Shear behavior of two-order asperities in three-dimensional rock joints: Experimental investigation and development of a morphology-based shear strength criterion
Bibliographic record
Abstract
Rock instability is predominantly driven by the shear failure of rock joints, with joint morphology playing a critical role in governing shear behavior. Most existing studies emphasize overall joint morphology, often neglecting the distinct contributions of first- and second-order asperities. To address this limitation, this paper systematically investigates the roles of waviness and unevenness in influencing the shear behavior of rock joints. Joint morphology was decomposed using three-dimensional laser scanning and wavelet transformation techniques. Digital carving technology was employed to fabricate rock joint specimens, which underwent parallel direct shear tests. The results indicate that waviness primarily governs peak shear strength, while unevenness contributes to shear behavior during the pre-peak stress accumulation stage. Overestimating the contribution of unevenness results in an inaccurate assessment of roughness effects on peak shear strength. At high normal stresses, increased damage to waviness contributed to shear strength increments. Based on these findings, a shear strength criterion was developed, integrating the differential morphological contributions of waviness and unevenness. The proposed criterion demonstrated superior predictive accuracy when validated against experimental data. This work provides a deeper understanding of the multi-order asperity contributions to shear behavior.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".