A Comprehensive Comparison Between Discrete Fracture Network and Generalized Anisotropic Material Behavior for Modeling Jointed Rock Mass
Bibliographic record
Abstract
ABSTRACT: Rock masses are blocky assemblages formed by networks of natural discontinuities (fractures), such as joints, foliations, shear zones or faults, formed by geological activities. To accurately predict the mechanical behavior of rock mass, it is essential to consider these fractures. The complex interaction between intact rock and those fractures coupled with man-made excavation imposes critical limitations to predict rock mass behavior solely via kinematic or analytical methods. In this context, numerical modeling approach fills the need for a powerful tool that can provide geotechnical engineers with sophisticated solutions to complex rock mechanics problems. This paper demonstrates different approaches to model joint sets in 3D Finite Element Method (FEM) to model discontinuities in jointed open pit mine. They include the explicit representation of discontinuities using Discrete Fracture Network (DFN) and incorporation of a special constitutive model that considers anisotropic material behavior to the solid elements that implicitly account for the presence of joint sets. The investigation presents findings from simulations involving models with single and multiple joint sets. Notably, the focus is given to validating the use of DFN and the benefits for slope stability assessment. The obtained results showed high levels of agreement between the two modeling approaches, underscoring the efficacy of DFN in replicating the complex behavior of geological structures. These insights contribute to the enhancement of the rock slope designs with significant implications for the safety and profitability of mining operations. 1. INTRODUCTION In open pit mining, the primary objective is to maintain the structural integrity of the slope surface and prevent/mitigate any potential failures. Achieving a cost-effective yet stable design for open-pit mines requires a comprehensive grasp of lithology, rock mass characteristics, and structural geology. Thus, conducting a robust slope stability assessment for open pit excavations is of utmost importance to ensure the safety of mining operations. The presence of geological structures (also termed as natural discontinuities) such as joints, foliations, and faults within rock mass introduces complexities in stress distribution and stability, which are unique to the geological history of different regions. Therefore, a thorough consideration of natural discontinuities is imperative in geomechanical analyses and design exercises to gain accurate prediction of rock mass behavior. They play a pivotal role in controlling the driving failure mechanism to slope instability. With respect to the distribution, surface condition, and orientations of discontinuities, the mechanical response of rock mass should vary extensively.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".