An Ultrasonic Rock Bolt Sensing Technology (I): Methodology and Laboratory Studies
Bibliographic record
Abstract
Abstract Rock bolts play a critical role in ground support in mining, tunneling and construction. Being able to obtain timely information on rock bolt load condition and deformation could help immensely in safeguarding workers’ safety, optimization of ground support system, and ensuring stability and longevity of underground structures. The objective of this work was to develop a practical ultrasonic rock bolt sensing technology for monitoring axial load and deformation of a full-bodied rock bolt in both elastic and plastic deformation regimes. To this end, empirical mathematical models involving simultaneous use of times of flight of longitudinal and shear ultrasonic waves propagating along the axial direction of the rock bolt were developed, sensors were designed and fabricated, and laboratory studies were conducted. The results showed that the technology was able to measure load change, provide early detection of yield, and measure both plastic and total elongations of the rock bolt undergoing a pull test. Additionally, sectional load and elongation information could be obtained by applying the technology to rock bolts divided into sections by drilled holes along their shanks, therefore, providing the capability to assess the condition of grouted bolts at different depths within a rock mass. This work has laid the groundwork for the deployment of the technology in the field.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".