A dual-porosity model for the study of chemical effects on the swelling behaviour of MX-80 bentonite
Bibliographic record
Abstract
Significant chemical influence on the swelling potential of MX-80 bentonite was observed during swelling tests where specimens were hydrated with highly concentrated brine. The maximum swelling pressure for specimens hydrated with brine was about 30% of the maximum swelling pressure for the same specimens hydrated with de-ionized water. The maximum swelling pressure was attained within tens of hours of brine infiltration and further decreased by half within a year. A fully coupled hydro–mechanical–chemical (HMC) dual-porosity model is proposed in this paper to interpret the swelling behaviour of MX-80 when infiltrated with brine. The dependence of hydraulic and mechanical properties on such factors as porosity, salinity and water content was investigated. A nonlinear elastic constitutive model was proposed to correlate the swelling pressure with the variation in the microporosity. The chemical effects on the mechanical behaviour were coupled at the micropore level. A number of relationships have been developed for MX-80, i.e. micropore permeability as a function of void ratio, water retention characteristics of micropores and macropores, micropore dependence on water content and the diffusion coefficients of the two types of pore structure. The proposed model was successful in reproducing both quantitatively and qualitatively the experimental results from two sets of infiltration experiments on compacted MX-80 bentonite.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".