Failure forecast for large rock slides by surface displacement measurements
Bibliographic record
Abstract
Forecasting the failure of large rock slides is difficult because of nonlinear time dependency and seasonal effects, which affect the displacements. Starting from the accelerating creep theory proposed by Voight, a method is suggested to forecast slope failures and to assess alert velocity thresholds using monitoring data. The 20 Mm 3 Ruinon rock slide (Valfurva, Central Alps, Italy), susceptible to evolve into a rock avalanche, is studied. Three different evolutionary patterns of displacements have been recognized through the analysis of the monitoring data for a 5 year period. Data representing the surface-based large-scale behaviour of the rock mass were fitted by power-law curves, according to the "accelerating creep" model. Voight's equation has been expressed in terms of displacement and used to fit the data by nonlinear estimation techniques. Values for the controlling parameters (A, α, t f ), representative of the mechanical behaviour of the rock mass approaching failure, have been determined both for single and multiple accelerating phases. "Characteristic velocity curves" have been computed by assuming these parameters are representative of the rock mass behaviour. Velocity threshold values for pre-alert, alert, and emergency phases have been computed. The method has been validated by collecting and analysing literature data for historical rock slope failures.Key words: slope stability, rock slide, accelerating creep, monitoring, failure forecasting, velocity thresholds.
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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 | 0.002 |
| 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".