Experimental study on deformation and failure of a mining slope under the action of rainfall
Bibliographic record
Abstract
On 28 August 2017, a mining slope collapse in Pusa Village, Guizhou Province, China, resulted in the release of ~8600 m3 of earth and rock, leading to 35 fatalities. The deformation and failure mechanisms of the mining slope under the influence of rainfall was studied by a physical model test, using the Pusa Village collapse as an example. Data were collected through strategically placed monitoring points in a physical model, forming the basis for a quantitative analysis of slope deformation and failure patterns. The tested results revealed that sequential mining and rainfall activities induce progressively increasing stress variations, exhibiting a cyclical pattern of stress concentration, relaxation, and stabilization. Mining activities initiated the formation of tension and separation fractures within the model, which were subsequently exacerbated by rainfall. Rainwater infiltrating through these fractures and geotechnical bodies resulted in a more pronounced increase in water content in the upper part of the model than in the lower part. Displacement values within the model progressively increased with ongoing coal seam mining, primarily occurring in the mining area's roof and extending to the model's top following rainfall. The failure mode of the Pusa slope under combined mining and rainfall conditions is characterized by the following sequence: bending-pulling-subsidence-creep-dumping. The deformation and failure processes can be categorized into four stages: mining-induced disturbances, fractures extension and extension, formation of a potential collapse surface, and destabilization failure.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".