A novel approach for micro‐scale characterization and modeling of geomaterials incorporating actual material heterogeneity
Bibliographic record
Abstract
The mechanical response of geomaterials is highly influenced by geometrical and material heterogeneity. To date, most modeling practices consider heterogeneity qualitatively and their choice of input parameters can be subjective. In this study, a novel approach to combine a detailed micro‐scale characterization with modeling of heterogeneous geomaterials is presented. By conducting grid micro‐indentation and micro‐scratch tests, the instrumented indentation modulus and fracture toughness of the constituent phases of a crystalline rock were obtained and used as accurate input parameters for the numerical models. Additionally, X‐ray micro Computed Tomography (CT) was used to obtain the spatial distribution of minerals, and thin section analysis was performed to quantify the microcrack density. Finally, a Brazilian disc test was modeled using a Combined Finite‐Discrete element method (FEM/DEM) code. Compared with the laboratory results of a sample that was initially CT scanned, the simulation results showed that by incorporating accurate micromechanical input parameters and the intrinsic rock geometric features such as spatial phase heterogeneity and microcracks, the numerical simulation could more accurately predict the mechanical response of the specimen, including the fracture patterns and tensile strength. It is believed that the proposed micromechanical approach for evaluating the material properties and the sample geometry can be readily applied to other problems to accurately model the mechanical behavior of heterogeneous geomaterials.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".