Dynamic Shear Strength of Rock Joints and Its Influence on Key Blocks
Bibliographic record
Abstract
Shear strength deterioration of rock joints and its induced instability of key blocks subjected to dynamic disturbance constitute the mechanism of many geological disasters such as rockburst, landslide, and rockfall. In this study, the influence of dynamic disturbance on rock joints was quantified in the form of stress, of which model was established by quasistatic method. The dynamic shear strength of rock joints was analyzed theoretically following the Coulomb-Navier criterion and further investigated experimentally by split Hopkinson pressure bar (SHPB) tests with impact velocities of 4.850 m/s, 8.722 m/s, 12.784 m/s, and 16.935 m/s. The effect of engineering disturbance on shear strength of rock joints was measured, and a degradation coefficient was used to describe it quantitatively. Considering the degradation of dynamic shear strength of rock joints and its influence on block stability, the method for determining key blocks under dynamic disturbance was given. Implementing this method into the program of Geotechnical Structure and Model Analysis-3D (GeoSMA-3D) developed by the authors’ team, the visualization of key blocks was achieved. From theoretical analysis and experimental investigation, it was figured out that the shear strength of rock joints is degraded under dynamic disturbance. The degradation of friction angle becomes more sensitive than that of cohesion. Additionally, numerical results show that the number of key blocks was increased with the increasing impact velocities.
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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 | 0.001 |
| 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 | 0.000 |
| 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".