Alternative method of determining resilient modulus of subbase soils using a static triaxial test
Bibliographic record
Abstract
The cyclic test for the determination of resilient modulus (MR) is often too complex and time-consuming to be applicable on a production basis. Therefore, the development of a simple and reliable alternative MRtesting technique is essential for the application in the mechanistic design of a flexible pavement system. Seven disturbed subbase soils were collected from the actual pavement projects for testing. To evaluate the effect of particle size on MR, standard MRtests with various maximum particle sizes and specimen diameters were performed using three subbase soils. The resilient moduli determined from various specimen sizes with the same particle-size distribution were almost identical. However, the value of the slope parameter k2in the bulk stress model was constant, but the value of k1increased with a decrease in maximum particle size. The effects of mean effective stress, loading frequency, and number of loading cycles on modulus were evaluated from torsional shear (TS), triaxial (TX), and MRtests. The alternative MRtesting procedure using the static TX test was proposed considering deformational characteristics of subbase soils. The predicted MRvalues from the proposed method matched well with those determined by the standard MRtest, showing the capability of the proposed method for determining MR.Key words: resilient modulus (MR), alternative MRtest, subbase soils, triaxial compression test, deformational characteristics, particle size.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".