Probabilistic and Deterministic Approach to Define the Vertical Stress in Inclined Mine Stopes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Due to its environmental utility and capacity to increase the stability of mine excavations, underground mine backfilling is a proven technique and has become a common practice in the mining industry. The backfilling of underground stopes is a technique that has been used for decades in Canada and worldwide. In the last few years, several contributions reported the potential of analyzing backfill stress in mine stopes through analytical equations, numerical modeling, and in situ measurements. Using a probabilistic stress analysis approach, this study proposes an analytical solution to determine the stress of a backfill on the pillar of an inclined mine stope to rectify its mathematical and physical limitations. The backfill parameters utilized in these analyses were obtained from laboratory investigation data conducted at the Canadian Niobec mine in Quebec. Monte Carlo simulations were employed to generate a comprehensive database encompassing both analytical and numerical results. The obtained results exhibited comparability across multiple cases, revealing that the standard deviation of vertical stress decreases with increasing stope inclination and increases with the augmentation of stope height. Additionally, simulations were conducted to assess the feasibility of the proposed solution, demonstrating an accurate approximation of the actual stresses applied to various stope geometries in the Niobec mine. Furthermore, conducting a simulation specific to the data and geometry of the Niobec mine construction sites allows for the quantification of the stresses present in this scenario.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it