Experimental and numerical analysis of rainfall-induced slope failure of railway embankment of semi high-speed trains
Bibliographic record
Abstract
Abstract Safety and maintenance of railway tracks has been very crucial, for sustainable economic development of many nations. Almost the entire Indian railway tracks were built over the raised earth embankments. These embankments are susceptible to slope failure due to numerous reasons. One of the major cause is seepage and surface runoff during rainy (monsoon) season. Erosion by gullying has regarded as most significant failure scar. Various researches had studied the embankment failure due to rainfall. However, the gullying effect on the slope failure has been missing in these studies. Hence, in this study slope stability analysis of the railway embankment has been performed considering the gullying. Embankment of Dedicated Freight Corridor (India) has been taken up in this study. The present study has three sections (a) Field observation, (b) scaled laboratory modelling, and (c) FEM-based numerical analysis. The effect of vegetation, degree of compaction, and the intensity of rainfall on the slope stability has been evaluated. Effect of gullying has incorporated through change in shape and dimension of embankment. It has been found that vegetation significantly reduced the gully formation and also the less compacted slope experienced more gullies formation as compared to the more compacted slope. While varying the rainfall intensity from 20 to 100 mm, it has been observed that without consideration of gully higher FOS (factor of safety) was reported. Moreover, FOS decrease with increase of rainfall from 20 to 100 mm and becomes constant after that.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.002 | 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".