Developing site-specific preconditioning blast designs for ultra-deep shaft sinking: A case study from the Onaping Depth project
Bibliographic record
Abstract
This paper presents numerical modeling simulations that replicate the highly successful preconditioning blasting campaign used during the ultra-deep Onaping Depth shaft construction. The model was calibrated based on field observations and microseismic data collected while sinking the shaft. Due to the brittle characteristics of the rock mass and the high stresses at the depths of construction, strainbursting was a pervasive high-risk problem that frequently occurred on the bench of the shaft as the muck was removed and the shaft bench exposed. The development of a preconditioning blasting strategy became a critical control for mitigating strainbursts and protecting operators working in the shaft. It was also found that one type of preconditioning blast design did not suit all rock masses, and the design had to be modified for the conditions encountered. There are no guidelines for preconditioning blast design for shaft sinking operations. This leads to trial-and-error development of blast designs, which may not provide any benefit if the blast design is not suited to the local conditions. The goal of this study is to use numerical modeling, supported by field observations, to provide substantiated guidelines for preconditioning blast design based on field conditions encountered. The strategies presented are beneficial for managing rockburst risks in deep shaft sinking operations in the future and safeguarding operators while constructing these excavations. • Preconditioning blasting reduced frequent strainbursts in brittle rock under high-stress shaft sinking conditions. • Preconditioning blast designs need to be adapted to different rock masses due to variable ground conditions encountered. • Numerical modeling replicated effective preconditioning blasts used in ultra-deep Onaping Depth shaft construction. • Numerical modeling and field observation data helped create practical guidelines for safer preconditioning blast designs for deep shafts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".