A Review of Relationship between Texture Characteristic and Mechanical Properties of Rock
Bibliographic record
Abstract
The textural characteristics of rocks influence their petrophysical and mechanical properties. Such parameters largely control rock mass stability. The ability to evaluate both immediate and long-term rock behaviors based on the interaction between various parameters of rock texture, petrophysical and mechanical properties is therefore crucial to many geoengineering facilities. However, due to the common lack of high-quality core samples for geomechanics and rock texture laboratory tests, single and multivariable regression analyses are conducted between mechanical properties and textural characteristics based on experimental test data. This study presents a review of how rock texture characteristics influence the geomechanical properties of a rock, and summarizes the regression equations between two aspects. More specifically, a review of the available literature on the effects of mineralogy, grain size, grain shape, packing density, foliation index, porosity, degree of weathering, and other rock physical characteristics on geomechanics is presented. Similarly, a review of the literature discussing the failure criteria of anisotropic rocks, both continuous and discontinuous, is also presented. These reviews are accompanied by a comparison of the fundamentals of these methods, describing their equations and discussing their advantages and disadvantages. This exercise has the objective of providing better guidelines on how to use these criteria, allowing for safer underground excavations via an improved understanding of how rock texture parameters affects the mechanical behavior of rocks.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".