Effect of Disturbance Factor Distribution Function on Stability of an Open Pit Mine
Bibliographic record
Abstract
ABSTRACT: The disturbance factor (D) is a parameter in the Generalized Hoek-Brown failure criterion for rock slopes in slope stability. It represents the subsurface damage to the rock material properties resulting from blasting and stress relaxation during excavations. Within the region of assumed damage, a number between zero (undisturbed) and unity (very disturbed) is prescribed as the value of the disturbance factor. Most commonly a uniform value of D is assumed within the entire region of damage, but little research has been done to study the impact of the variation in the D parameter on stability. Through use of an example, this paper examines the effect of various distribution functions of D through the damaged region, such namely, as constant, linearly varying, and exponentially varying. The failure surfaces and factors of safety for the slope as determined via limit equilibrium are also compared with finite element analyses. Varying the distribution of the damage function was found to significantly affect the failure surface and factor of safety. It is recommended that practitioners adopt care to select an appropriate distribution for slope stability analysis. 1. BACKGROUND The Generalized Hoek-Brown method (Hoek and Brown 2018) is widely used for determining rock mass strength in rock mechanics. One application of this method is in the design of open pits in rock masses which requires the evaluation of factor of safety against overall sliding. During excavation, subsurface damage can occur to the surrounding rock masses and cause fracturing. The damage can be caused by two sources: (a) blasting and (b) stress relaxation (Hoek et al. 2002), and can be quantified in the form of a disturbance factor, D, which ranges from a zero (undisturbed) to unity (disturbed) within the rock masses. For slope stability, the shear strength of the rock activated along the slip surface can be obtained by solving Eq. (1) to Eq. (4) (Hoek et al. 2018).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".