Development of an early-warning time-of-failure analysis methodology for open-pit mine slopes utilizing ground-based slope stability radar monitoring data
Bibliographic record
Abstract
The recent introduction of ground-based slope stability radar in open-pit mines to complement conventional geodetic monitoring programs provides near real-time deformation measurements over a broad coverage area; this allows geotechnical engineers to observe the spatial distribution of pit wall movements and their progression over time. This paper presents a newly proposed early warning time-of-failure (TOF) analysis procedure for use in real-time with ground-based radar measurements designed to be integrated in an open-pit mine’s trigger action response plan (TARP). The inverse-velocity and slope gradient (SLO) TOF analysis methods are applied to radar displacement measurements using a new systematic multi-pixel selection technique termed the “percent deformation method.” The utilization of the percent deformation method in the proposed real-time TOF analysis methodology gives more-reliable results than current practice by providing recommendations for pixel selections, data filtering, where and how to undertake TOF analyses, and presenting TOF results in real time. The addition of a more rigorous, methodical treatment of radar monitoring data when faced with critical slope instability will reduce uncertainty and increase confidence in any trigger action response decisions, helping to ensure a safer work environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".