Twenty-ninth Canadian Geotechnical Colloquium: The role of advanced numerical methods and geotechnical field measurements in understanding complex deep-seated rock slope failure mechanisms
Bibliographic record
Abstract
The underlying complexity associated with deep-seated rock slope stability problems usually restricts their treatment to phenomenological studies that are largely descriptive and qualitative. Quantitative assessments, when employed, typically focus on assessing the stability state but ignore factors related to the slope’s temporal evolution including rock mass strength degradation, internal shearing, and progressive failure, all of which are key processes contributing to the final collapse of the slope. Reliance on displacement monitoring for early warning and the difficulty in interpreting the data without a clear understanding of the underlying mechanisms has led to a situation where predictions are highly variable and generally unreliable. This paper reviews current knowledge regarding prefailure mechanisms of massive rock slopes and current practices used to assess the hazard posed. Advanced numerical modelling results are presented that focus on the importance of stress- and strain-controlled rock mass strength degradation leading to failure initiation. Efforts to address issues related to parameter and model uncertainty are discussed in the context of a high alpine research facility, the “Randa In Situ Rockslide Laboratory”, where state-of-the-art instrumentation systems and numerical modelling are being used to better understand the mechanisms controlling prefailure deformations over time and their evolution leading to catastrophic failure.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".