Fracture Undulation Modelling in Discontinuum Analysis: Implications for Rock-Mass Strength Assessment
Bibliographic record
Abstract
Synthetic rock mass (SRM) models commonly represent fractures as planar surfaces, potentially oversimplifying the complex geometries observed in natural rock masses. This study investigates whether incorporating large-scale fracture undulations significantly affects predicted rock-mass strength compared to conventional flat joint representations. Using the Finite-Discrete Element Method (FDEM), we analyzed multiple discrete fracture network (DFN) configurations under uniaxial and biaxial loading conditions, comparing models with geometrically simplified planar fractures against those incorporating conceptual undulated surfaces. Results reveal counterintuitive and inconsistent patterns across different DFN geometrical realizations, demonstrating that network topology and connectivity patterns govern overall behaviour more than individual fracture geometry. These findings challenge assumptions that geometric simplification can be systematically compensated through parameter adjustments. However, given that detailed fracture characterization data are typically unavailable until design completion, and even accessible rock outcrops provide only limited 2D surface exposures of inherently 3D fracture networks, pursuing sophisticated geometric representations may be impractical. Instead, engineering practice should focus on quantifying inherent variability bounds.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".