Automatic identification of discontinuities and refined modeling of rock blocks from 3D point cloud data of rock surfaces
Bibliographic record
Abstract
The spatial distribution of discontinuities and the size of rock blocks are the key indicators for rock mass quality evaluation and rockfall risk assessment. Traditional manual measurement is often dangerous or unreachable at some high-steep rock slopes. In contrast, unmanned aerial vehicle (UAV) photogrammetry is not limited by terrain conditions, and can efficiently collect high-precision three-dimensional (3D) point clouds of rock masses through all-round and multiangle photography for rock mass characterization. In this paper, a new method based on a 3D point cloud is proposed for discontinuity identification and refined rock block modeling. The method is based on four steps: (1) Establish a point cloud spatial topology, and calculate the point cloud normal vector and average point spacing based on several machine learning algorithms; (2) Extract discontinuities using the density-based spatial clustering of applications with noise (DBSCAN) algorithm and fit the discontinuity plane by combining principal component analysis (PCA) with the natural breaks (NB) method; (3) Propose a method of inserting points in the line segment to generate an embedded discontinuity point cloud; and (4) Adopt a Poisson reconstruction method for refined rock block modeling. The proposed method was applied to an outcrop of an ultrahigh steep rock slope and compared with the results of previous studies and manual surveys. The results show that the method can eliminate the influence of discontinuity undulations on the orientation measurement and describe the local concave-convex characteristics on the modeling of rock blocks. The calculation results are accurate and reliable, which can meet the practical requirements of engineering.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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 | 0.001 |
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".