A coupling method incorporating digital image processing and discrete element method for modeling of geomaterials
Why this work is in the frame
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Bibliographic record
Abstract
Purpose This paper aims to present a digital image processing (DIP)-based discrete element method (DEM) for the analysis of heterogeneous geomaterials. Taking a soil and rock mixture as an example, the direct shear test is used to illustrate the application of this method. The numerical result is validated by the laboratory experiment and implies its feasibility in the analysis of heterogeneous geomaterials. Design/methodology/approach This method has two major steps. Based on a modification of the connected-component labeling algorithm, a novel vectorization method, which can transform the digital photos to vectorized geometry automatically, is proposed first. Then, a simple yet effective method for the generation of heterogeneous DEM models is presented using the simulation of simplicity technique. Findings DIP-DEM method is a feasible approach for the analysis of mechanical behavior of heterogeneous material. For soil and rock mixtures (SRM), the horizantal deformation at peak shear point becomes larger with the normal stress. Compared with pure soil, the rock aggregates mainly improve the friction angle of SRM. Originality/value As a universal method taking advantage of both DIP and DEM, this method has broad application prospects in related fields.
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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 | 0.000 | 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 | 0.000 | 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 it