Predicting the Resilient Moduli of Unbound Base Material Using Field and Laboratory Light-Weight Deflectometer Tests
Bibliographic record
Abstract
It is well known that unbound granular materials (UGMs) play a fundamental role as the base layer in flexible pavements.The dynamic properties, namely the resilient moduli, are quite important to characterize the unbound materials for the Mechanistic-Empirical Pavement Design Guide (MEPDG).The repeated load triaxial (RLT) test used to measure the resilient moduli is a rather expensive and time-consuming test.In this study, extensive research has been carried out to establish the relationship between the resilient moduli (Mr) measured by RLT tests and the dynamic deformation moduli (Evd) measured by a simpler technique, namely the Light Weight Deflectometer (LWD) tests, for a local type of commonly available unbound material in Sweden.To measure the dynamic material parameters using the LWD and RLT tests under similar test conditions, a series of in situ and laboratory LWD and RLT tests were carried out at different moisture contents and stress levels.The overall test results were analyzed, and a strong regression correlation (R=0.95) was found between the dynamic parameters measured from the RLT and LWD tests for the tested material.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.001 |
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".