Resilient Modulus of Subgrade Soils A-1-b, A-3, and A-7-6 Using LTPP Data: Prediction Models with Experimental Verification
Bibliographic record
Abstract
Accurate evaluation of subgrade resilient modulus (MR) is important for effective and economical design of flexible pavements. Resilient modulus of subgrade soil is the elastic modulus based on the recoverable strain under repeated loads and depends on several factors like soil properties, soil type, and stress states. This paper presents prediction equations to estimate MR from a set of soil physical properties for 3 AASHTO subgrade soil types, namely A-1-b, A-3, and A-7-6. The prediction models were developed using the multiple linear regression analysis of the data extracted from Long Term Pavement Performance Information Management System (LTPP IMS) Database for 82 test specimens, approximately 1230 MR values, from 16 states in New England and nearby regions in the U.S. and 1 province in Canada. Generalized constitutive model consisting bulk stress and octahedral shear stress was used to predict MR of subgrade soils by developing regression equations for the k-coefficients that relate these coefficients to various soil properties, e.g. particle size, moisture contents, densities, and Atterberg limits. The R2 values obtained for the prediction equations for k coefficients relating to the soil properties range from 0.45 to 0.79. To verify the prediction models independently, laboratory MR tests were conducted on a few New England subgrade soils. It was observed that the predicted MR values compared well with the laboratory measured values for A-1-b soil samples. However, because of some soil property values being outside the range used in the development of the prediction models, the comparison was poor for A-3 soil samples, and for A-7-6 soil, MR values could not be predicted due to negative values for k2 coefficient.
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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 | 0.000 | 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 | 0.000 | 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".