A Unified Semiempirical Model for Small-Strain Shear Modulus of Fine-Grained Soils under Hydromechanical Loading
Bibliographic record
Abstract
In recent years, several empirical and semiempirical relationships have been proposed to predict the small-strain shear modulus of unsaturated fine-grained soils along different hydraulic and mechanical loadings paths. However, a major deficiency of these relationships is the absence of a coupled linkage between hydraulic and mechanical processes that occur in unsaturated conditions. Specifically, the void ratio and effective stress are considered uncoupled, and changes in soil volume are rarely considered when implementing soil water retention curves in these equations. This study aims to address these deficiencies by discussing the coupled effect of hydraulic and mechanical processes in unsaturated soils and presenting a semiempirical model to predict the small-strain shear modulus, Gmax, of unsaturated low plasticity soils subjected to volume and effective stress changes along different mechanical and hydraulic stress paths. Predictions from this model and three other recently proposed models in the literature are compared with experimental results obtained from a series of suction-controlled bender element tests on silty soil specimens to validate the proposed model. The comparison reveals that the model proposed in this study provides more consistent predictions of the small-strain shear modulus during hydraulic hysteresis, as well as different paths of loading and unloading.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".