Assessing Landslide Susceptibility through Rock Cut Analysis: A Case Study of National Highway NH-919, India
Bibliographic record
Abstract
Abstract In the recent past, slope failures along highways have resulted in the destruction of infrastructure and loss of lives and properties throughout the world. Many issues of landslips, debris flow, and rockfalls in inhabited areas along road networks in hilly states of India, the USA, Australia, China, Canada, Brazil, etc., are testimony to this. In the present study, the slope stability of an overpass of National Highway NH-919 in the NCR region, India, has been analyzed. Field observations and petrographic analysis identified Quartzite with localized Schist presence. This study aims to identify the vulnerability of an existing road to landslide hazards and for conditional monitoring of roads for their stability in a present-day scenario of increasing vibrations of heavy vehicles and continuous seismicity in North-Western India, which are two important factors destabilizing the slopes along roads. During field assessment, a few major discontinuities were observed while collecting data at three probable failure locations. The hazard potential has been worked out by incorporating Landslide Susceptibility Score [LSS] and Landslide Hazard Evaluation Factor [LHEF], and the Slope Mass Rating [SMR] system has been used to identify the stability class. The DEM file, combined with the USGS DEM dataset, was used to generate thematic maps. Given the changing rainfall patterns and the heavy precipitation during the monsoon season, the chances of failure increase even more. The study also recommends applying simple mitigating measures to improve road stability and guarantee the long-term safety of residents and users.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".