Investigation of rockfall-prone road cut slope near Lengpui Airport, Mizoram, India
Bibliographic record
Abstract
Rockfall is one of severe natural hazards that are frequently reported in northeast region of India. It carries rock block falling from the cliff with high velocities and energies which can result in damages to vehicles, disruption to transportation, injuries and fatalities. The massive rockfall event which occurred in April 2017 on the highway NH-44A, near Lengpui Airport, blocked the traffic for 1 d, and fortunately, no casualties were reported as the event occurred in the night. This is the only highway connecting the Aizawl city to the airport and the region is highly prone to rockfall events. Hence assessment of rockfall along this highway is necessary. In the current study, rockfall hazard assessment has been carried out on three locations by rockfall hazard rating system (RHRS). During pre-failure analysis, the result shows that most hazardous slopes have RHRS score of 639. The slopes were found to be vulnerable and later on the rockfall activity occurred. Three-dimensional (3D) stability analysis has been carried out using 3DEC software package to analyze the failure behavior and to decide the rockfall-prone zone (unstable blocks) for slope. The total displacement of 2.24 cm and velocity of 2.25 mm/s of the failed block have been observed in the numerical analysis. Further, the rockfall vulnerable zone (unstable blocks) is considered to determine the parameters such as run-out distance, bounce height and energies of the falling rock blocks. The maximum total kinetic energy of 5047 kJ has been observed in the numerical analysis with the maximum run-out distance up to 18 m.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".