A SIMPLE EXPERIMENTAL PROCEDURE FOR DETERMINING THE FITTING PARAMETER, κ FOR PREDICTING THE SHEAR STRENGTH OF AN UNSATURATED SOIL
Bibliographic record
Abstract
The shear strength of an unsaturated soil can be predicted with a semi-empirical shear strength function developed at the University of Saskatchewan both for low and as well as large suction ranges. A fitting parameter, κ is necessary in this function to provide comparisons between the predicted and measured shear strength values. A relationship is recently proposed between the fitting parameter, κ and the plasticity index, Ip. This relationship is useful to determine the required fitting parameter κ value and use in the function for predicting the shear strength. The Ip vs κ relationship is proposed using the available experimental results in the literature on statically compacted specimens. The validity of using the κ value from this relationship for different types of natural soils and compacted conditions is not well known. In this paper, a simple experimental procedure is proposed for determining the fitting parameter, κ value. This procedure requires the results of unconfined compressive shear strength along with the matric suction value of the unsaturated soil specimen. Using the value of fitting parameter, κ determined from this simple experimental procedure, comparisons were provided between the predicted and measured shear strength values for clay till specimens that were compacted at three different initial water content conditions representing dry of optimum, optimum, and wet of optimum conditions. There is a reasonably good comparison (i.e., with in a percentage of accuracy of 10 to 15%) between the predicted and measured shear strength values of clay till specimens. RÉSUMÉ
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".