Model for predicting resilient modulus of unsaturated subgrade soil using soil-water characteristic curve
Bibliographic record
Abstract
Soil suction (ψ) is one of the key factors that influence the resilient modulus (M R ) of pavement subgrade soils. There are several models available in the literature for predicting the M R –ψ correlations. However, the various model parameters required in the existing models are generally determined by performing regression analysis on extensive experimental data of the M R –ψ relationships, which are cumbersome, expensive, and time-consuming to obtain. In this paper, a model is proposed to predict the variation of the M R with respect to the ψ for compacted fine-grained subgrade soils. The information of (i) the M R values at optimum moisture content condition (M ROPT ) and saturation condition (M RSAT ), which are typically determined for use in pavement design practice; (ii) the ψ values at optimum moisture content condition (ψ OPT ); and (iii) the soil-water characteristic curve (SWCC) is required for using this model. The proposed model is validated by providing comparisons between the measured and predicted M R –ψ relationships for 11 different compacted fine-grained subgrade soils that were tested following various protocols (a total of 16 sets of data, including 210 testing results). The proposed model was found to be suitable for predicting the variation of the M R with respect to the ψ for all the subgrade soils using a single-valued model parameter ξ, which was found to be equal to 2.0. The proposed model is promising for use in practice, as it only requires conventional soil properties and alleviates the need for experimental determination of the M R –ψ relationships.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".