Shear Response of Rough Rock Discontinuities Subjected to Impact Loading: Experimental Study and Theoretical Modelling
Bibliographic record
Abstract
Abstract The presence of discontinuities can significantly weaken rock masses, whose shear load-bearing capacity is always dictated by discontinuity failures. The shear response of rock discontinuity has been extensively studied under low loading rate conditions, while the effect of impact loading on its shear strength was unclear. To address this issue, rock discontinuity samples with quantified surface roughness were tested under six normal stresses (0%, 5%, 10%, 15%, 20%, and 25% of its uniaxial compressive strength) and different loading rates (varying from 100 MPa/ms to 700 MPa/ms) by a novel-designed impact shear testing system. Experimental results show that the impact shear strength is proportional to the loading rate and exhibits significant rate dependence. Moreover, the cohesion is clearly rate-dependent and the friction angle keeps constant during impact loading, which shows obviously different behaviors with those under static loading conditions. Considering the rate effect of the shear strength parameters, the shear strength criterion of rough rock discontinuity under impact loading was established. Furthermore, a statistical constitutive model was proposed by incorporating the Weibull distribution of the shear damage. The successful application of the theoretical model to the experimental data shows that the proposed models can well predict the shear strength and describe the shear damage of rough rock discontinuities under impact loading.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".