Failure characterization in rock engineering using a unified DFN-FDEM analysis approach
Bibliographic record
Abstract
This thesis presents a unified approach for better understanding the failure mechanisms associated with naturally fractured rock masses. With the advances in computing power, it is now possible to simulate rock engineering problems in a more realistic manner by including the effects of natural fracture networks. By integrating discrete fracture networks (DFN) with hybrid finite-discrete element models (FDEM), it is possible to fully account for block kinematics, fracturing processes and to capture anisotropic behaviour in an explicit manner. Key contributions include: i) Developing techniques to provide geometrical and statistical analysis of discrete fracture networks prior to implementation in geomechanical software; ii) Developing techniques that allow for direct integration of discrete fractures in FDEM models by solving key issues relating to generating good quality finite element meshes and minimizing the presence of distorted elements. iii) Developing a series of techniques to improve on the analysis of FDEM outputs; the proposed methods are capable of calculating the length of induced fractures and tracking whether these fractures interact with existing fractures to form new blocks. iv) Developing methods to capture block formation directly from simulation mesh data, skipping the simplification and inaccuracy of fracture lines, by utilizing graph data structures and graph theory. v) Developing methods to analyze blocks spatially within each timestep, and more importantly, introducing techniques relating those blocks and their analysis temporally, across timesteps. The entire set of newly developed techniques is applied to investigate the behaviour exhibited in simulated pillar models using existing empirical and numerical methods to make the problem more tractable and allow calibration of the results. The innovative techniques developed in this thesis are generic and could be applied to a variety of rock engineering problems in which it is important to determine the relative role of brittle and structurally controlled failures, including block caving fragmentation, and slope and tunnel stability analysis. The techniques developed in this thesis are defined as sets of rules to be followed in certain order and they provide the ground work needed to utilize machine leaning algorithms to investigate the relationship between initial DFN configuration and structural and brittle damage.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".