Experimental and numerical analysis of desiccation of a mining waste
Bibliographic record
Abstract
The paper presents an analysis of the desiccation process in mining materials, based on physical laws. Due to the process complexity, most of the previous approaches used have an empirical basis. In this case, however, a formulation using coupled hydromechanical equations has been considered. To show the capabilities of the theoretical framework, several laboratory tests were performed, and afterwards a numerical simulation of the measured variables was attempted. The material tested was a metallurgical waste in tailings form and it was exposed to atmospheric desiccation. Further cracking will eventually change its properties, and this may have an environmental impact. Some of the experiments were devoted to material characterization, including the water retention curve, the hydraulic conductivity, and the tensile strength. In addition, laboratory drying tests open to the atmosphere or in hermetically closed containers were also performed. The numerical analyses carried out attempted to simulate some of these tests. One of the main outcomes of the analyses was the prediction of the time and the location of crack initiation. Finally, it should be pointed out that good agreement between the experiments and numerical simulations indicates that the formulation is taking into account the fundamental mechanisms involved in the desiccation process.Key words: desiccation, drying cracks, unsaturated soils, waste material, coupled phenomena, modelling.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".