A review of the geological characterization, classification, modeling, and case studies of anisotropic rock masses
Bibliographic record
Abstract
Rock anisotropy caused by inherent structures like bedding, foliation, and micro-fractures directly influences strength, deformability, and stress distribution variations. These directional changes can affect the stability of rock engineering practices, such as underground openings and slopes, and dealing with the anisotropic rock masses (ARMs) is one of the significant challenges. The commonly used conventional classifications are solely based on the isotropic behavior of rock masses and are unsuitable for anisotropic ones. Despite the limitations of these classifications, engineers tend to oversimplify the situation and characterize or design the ARMs, ignoring the impact of anisotropy. This study presents a summary of geological conditions, mechanical behavior, and classification systems of ARMs, as well as a review of numerical modeling techniques that may be applicable in the design phase within such medium. ARM Rating (ARMR), or any other type of alternative classification system that considers the directions in which rocks act instead of just their strength levels, can facilitate improved feasibility analysis for complex geological conditions and supporting systems design in ARMs. Moreover, the failure criteria considering the anisotropic behavior reflect the nonlinear development with long-term dependence on rock strength. Such criteria may be applied to numerical methods, such as the discrete element method (DEM), which offers more or less realistic simulations of ARMs' responses. Nevertheless, establishing standard procedures for the characterization, classification, and design of ARMs, especially in deep underground anisotropic conditions, is in high demand.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".