Subgrade resilient modulus prediction using light-weight deflectometer data
Bibliographic record
Abstract
Resilient modulus has been used for decades as an important parameter in pavement structure design. Resilient modulus, like other elasticity moduli, increases with increasing confining stress and decreases with increasing deviatoric stress. Several constitutive models have been proposed in the literature to calculate resilient modulus as a function of stress state. The most recent model, recommended by the Mechanistic–empirical pavement design guide (MEPDG) and used in this paper, calculates resilient modulus as a function of bulk stress, octahedral shear stress, and three fitting coefficients: k 1 , k 2 , and k 3 . Work in this paper presents a novel approach for predicting resilient modulus of subgrade soils at various stress levels based on light-weight deflectometer (LWD) data. The proposed model predicts the MEPDG resilient modulus model coefficients (k 1 , k 2 , and k 3 ) directly from the ratio of applied stress to surface deflection measured during LWD testing. The proposed model eliminates uncertainties associated with needed input parameters for surface modulus (E LWD ) calculation, such as the selection of an appropriate value of Poisson’s ratio for the soil layer and shape factor. The proposed model was validated with independent data from other studies reported in the literature.
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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 | 0.001 |
| Open science | 0.001 | 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".