Numerical Brazilian split test of pre-cracked granite with randomly distributed micro-components
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the mechanical behavior and propagation of cracks of numerical granite samples through the Brazilian split test and to provide a reference for predicting the behavior of real granite samples. Design/methodology/approach The numerical models of granite containing two fissures are established using the parallel bond model (PBM) and the smooth joint model (SJM) in PFC2D. The peak stresses, number of cracks and anisotropic ratios are obtained to study the influence of the mineral composition and the angle of inclination of rock bridge on the strength, failure mode and deformation characteristics. Findings The numerical results obtained show that the mineral composition has a marginal influence on the peak stress. When the angle of inclination of rock bridge β increases, the peak stress drops to its minimum value at β = 90° and then gradually increases to a relatively low level. The behavior of cracks falls into three categories based on the distribution of cracks. By analyzing the stress–strain curve and the process of crack propagation for sample No. 4 with β = 60°, it is found that the process of failure can be divided into four stages and tensile cracks dominate. The anisotropic ratios of peak stress and a number of cracks obtained show that the peak stress is low anisotropic and the number of cracks is medium anisotropic. Originality/value This paper presents a numerical simulation method to analyze mechanical behavior and propagation of cracks under different conditions. The proposed method and the results obtained are useful for predicting the behavior of real granite samples in laboratory and engineering projects.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".