Study of the Permanent Deformation of Soil Used in Flexible Pavement Design
Bibliographic record
Abstract
The decreasing supply of soils with geotechnical parameters suitable for pavement designs is a visible problem in our environment. In order to establish more efficient designs and adequate construction criteria, it is essential to understand the performance of materials. This is a study of the permanent deformation (PD) of soil used in pavement layers, obtaining prediction models through the technique of artificial neural networks, in addition to the design of pavement structures using mechanistic‐empirical and empirical methods. The multistage repeated load triaxial (RLT) test, as well as numerical analyses of stresses and displacements using the CAP3D program, was used. The results showed that both the test procedure and the prediction models performed satisfactorily in obtaining PD behavior. Moreover, designs using the methods adopted resulted in distinct structures, that is, thickness different from the granular pavement layers. It was concluded that the model and test procedure exhibit significant potential for characterizing and modeling the PD of granular materials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".