Intense rockburst impacts in deep underground construction and their prevention
Bibliographic record
Abstract
In the last few decades, in both mining and civil engineering projects, deeper excavations have been carried out than in the past. With this increase in depth, rocks may overstress and rock failures can occur during excavation. When competent rock strata are encountered under high stress conditions, these failures can vary from superficial spalling to explosive rockburst. Intense rockbursts may cause fatal injuries to workers and significant loss of equipment and time. The occurrence of rockbursts is always difficult to predict and special steps and measures must be taken to control them. First, burst-prone zones must be predicted by an early exhaustive geological study and by the assessment of in situ stress level and orientation. Second, basic design parameters, e.g., shape, size, and excavation method, should be modified and adapted to the expected conditions to minimize rockburst risk. Third, in situ pre-conditioning methods, e.g., destress blasting, can be applied to decrease the capacity of the rock mass to store energy. Finally, special rock support and reinforcement systems, i.e., yielding systems, must be installed after excavation to ensure total stability of the opening. This paper reviews the geological and geomechanical factors that provoke and influence rockbursts in overstressed rock masses and the engineering measures taken to control them.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".