Use of Accelerated Corrosion Techniques to Aid in the Selection of Ground Support in Underground Mines
Bibliographic record
Abstract
The stability of underground mining excavations is maintained by the use of ground support, including rockbolts. The engineering objective is to select a rockbolt that can accommodate the anticipated loads and deformation for the design life of an excavation. In this context, rockbolt configuration and material properties dictate its performance under different loading conditions. In corrosive mining environments, the long-term performance of rockbolts can be a concern. The majority of rockbolts are made using low-alloy carbon steel, and exposure to a corrosive environment may result in material degradation and loss of capacity that may lead to failure of the rockbolt. This may compromise the integrity of an excavation and result in a fall of ground that would affect the safety of personnel and equipment. The selection of specific rockbolt types that may be less susceptible to corrosion is typically based on guidelines derived from experience gained over long term exposure at different mine sites. This option is not available when a mining operation wants to explore the use of new types of rockbolts where there is no field data on their susceptibility to corrosion. This paper presents the results of accelerated corrosion investigations to determine the susceptibility to corrosion of new rockbolt types intended to be used in corrosive underground environments. These investigations are part of ongoing research at the University of Toronto aiming to establish the performance of new rockbolt types in deep and high stress mines. The accelerated corrosion investigations have allowed a comparison between different rockbolts. This information provides valuable data in both anticipating the long term performance of rockbolts as well as prioritizing the selection of rockbolts or long term field trials.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".