Predicting the Variation of Resilient Modulus with Respect to Suction Using the Soil-Water Characteristic Curve as a Tool
Bibliographic record
Abstract
The resilient modulus, MR, which is a key parameter in the design of pavements, is significantly influenced by seasonal moisture variations and resultant suction fluctuations. Several relationships or models are available in the literature for predicting or estimating the MR taking account the influence of moisture content or suction. In this paper, two models from the literature are used for providing comparisons between the measured and predicted MR values of a fine-grained soil compacted at three different initial water contents. The strengths and limitations of the two selected models are discussed. In addition, a semi-empirical model is proposed for predicting the variation of the MR with respect to suction using the soil-water characteristic curve (SWCC) as a tool. The proposed model is promising and can be used in the reliable prediction of the variation of the MR with respect to suction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".