Dynamic impact and static testing of self-drilling dynamic bolt types installed in Normet’s urea-silicate injection resin: a new path forward to reducing worker exposure to high-stress ground conditions
Bibliographic record
Abstract
In deep mining operations, large deformation of surrounding rock and rockbursts have become unavoidable concerns worldwide, exposing workers to high-stress hazards. In response to these challenges, Normet has developed high-energy dissipation self-drilling dynamic bolt (SDDB®) varieties installed in urea-silicate injection resin. This research evaluates the static and dynamic performance of Normet’s fully encapsulated SDDB. To enable a comprehensive evaluation, an innovative test method utilising continuous and split tube configurations was developed to assess the installation of fully encapsulated bolts in both intact surrounding rock and jointed rock mass. The tests included pull tests, shear tests, continuous tube drop tests and split tube drop tests. Static tests provided insights into the yield load, maximum load, failure load, and bolt elongation under tensile and shear stress, while the dynamic tests evaluated the performance of the SDDB during impact and characterised the failure patterns of the bolts. The fully encapsulated Leinster SDDB exhibited a maximum load capacity of 349.8 kN with 80 mm elongation in the pull test. In comparison, the Onaping SDDB withstood a shear load of 282.6 kN and generated a displacement of 27 mm. Furthermore, the Nevada and Nordic coupled SDDB demonstrated a total elongation of 196 mm and withstood an impact load of 271 kN in the drop test. These research findings highlight the exceptional mechanical properties of Normet’s SDDB and its efficacy as a dynamic ground support component in high-stress environments.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".