A method to evaluate the three-dimensional roughness of fracture surfaces in brittle geomaterials
Bibliographic record
Abstract
Conventionally, the evaluation of fracture surface roughness in brittle geomaterials, such as concrete and rock, has been based on the measurement and analysis of two-dimensional profiles rather than three-dimensional (3D) surfaces. The primary reason for doing so was the lack of tools capable of making 3D measurements. However, in recent years, several optical and mechanical measurement tools have become available, which are capable of quickly and accurately producing high resolution point clouds defining 3D surfaces. This paper provides a methodology for evaluating the surface roughness and roughness anisotropy using these 3D surface measurements. The methodology is presented step-by-step to allow others to easily adopt and implement the process to analyze their own surface measurement data. The methodology is demonstrated by digitizing a series of concrete fracture surfaces and comparing the estimated 3D roughness parameters with qualitative observations and estimates of the well-known roughness coefficient, R(s).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".