Anisotropic mechanical characterization of gneissic rock from Canadian Shield: Bridging the micro- and meso-scale gap
Bibliographic record
Abstract
The microstructure and fabric of rocks largely control their mechanical behavior, and their spatial variations can lead to anisotropic behavior. Metamorphic rocks such as gneiss exhibit anisotropy, and characterizing this anisotropy is crucial in geoscientific and engineering applications including geothermal plays, active fault zones, and mining sites. We investigate a foliated gneiss from the French River area of the Canadian Shield to determine its mechanical properties and assess the impact of anisotropy across different scales. We combined micro-scale experiments (e.g. nanoindentation and optical and electron microscopy), with meso-scale experiments (e.g. unconfined compressive strength (UCS) and indirect tensile test), to attempt bridging the micro-to meso-scale elastic property gap. Our results show that micro- and meso-mechanical properties of gneiss are orientation-dependent across scales. Young's modulus, upscaled from nanoindentation testing, varied between 51 GPa and 74 GPa, while meso-scale Young's modulus from UCS tests varied between 45 GPa and 54 GPa. The ultrasonic velocities (P- and S-wave) exhibited anisotropy of 26% and 24%, respectively, while the estimated UCS anisotropy was 30%, with the highest values observed in the direction parallel to the foliation. The direction of the mineral alignment forming the foliation plane plays a crucial role in determining the failure pattern of the rock. We observed predominantly tensile failure in samples with 0°–15° foliation plane angle, shear-slip failure for samples with 20°–65°, and a conjugate shear failure in the sample at 90° foliation plane angle to the loading direction. These findings provide insight into the anisotropic (orientation-dependent) characterization of foliated metamorphic rocks, which can be useful in rock engineering applications and numerical simulations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".