An effective stress-based approach to modeling the chemo-mechanical behavior of saturated active clay
Bibliographic record
Abstract
Pore water chemistry can exert significant controls over the chemo-mechanical behavior of chemically active soils, which has not been characterized by using the traditional Terzaghi’s effective stress to a satisfactory extent. In this paper, based on the concept of intergranular stress, a generalized effective stress for saturated active soils is reconceptualized and incorporated into the framework of the modified Cam–Clay (MCC) model to describe the mechanical response of soils upon complex chemical and mechanical loadings. The proposed model is capable of capturing very well the diverse features of the chemo-behavior of saturated active soils, while inheriting all the advantages of the MCC model, by introducing only one additional parameter. Within this context, a simple equation is derived to predict the development of swelling pressure with the variation of pore water chemistry. It is shown that the proposed swelling-pressure equation predicts very well the development of swelling pressure for expansive soils with a dilute pore solution, though discrepancy appears at high concentration. Because the double layer of clay particles is severely suppressed at high salt concentration, the microfabric of soil can be significantly changed, pointing to the importance of taking into account the effect of soil fabric in the constitutive modeling of active soils with high pore water concentration.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".