Alternative method of determining resilient modulus of subgrade soils using a static triaxial test
Bibliographic record
Abstract
The resilient moduli (MR) of subgrade and subbase soils are very important properties in the analysis and design of a flexible pavement system. However, difficulties and complexities in performing cyclic MRtesting and the high cost of the testing system have prevented the cyclic MRtest from becoming a routine test. Therefore, the development of an alternative simple and reliable MRtesting technique is essential to the application of the mechanistic design of a flexible pavement system. In this study, an alternative MRtesting technique for subgrade soils was developed using a static triaxial compression (TX) test. For the development of the alternative testing method, the effects of strain amplitude, loading frequency, mean effective stress, and number of loading cycles on the resilient modulus of subgrade soils were fully investigated. Cyclic MR, static TX, and resonant column torsional shear tests were performed to evaluate the deformational characteristics. Synthetic specimens of known stiffnesses ranging from those of soft subgrade soils to those of subbase materials were developed, and all of the testing systems used in this study were calibrated. The alternative MRtesting procedures were proposed considering deformational characteristics of subgrade soils. The reliability of the proposed test method was verified by comparing the moduli determined by the proposed alternative MRtesting method with those determined by the standard MRtests.Key words: resilient modulus (MR), alternative MRtest, subgrade soils, static triaxial test, deformational characteristics.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".