Understanding parameters impacting the mechanism leading to spalling around underground excavations in massive rocks under high stress
Bibliographic record
Abstract
Abstract Underground excavation is required for mining engineering and nuclear waste repository. As the excavation progresses, the stress increases, and brittle failures frequently exist. Brittle failures are arranged into two types: spalling and rock bursting. Spalling in the shape of a V-notch may severely damage the machine and workers involved in its development. This potential hazard can be mitigated by fully representing all parameters that cause spalling in the model. Thus, optimized tunnel design can be achieved. As a result, the company can strike a balance between profit and safety. Several possible results can be proposed by conducting a series of sensitivity studies on a credible model based on a real-world case study of spalling in the Underground Research Laboratory (URL) in Canada. It includes predicting the Depth of Failure (DOF) of spalling by changing two main parameters, particularly strength (tensile and crack initiation) and stress (σ1). The result reveals that increasing tensile strength over UCS decreases DOF, increasing percentages of crack initiation (CI) over UCS decreases DOF, and increasing σmax over UCS increases DOF. Moreover, systematic errors in the generated model can lead to inaccurate DOF estimation. This problem must be approached with engineering judgment in order for the solution to correspond to the actual spalling phenomenon.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".