Enhancement of constant normal stiffness direct shear testing protocols for determining geomechanical properties of fractures
Bibliographic record
Abstract
Discontinuity behaviour can have a large impact on geotechnical engineering design; therefore, it is essential to determine their geomechanical properties to predict rockmass behaviour and mitigate any potential failure that may affect personnel safety or damage property. Geotechnical numerical software programmes that discretely simulate discontinuities rely on direct shear laboratory tests to provide the properties needed as inputs to these tools. This study presents valuable direct shear laboratory test results for the mechanical properties and behaviours of fresh, unweathered, rough fractures in the Pointe du Bois granite. The testing programme considers three separate boundary conditions: constant normal stress (CNL*) and two variations of constant normal stiffness (CNS). A new machine stiffness model is proposed to estimate the machine stiffness for tunnelling applications. Measurements of shear strength and a proposed secant dilation angle are assessed and critically evaluated. It is found in this study that there is no significant difference between shear strength parameters determined from direct shear data from CNL* and CNS boundary conditions when comparing maximum strength and residual strength. In addition, it is found that the proposed secant dilation angle, critical shear displacement, and total dilation potential all have a negative relationship with increasing normal stress.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".