Integrating monitoring data into risk assessment and management for rock slopes
Bibliographic record
Abstract
Large open pit and natural rock slope monitoring methods have become increasingly available and useful with advances in equipment, analysis techniques and data integration. The toolbox of remote and in situ instrumentation provides a wealth of opportunities to collect data and inform deformation-based analyses. As a result, observational design approaches are increasingly being adopted and are of benefit, as long as they are well integrated into risk assessments and the consequence of potential failures is well understood. As monitoring data becomes increasingly available, we are able to consider the deformation capacity of slopes, particularly in post failure event back-analyses. The capacity for slope deformation prior to failure ranges from very small to very large strains, depending on the failure mechanism, which, in turn, depends on the geological and rock mass characteristics. Small strain deformations and failure modes must be identified early, so that the risk of failure can be assessed and mitigated if required. This relies on understanding the geological setting, and, in particular, the structural controls on the slope’s stability. Case histories from several locations will be discussed within this framework.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".