Recent developments in unmanned aerial vehicle (UAV) surveys for rock slope stability analysis—a review
Bibliographic record
Abstract
The existence of most of the cut and natural slopes in morphological complex areas makes it impossible to characterize the discontinuities along the rock slope using conventional scanline mapping and geological compass. Consequently, the unmanned aerial vehicle (UAV) survey has gotten greater attention from numerous researchers in recent decades. This research presents a detailed review of the UAV surveys and their applicability of precise determination of discontinuity orientations and spacings along the rock slope. As the discontinuities characterization is carried out in rock slope only, therefore the scope of review article is limited to rock masses. The review article is composed of three sections. The first section compares the applicability of various UAV sensors followed by detail explanation of fundamental principles of UAV survey, such as data acquisition, data processing, and extraction. The next section provides a brief introduction and comparison of various types of UAVs platforms. It is followed by review of the applications of UAV photogrammetry for slope stability assessment, such as kinematic analysis, rock mass quality characterization, and slope deformation monitoring based on previous work. The primary aim of this review article is that no review article was published previously that assess the applicability of UAV photogrammetry in characterization of the entire slope on limited number of Rock Quality Designation (RQD) data. In this paper, a real case study is presented to estimate the area of various RQD indices along the slope. Based on the RQD maps, the area of poor, fair, good, and excellent rock were computed as 1382, 3039, 393, and 37 m2. This shows that the UAV can obtain precise discontinuity spacing and is a reliable way of characterizing the rock mass quality. The previous study reveals that UAV photogrammetry is currently an extensively applied approach for performing kinematic analysis, and slope deformation study. However, its application in rock mass quality characterization is not yet fully adopted. Furthermore, the capability of UAV to be equipped with various geophysical equipment, such as gravity, ground penetrating radar (GPR), and electromagnetic instrument forecast it increasing applications in future for geotechnical investigation.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".