Grain-Based Modeling Of The Macro-Mechanical Behavior Of Crystalline Rock Considering The Heterogeneity Of Grain Boundary Contacts
Bibliographic record
Abstract
Grain-Based Model (GBM) based on the Discrete Element Method (DEM) has been widely used to simulate the rock specimen under load realistically.A common problem in the current implementation of GBM is that only a single bond parameter is used on the grain boundaries.However, there are several minerals present in the rock specimens.For this purpose, this study focuses on accurately simulating the rock specimen based on the GBM model by considering various grain boundary contacts between different minerals.A GBM was proposed and incorporated into the Particle Flow Code (PFC), which can capture the intergranular textures of rock.The model is applied to reproduce the laboratory response of Lac du Bonnet granite.An iterative calibration approach is developed to match the unconfined compressive (UCS) of the models to those of the laboratory results.The transition in the failure mode, stress-strain response, and the evolution of inter and intra-grain micro-cracks were systematically examined and compared with those derived from the traditional model with a single bond parameter for grain boundary.The results show that the inter cracks are more dispersed in the specimen in the new model, especially at the pre-peak stage.At the post-peak stage, inter-grain tensile and shear cracks still dominate the microcrack distributions in the traditional model; in contrast, the intra-grain cracks dominate the microcrack distributions in the new model.The order in which the micro-cracks appear significantly differs between traditional approaches and the new model.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".