Shear process of rock joints with two-order asperities based on the mobilizable shear strength theory
Bibliographic record
Abstract
Predicting shear characteristics of rock joints is crucial to the stability analysis of rock masses and early warning of pertinent engineering-geological disasters. Natural rock joints are rough with multi-scale asperities, and these asperities are degraded in the shear process with declining dilation. However, the progressive degradation of irregular asperities over shear remains thorny to quantify. Here we proposed an analytical model for the shear behavior of rock joints with two-order asperities within the framework of the mobilizable shear strength theory initiated by the first author. The asperity is shaped sinusoidal, and its geometrical properties are quantifiable by wavelet analysis of a natural profile. The degradation and dilation of each sinusoidal-shaped asperity undergoing shear are predicted by first locating the attack point of the asperity profile. The attack point is determined by equating the slope of the sinusoidal curve and the mobilizable dilation angle as a function of the accumulated elastic shear work at the end of the elastic stage. The succeeding dilation and degradation are evaluated by considering the progressive area reduction of the sinusoidal-shaped asperity as the plastic shear work accumulates. The performance of the analytical model is demonstrated by predicting the shear stress/dilation-shear displacement relationships of synthetic and natural joints with sinusoidal-shaped and irregular asperities. The new model has a great potential to predict the shear resistance of rock joints for assessing the stability of natural and engineered rock structures and joint-slip-induced disasters.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".