Prediction of Compression and Permeability Characteristics of Mine Tailings Using Natural Computation and Large-Strain Consolidation Framework
Bibliographic record
Abstract
Prediction of consolidation behavior of soft soil deposits, such as mine tailings and hydraulic fills, is an essential and routine exercise in mining and dredging industries using large-strain consolidation theory. The values of compressibility and hydraulic conductivity deduced from settlement vs. time response are influenced by the selected form of e – log σ' and e – k relationship. In this paper, we propose a method of back-analysis to deduce compressibility and hydraulic conductivity of soft soils from its settlement vs. time response. The curve-fitting parameters for the nonlinear relations between e – log σ' and e – k are inferred using the settlement response and the initial conditions of the soil for the cases of self-weight consolidation of oil sands mature fine tailings (MFT). A piecewise-linear model of large-strain consolidation is used. The inverse analysis is carried out using natural computation algorithms. It is demonstrated that accurate deduction of compressibility and hydraulic conductivity is possible using natural computation algorithms and that these algorithms are much more stable ad accurately predicts the parameters.
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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 | 0.000 | 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".