan scenic spot, with a direct loss of RMB 6 million and an indirect loss of RMB 95 million. This article discusses the development process of the shallow soil landslide and the underlying deep mudstone rock landslide. The influence of groundwater level variation on the deformation of the shallow soil landslide and deep mudstone rock landslide of the Fangshan landslide are also discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".