ANN-based optimization of single-stage triaxial tests for predicting permanent deformation in pavement granular layers
Bibliographic record
Abstract
Inadequate characterization of the behavior of granular materials under accumulated plastic deformation in flexible pavements can lead to excessive rutting, severely affecting the structural and functional performance of highways. Permanent deformation (PD) is normally measured using repeated triaxial load tests, as required by Brazilian standards, which specify single-stage tests with 150,000 cycles in nine stress states per soil. Although effective, especially for tropical soils that exhibit rapid initial PD accumulation followed by rutting stabilization, the procedure requires substantial time, personnel, and laboratory resources. This study proposes reducing the number of cycles required for this characterization through prediction models. Seven soil samples were tested at nine pairs of stresses: five samples for model development and two for validation. From this, Artificial Neural Networks were trained using the data, removing the first 1,000 cycles to avoid the effects of rapid initial growth. The models generated PD predictions similar to the results obtained in tests of 150,000 load cycles using only 30,000 cycles for these predictions, with errors below 0.09 mm under severe traffic. The results confirm that the proposed approach can reduce the time required for PD characterization by approximately 50%, while maintaining reliable performance estimates.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".