Quantification of landslide velocity from active waveguide–generated acoustic emission
Bibliographic record
Abstract
Acoustic emission (AE) has become an established approach to monitor stability of soil slopes. However, the challenge has been to develop strategies to interpret and quantify deformation behaviour from the measured AE. AE monitoring of soil slopes commonly utilizes an active waveguide that is installed in a borehole through the slope and comprises a metal waveguide rod or tube with a granular backfill surround. When the host slope deforms, the column of granular backfill also deforms and this generates AE that can propagate along the waveguide. Results from the commissioning of dynamic shear apparatus used to subject full-scale active waveguide models to simulated slope movements are presented. The results confirm that AE rates generated are proportional to the rate of deformation, and the coefficient of proportionality that defines the relationship has been quantified (e.g., 4.4 × 10 5 for the angular gravel examined). It is demonstrated that slope velocities can be quantified continuously in real time through monitoring active waveguide–generated AE during a slope failure simulation. The results show that the technique can quantify landslide velocity to better than an order of magnitude (i.e., consistent with standard landslide movement classification) and can therefore be used to provide an early warning of slope instability through detecting and quantifying accelerations of slope movement.
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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) | 0.003 | 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".