A structural state model for interpreting residual strength transition behavior of land-sliding soils with different clay fractions
Bibliographic record
Abstract
Abstract Residual friction angles of soils are commonly determined by laboratory tests, which are time-consuming and costly. It is more desirable to find practical means of estimating the residual strength of soils in the slip zones. Previous researchers attempted to correlate the soil residual strength with clay fraction or Atterberg limits using empirical equations. A large amount of laboratory data demonstrate that the magnitude of residual strength decreases with the increase in the clay content. However, the physical mechanisms of such correlation are not well interpreted. In addition, the decreasing trend has never been modeled with a unique empirical or semi-empirical equation because of the variety of influencing factors such as the clay mineralogy and the applied normal stress. In this study, a new approach is proposed to estimate the residual friction angles of land-sliding soils. The residual friction angle of soils is derived as a weighted average result of the friction angles of nonclay minerals and clay matrix. A structural state coefficient is used as the weight function, and the plasticity index is used to consider the difference in clay mineralogy. The percolation theory is used to physically explain and illustrate the structural state transitional behavior of soils with different clay fractions. The results demonstrate that the semi-empirical approach can be used for predicting residual friction angles of a wide range of soils that differ in geology, soil type, mineralogical properties, and shear strength. The effects of applied normal stress, pore fluid salinity, and particle size on the estimations are also discussed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".