Prediction of resilient modulus of frozen unbound road materials using soil-freezing characteristic curve
Bibliographic record
Abstract
The resilient modulus is a key parameter required in the mechanistic design of pavements. Experimental determination of the resilient modulus requires elaborate equipment for testing and requires trained personnel; for this reason, it is expensive. There are several models for predicting the resilient modulus for unbound road materials that take into account the influence of wetting and drying conditions. However, well-established models are not available for the prediction of the resilient modulus of these materials in a frozen state. In this paper, a semi-empirical model, which uses a soil-freezing characteristic curve as a tool, is proposed for predicting the variation of the resilient modulus with subzero temperature and the associated cryogenic suction for frozen soils. Experimental data on seven different pavement unbound materials were used to validate the proposed model. It is shown that the model can reasonably predict the resilient modulus of the investigated soils that are in a frozen state. More investigations on different types of soils would be useful to better understand the strengths and limitations of the proposed model.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".