Reliability analysis and design of backfill in a cut-and-fill mining method
Bibliographic record
Abstract
In underground mining, hydraulic backfill materials, such as waste tailings, river sand, and cement, are often used to fill underground mined stopes. In cut-and-fill mining methods with blasthole stoping and delayed backfill, after extraction of adjacent pillars that contain economic minerals, the backfill is often subject to exposure of free standing on at least one side. A key concern for mining engineers is the stability of this immediately-bordered backfill body, because the backfill stability has a significant effect on the dilution/loss rate and the safety of mining operations. It is found that backfill stability is one of the mining subjects most dominated by uncertainty. Rock and backfill properties, environmental conditions, and analytical models are such factors contributing to uncertainty. Conventional methods simplified the problem by considering the uncertain parameters to be deterministic, and accounted for the uncertainties through the use of empirical factors of safety. This paper aims to conduct stability analysis of backfill in underground mining using a probabilistic reliability method, which is an extension of conventional deterministic methods. The parameters of backfill properties are modelled as random variables. In order to determine the failure probability of the backfill in a cut-and-fill mining method of an underground mine in China, a three-dimensional wedge model is set up for the backfill and a corresponding limit state function is established to characterize the backfill stability for the purpose of reliability analysis. The influences of the mean values, coefficients of variation, probability distribution types, and correlation between random variables are carefully investigated through sensitivity analysis. The results obtained give insights into the mechanism of backfill stability and could provide some useful clues on how to choose the right backfill materials.
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.
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 | 0.000 | 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 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".