High-resolution ground-deformation and support monitoring using a portable handheld LiDAR approach
Bibliographic record
Abstract
Light detection and ranging (LiDAR) technology plays a strategic role in the design and maintenance of underground support systems. Ground deformation, and by extension, ground support monitoring, are typically performed using tripod or wall-mounted survey-grade tools, single-point to multipoint measurements or by simple visual inspection. These traditional data-collection practices offer limited quality control, decreased accuracy and minimal standardisation across geotechnical personnel. As more portable underground LiDAR solutions become available, mine sites are integrating them into their daily underground inspections. Using a LiDAR-based approach, ground convergence and subsequent deformation monitoring can be completed using a model-to-model comparison between two scans taken at the same location but at different points in time. The comparison uses a distance computation to calculate the relative change between the two scans and generates a heat map based on the results. This paper presents a case study on the lowest reliable detection threshold for relative ground deformation by a handheld LiDAR solution. Mine sites with relatively small ground displacements require very high-resolution point clouds to achieve useful results in a model-to-model comparison. This paper aims to demonstrate that handheld LiDAR solutions can meet point cloud resolution and productivity requirements to efficiently capture small ground displacements in underground mining operations. This is achieved through the use of a portable infrared LiDAR device, which contains an integrated onboard attitude and heading reference system (AHRS). The combination of the infrared LiDAR and AHRS allows for the capture of georeferenced scans in seconds, which increases the efficiency of the data capture and downstream postprocessing workflow. Improving the fidelity of the data captured for ground deformation and support monitoring can result in safer excavations for both personnel and equipment, as well as more economical design and timelier support rehabilitation interventions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".