Discontinuity surface orientation extraction and cluster analysis based on point cloud data
Bibliographic record
Abstract
Measurement of the orientation of discontinuity surfaces such as joints, fractures, and faults in rock masses is an essential part of research in the fields of geotechnical engineering. This paper proposes a method for extracting rock discontinuity surfaces by combining several algorithms. First, the unit normal vector information of the points in the point cloud is determined using the K-dimension tree (K-d tree) process. Next, the OPTICS algorithm filters out the discontinuity surface sets and the discontinuity surfaces within each set. Finally, Fuzzy C-Means (FCM) is employed to determine the cluster center orientations of the selected discontinuity surface sets and the orientations of the individual discontinuity surfaces. To validate the effectiveness of our method, we use two publicly available standard geometric point cloud models to extract the orientations of the structural surfaces. Then, we also use an actual rock outcrop to further validate the reliability of the method in extracting joint surface sets. The method covers most of the point cloud information and improves the accuracy of the measurements compared to our previous findings. This paper provides an alternative method for extracting discontinuity surfaces and determining the orientation in real 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".