On equivalent granular void ratio and steady state behaviour of loose sand with fines
Bibliographic record
Abstract
Void ratio has traditionally been used as a state variable for predicting the liquefaction behaviour of soils under the critical state soil mechanics framework. Recent publications show that void ratio may not be a good state variable for characterizing sand with fines. An alternative state variable referred to as the equivalent granular void ratio has been proposed to resolve this problem. To calculate this alternative state variable, a b parameter is needed. This b parameter represents the fraction of fines that actively participate in the force structure of the solid skeleton. However, predicting the value of b is problematic. Most, if not all, of the b values reported were determined by case-specific back-analysis, that is, the b value was selected so that a single correlation between equivalent granular void ratio and the measured steady state strength (or cyclic resistance) could be achieved. This paper examines the factors affecting the b value based on published work on binary packing. This leads to a simple semi-empirical equation for predicting the value of b based on fines size and fines content. Published data appear to be in support of the proposed equation. A series of experiments were conducted on a specially designed sand–fines type to provide additional validation of the proposed equation and to reinforce the use of equivalent granular void ratio in a more generalized context.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".