Tilting deformation analysis and instability prediction of arch-locked-segment landslides induced by rainfall
Bibliographic record
Abstract
The failure of locked-segment landslides is associated with the destruction of locked segments that exhibit an energy accumulation effect. Thus, understanding their failure mode and instability mechanism for landslide hazard prevention and control is critical. In this paper, multiple instruments, such as tilt sensors, pore water pressure gauges, moisture sensors, matrix suction sensors, resistance strain gauges, miniature earth pressure sensors, a three-dimensional (3D) laser scanner, and a camera, were used to conduct the physical model tests on the rainfall-induced arch locked-segment landslide to analyze the resulting tilting deformation and evolution mechanism. The results indicate that the tilting deformation characteristics in the locked segment are consistent with the variation in its strain, stress, hydrodynamic responses, and slope morphology, suggesting that tilting deformation can serve as a novel monitoring approach for landslide instability. Further, the tangent angle method and the tilting rate reciprocal method can be utilized to predict the landslide instability based on the landslide tilting deformation curve. The effectiveness of this method is validated in the Huangzangsi dam area, which provides theoretical foundations for understanding the catastrophic mechanism and instability prediction of arch-locked-segment landslides.
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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 | 0.000 |
| 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 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".