A new paradigm in ground support monitoring through ultrasonic monitoring of clusters of rockbolts
Bibliographic record
Abstract
In most of today’s underground mines, ground support monitoring is mainly conducted through using microseismic sensors, LiDAR, extensometers, cameras, or visual inspection. These monitoring tools are complementary in nature. Due to high costs associated with purchase, installation, maintenance and utilisation, they are usually deployed or used at sparsely selected critical locations, some of them on a noncontinuous basis. This means that some important pieces of information on ground support conditions may be missing either location-wise or time-wise. In the last four years, the Energy, Mining and Environment Research Centre of the National Research Council Canada (NRC), in collaboration with CanmetMINING of Natural Resources Canada (NRCan), has developed next generation ultrasound rockbolt sensors (RBSTM) for monitoring load change and deformation experienced by rockbolts. Intrinsically low costing and installation onto exposed end of rockbolts using production bolters, the technology is meant to be deployed on a large number of rockbolts whereby the instrumented rockbolts become a network of ground condition sensors to provide on-demand 3D mapping of ground stress change and deformation all over excavated zones. Field trial data collected in a production mine has demonstrated that monitoring a cluster of rockbolts can provide much more meaningful and reliable information about ground condition when compared with information provided by a single instrumented rockbolt. Therefore, monitoring clusters of rockbolts is recommended as being an effective practice for ground support monitoring.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 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".