An Improved Shear Stress Monitoring Method in Numerical Direct Shear Tests by Particle Flow Code
Bibliographic record
Abstract
Abstract During a direct shear test in particle flow code (PFC; Itasca Consulting Group, Minneapolis, MN) simulation, the initial force is often neglected by the existing methods of monitoring shear stress, which results in a difference between the monitoring data and simulation result. An improved monitoring method, which involves monitoring the resultant force on the left and right walls (specimen boundary) as the shear force, is proposed to monitor the shear stress in this study. The effectiveness of the proposed method is first investigated by comparing the monitoring results with the peak shear strength of sawtooth triangular joints with base angles of 0°, 15°, and 30° and joints with Barton’s standard joint roughness profiles (joint roughness coefficient (JRC) = 5.8, 10.8, and 14.5) as predicted by well-known classical shear models under different normal stress conditions. Additionally, the capabilities of the proposed monitoring method were tested by comparing it with the peak shear strength obtained from laboratory direct shear tests on a real rock joint (JRC = 1.0) under different normal stress levels. Good agreement was found between the results of the numerical models that used the proposed monitoring method and well-known classical shear models as well as laboratory direct shear tests. The proposed method has a highly accurate capacity for determining the shear stress in numerical direct shear tests by PFC. This method has advantages for assessing joints with low roughness coefficients under low normal stress conditions. The results reveal that the proposed method reasonably reflects the shear characteristics of rock joints in numerical direct shear tests. The proposed method also contributes to the calibration of microparameters for a smooth joint model to more accurately represent the rock joint.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".