Comparison between Two New Ground-Based Remote Sensing Techniques for Rock Mass Characterization
Bibliographic record
Abstract
The convergence of advanced remote sensing technologies and analytical methodologies holds significant promise for bolstering the safety and sustainability of infrastructure development in geotechnical engineering. Rock mass characterization is a fundamental step for any rock-engineering project. However, surveying discontinuities in the rock masses is usually challenging and can be biased. This paper introduces novel approaches to leveraging remote sensing technology to analyze rock outcrops and rock slope cuts precisely. It delves into the specifics of two distinct portable techniques, metrology-grade laser scanner and SLAM-based laser scanner technology, and their respective efficacy in capturing engineering geological features, such as rock quality designation, discontinuity spacing, aperture, and surface roughness. The findings underscore the metrology-grade laser scanner’s superior ability to capture precise geological features compared to SLAM-based technology, which faces challenges related to uneven point distribution. While the metrology-grade laser scanner facilitates detailed analysis and accurate measurement at smaller scales, SLAM-based technology allows for swift data acquisition over larger areas with reduced processing time. A case study from Archer Point in British Columbia is presented to exemplify the practical implementation of these techniques.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".