Study of rainfall induced landslide with different slope profile
Bibliographic record
Abstract
The Lesser Himalayan Shimla district experiences significant rainfall during the monsoons.Landslides caused by slope failures occur in the area during rainy seasons.In the Shimla district, landslides are a serious issue.Over the past few decades, more landslides & subsidence's have occurred as a result of the building of roads and structures on top of the weak geological structure.In this study, the finite element software was used to analyse the stability of the slope.In this work, a failing slope in the Shimla region of Himachal Pradesh is taken into consideration, and numerical modelling was used to examine its stability at various slope angles including before & after rainfalls of various intensities i.e., maximum rainfall, minimum rainfall, and average rainfall.Factor of safety before rainfall or pre-monsoon was greater than 1, indicating a stable slope.For the slope with slope angle 37.65° and 42° the factor of safety came out to be 0.992 and 0.928 at max. rainfall which is less than 1 that indicates an unstable slope.This study establishes the Himachal Pradesh region's high susceptibility to rainfall-induced landslides, and it identifies critical slip surfaces that may be useful for future mitigation efforts.
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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 | 0.000 | 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".