New techniques for characterising damage in rock slopes: implications for engineered slopes and open pit mines
Bibliographic record
Abstract
The stability of high rock slopes is becoming an increasingly important concern in the fields of mining and civil engineering. The need for mineral resources due to the exponential world population growth is driving the excavation of deeper and steeper open pit mines. Today, large open pit mines can reach depths in excess of 1 km. Maintaining and monitoring the stability of the excavation is of paramount importance to ensure the safety of miners, equipment, and mining operations, as well as the profitability of the mine. Despite safe, state-of-the-art mining practices being followed, pit slope deformations occur, usually controlled by geological factors and driven by the progressive accumulation of stress within the pit walls. The deformation of high engineered rock slopes is inevitably associated with the formation of slope damage features, such as rock mass dilation and bulging, brittle fracture and rockfalls. The progressive accumulation of slope damage can reduce the slope rock mass and discontinuity strength causing a decrease in stability, potentially resulting in slope failure. Blast damage, localised at the pit wall surface, may also promote rockfalls and increase the risk of slope instability. In this paper, we present the results of recent slope damage research undertaken in the Engineering Geology and Resource Geotechnics Group at Simon Fraser University. The focus of this ongoing research program includes the definition and characterisation of slope damage, modelling, monitoring and visualisation of slope damage. The factors and mechanisms that can promote and/or induce the accumulation of slope damage within engineered slopes are discussed. The role of engineering geological factors, including geological structures, rock mass quality, lithology, intact rock strength, stress magnitude and groundwater, are addressed and a preliminary rock slope damage interaction matrix approach is presented. Examples of the characterisation of damage using field mapping and remote sensing are presented. New methods of quantifying slope damage are also described. The range of numerical modelling techniques we have used in the investigation of rock slopes is outlined, with a focus on the explicit simulation of rock slope damage accumulation. The critical inter-relationship between slope damage and fracture connectivity is discussed with implications for pit slope design. The importance of continuous monitoring of slope deformation (damage) is highlighted both for the purposes of early warning systems, and as a means to constrain numerical simulations. Finally, a brief discussion on the potential applications of innovative, immersive geo-visualisation methods, such as mixed and virtual reality, in the interpretation of slope damage mechanisms in engineered slopes is provided.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".