Experimental study on the resilient behavior of unbound granular materials and the evaluation of prediction model
Bibliographic record
Abstract
The resilient modulus ( Mr) serves as a key indicator for evaluating the stiffness of road subgrade and pavement base/subbase layers under long-term traffic loading. Although there have been many laboratory studies on the resilient behavior of road unbound granular materials (UGMs), and many models to predict Mr have been proposed; however, the relative roles of various factors on Mr and its prediction model are not clearly clarified. This study conducted a series of cyclic tests on UGMs based on a large-scale triaxial apparatus, in which various factors including the fines content, moisture content and oversized soil particle, confining pressure and initial deviatoric stress, amplitude, frequency, and waveform of cyclic stress were involved. Test results show that the effects of fines content and oversized soil particle on the resilient modulus are limited. The initial stresses are the primary factors influencing the resilient behavior, and the impact of initial deviatoric stress on Mr is much smaller compared to confining pressure. Both the amplitude and frequency of cyclic stress have significant effects on Mr, while the influence of loading waveform is relatively small. The prediction model of Mr recommended by JTG D50-2017 is employed to fit the test results, and it is found that the loading frequency is the predominant factor influencing the regression coefficients, the initial deviatoric stress, and loading waveform are moderate factors, while the fines content and oversized soil particle are minor factors. A model involving the factor of loading frequency is proposed based on the model recommended by JTG D50-2017, and its accuracy and application upon various factors are evaluated. Based on the test results, some advice is given for the application of UGMs on road subgrade and pavement base/subbase.
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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 | 0.001 | 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 | 0.001 |
| 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".