Numerical modelling of destress blasting – A state-of-the-art review
Bibliographic record
Abstract
As a proactive mine safety measure against the occurrence of rockburst, destress blasting has been applied to numerous mining conditions to precondition highly stressed rock mass to mitigate the risk of rockburst occurrence in deep mines as well as in deep underground constructions. However, the application of destress blasting mostly depends on engineering experience, while its mechanism and efficiency have not been well understood. Rapid advances in computer technology have made numerical simulation an economical and effective method to study the rock blasting effect. Enormous research efforts have been made to numerically investigate the blasting fracture mechanism, optimize blasting design, and assess the efficiency of destress blasting. This review focuses on the state-of-the-art progress in numerical modelling associated with destress blasting over the last two decades. Some commonly used modelling approaches for destressing blasting are compared and reviewed. Currently, two different ways of modelling based on static and dynamic modes are typically used to study the effect of blasting. In the static method, destress blasting is simulated by modifying the rock mass’s stiffness and strength properties to obtain the post-blast stress state in the destressed zone. The dynamic modelling technique focuses on the dynamic fracture process of coals and rock masses, during which the predetermination of the damage induced by blasting is not necessary. Moreover, the extent of damage zones around the blast hole can be precisely estimated in the dynamic modelling method by considering time-varying blast pressure and strain rate dependency on the strength of rock mass but at the cost of increased computation and complexity. Besides, different destress blasting modelling methods, generally classified into continuum-based, discrete-based, and coupled methods, are compared and reviewed. The fracture mechanism of blasting in the rock mass is revealed, and the destressing efficiency of the existing destress blasting design is assessed and compared with classical results. The factors that may affect the efficiency of destress blasting are summarized. Finally, the difficulties and challenges associated with the numerical modelling of destress blasting are highlighted briefly.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".