A Critical Review of Current States of Practice in Direct Shear Testing of Unfilled Rock Fractures Focused on Multi-Stage and Boundary Conditions
Bibliographic record
Abstract
Direct shear (DS) is a common geotechnical laboratory test used to determine strength and deformation properties of rock discontinuities, such as normal and shear stiffness, peak and residual shear strength, and dilation. These are used as inputs for discontinuous geomechanical numerical models to simulate discontinuities discretely and shear strength is often expressed by Mohr–Coulomb, Patton, or Barton–Bandis constitutive models. This paper presents a critical review of the different boundary conditions and procedural techniques currently used in practice, summarizes previous contributions, addresses their impacts on interpreted results for rock engineering design, and introduces clarifying terminology for shear strength parameters. Based on the review, the authors advise that constant normal stress is best suited for discrete numerical-model-based rock engineering design in dry conditions, but constant normal stiffness should be considered where fluid permeability is of interest. Multi-stage testing should not be used to obtain peak shear strength values except for stage 1, because of accumulating asperity damage with successive shear stages. Nevertheless, if multi-stage testing must be employed due to limited budget or specimen availability, guidance is presented to improve shear strength results with limited displacement techniques.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".