Model for the prediction of residual shear strength of unsaturated soils
Bibliographic record
Abstract
The residual shear strength (RSS) of unsaturated soils is a crucial parameter for the reliable analysis and design of geo-structures constructed with or within unsaturated soils undergoing large shear deformation. For investigating the RSS of unsaturated soils, two sets of data are specifically generated on the poorly graded sand with silt and Indian Head till using suction-controlled ring shear tests and three more sets (i.e., silty sand (SM), silty clayey sand, and fat clay) are gathered from the literature. A model is proposed extending two approaches for predicting the RSS for both coarse- and fine-grained unsaturated soils. In this model, the suction contribution was calculated considering the loss of degree of saturation due to shearing, which was described as a nonlinear function of degree of saturation. The capability of the proposed model is validated with the five sets of data using two different approaches. The best-fitting approach that is based on three fitting parameters provides good predictions. The approximate approach performs well for four studied soils, except for SM soil; this approach is simple for use in engineering practice because no fitting parameters are required. The proposed model is valid for the suction range where degree of saturation is higher than the residual degree of saturation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".