Cyclic Loading Type Effect on Performance of Base Aggregates with 12% Fines under High Stress Ratio
Bibliographic record
Abstract
Highway pavement consists of various layers that are capable of transmitting load from the moving vehicles to the subgrade. Among these layers, base and subbase act as an intermediate medium for transmitting the load from the surface course to the subgrade. A series of repeated cyclic load triaxial tests were conducted on a well-graded aggregate typically used in Illinois. The tests are used to determine cyclic deformation and resiliency characteristics of the aggregate under higher stress ratio (SR) 7, with dust ratio (DR) 0.4 and 0.6, and Plasticity Index (PI) 5% and 9%. The deformation and the resiliency of the specimen are based on dynamic loadings: Haversine and Sinusoidal. The impact of DR was studied for the samples with varying PI and loading conditions and found that the geotechnical precision is required during the construction of the unbound granular materials (UGM). For instance, under sinusoidal loading conditions, the permanent deformation (PD) for low plasticity aggregates with DR 0.4 exceeds the sample with DR 0.6 significantly by 200%−215%. Moreover, with an increase of PI from 5% to 9%, the interaction becomes complex. Therefore, the study investigates the effect of geotechnical parameters and assesses the criteria for determining quality aggregates used in base and sub-base layers of the flexible pavement system.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".