Estimating compression behavior of reconstituted clays with different initial water contents using two model parameters
Bibliographic record
Abstract
The virgin compression line (VCL) of reconstituted clay relies on the initial water content, and various equations are proposed to describe this behavior. However, they might be case-specific and limited in capturing the full range of nonlinear VCL. Therefore, a compression model based on equivalent concept is proposed to estimate the VCLs of reconstituted clays with various initial water contents. This is done by incorporating a new equivalent concept into the natural compression law. The effect of initial water content is explicitly captured by an equivalent specific volume, and the progressive yielding during compression process is quantified by a novel state variable, which is formulated as a function of current specific volume. Only two compression parameters are required for the proposed model, and they can be readily calibrated by one conventional compression test. Verification reveals a satisfactory performance of our model in capturing the nonlinear compression behavior of reconstituted clays with a broad spectrum of initial water contents, liquid limits and stress levels. Besides, remolded yield stress of reconstituted clays can be well estimated based on the maximum curvature of the predicted VCL. The proposed equation could provide a rational reference for engineering design of preloading projects on soft foundation.
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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) | 0.000 | 0.000 |
| 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) | 0.000 | 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".