Rock Fall Hazard Analysis for In-Pit Operations Potentially Impacting External Sensitive Areas
Bibliographic record
Abstract
Controlling rockfall-related risks is a requirement for safe pit operations and primarily mitigated through adequate bench geometry design and implementation. This paper presents a method for rockfall hazard analysis for in-pit operations potentially impacting external sensible areas, adapted from natural rockfall hazard analyses. The method considers the natural susceptibility to rockfalls pre-mining, rockfalls originated from bench failures, and those initiated as flyrock. Rockfall trajectory models are used to estimate the potential for blocks reaching exposed elements. Natural susceptibility to rockfalls and trajectories are used as a baseline on which to evaluate the potential effects of open pit operations on the environment and perceptions of communities in the area. The method is illustrated for an open pit in steep terrain in the Peruvian Andes at a feasibility level of study. The paper illustrates the flexibility for including considerations of pre-mining rockfall impacts on the external elements of interest, and for developing rockfall mitigation strategies that consider rock block velocities, heights, energies and the spatial distribution of trajectories. The results highlight the importance of considering the three-dimensional effects of the terrain on block trajectories, and how such insights allow for increasing the efficiency of resources available for rockfall protection structures.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".