Experimental and modeling study of shear strength of unsaturated fine-grained soils with bi-modal water-retention characteristics
Bibliographic record
Abstract
The mechanical behavior and strength characteristics of unsaturated fine-grained soils with dual-porosity are of crucial importance in geotechnical designs. Nanyang fine-grained soils have been selected as typical dual-porosity structure soils to perform experimental tests under a wide range of suction and different initial densities while studying its stress-strain-strength properties constitute the main scope of this study. Axial translation and vapor equilibrium techniques are jointly employed to apply a wide suction range. Our data suggest that soil behavior transits from strain-hardening with shear-induced contraction to strain-softening with shear-induced dilation as suction and density increase. By exploiting a bi-modal soil–water retention curve (SWRC) explicitly separating capillarity and adsorption mechanisms, the shear strength is allowed to be analyzed in the capillary suction stress-shear stress space. The strength envelop exhibits bi-linear characteristics. Building upon these findings, we propose a bi-linear shear strength criterion specifically for dual-porosity fine-grained soils. We utilize the obtained test data to evaluate existing strength criteria based on effective stress and dual stress variables that consider the bi-modal SWRC characteristics. The comparison indicates that the proposed bi-linear shear strength criterion can more reasonably represent the variation of shear strength under a wide range of suction for unsaturated dual-porosity fine-grained soils.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".