Assessing Stress Variability in Fractured Rock Masses with Frictional Properties and Power Law Fracture Size Distributions
Bibliographic record
Abstract
Abstract The presence of fractures in rock masses plays a major role in its stress state and its variability. Each fracture potentially induces a stress perturbation, which is correlated to its geometrical and mechanical properties. This work aims to understand and quantitatively predict the relationship between fractured systems and the associated stress fluctuations distribution, considering any regional stress conditions. The approach considers the rock mass as an elastic rock matrix into which a population of discrete fractures is embedded—known as a Discrete Fracture Network (DFN) modeling approach. We develop relevant indicators and analytical solutions to quantify stress perturbations at the fracture network scale, supported by 3D numerical simulations, using various fracture size distributions. We show that stress fluctuations increase with fracture density and decrease as a function of the so-called stiffness length, a characteristic length that can be defined as the ratio between Young’s modulus of the matrix and fracture stiffness. Based on these considerations we discuss, depending on DFN parameters, which range of fractures should be modeled explicitly to account for major stress perturbations in fractured rock masses.
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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 | 0.002 |
| 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 | 0.000 |
| 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".