Evaluation of structural performance of thinasphalt pavement reinforced with wicking geotextiles over expansive soils
Bibliographic record
Abstract
This research provides a comprehensive evaluation of the effects of a wicking geotextile capable of multiple functions, including separation and reinforcement and gravity and capillary suction-induced drainage for subsurface layers in flexible pavements built over expansive soils. Two test sections were designed and constructed near central Texas, which were prone to distress from cyclic moisture-induced strains related to expansive soils, during the Fall of 2018. The base layers in the first and second sections were constructed with reclaimed asphalt pavement and crushed stone aggregate, respectively, and wicking geotextiles were installed between the base and subgrade layers. The adjacent lane to the test sections was selected as the control section. Falling weight deflectometer (FWD) testing was conducted to evaluate the in-situ moduli of the pavement layers. Multiple performance indicators were selected to compare the performance of reinforced and control sections. A performance prediction software program was used to investigate the performance of the sections according to the mechanistic-empirical design and analysis approach. The results showed the rapid removal of moisture has a significant impact on controlling the permanent deformation of the pavement layer. FWD results revealed that the reinforced layer helped to improve the base and subgrade moduli values. The performance prediction results showed the wicking geotextile has the potential to be used for reinforcing the pavements constructed over expansive soil
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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 | 0.000 |
| 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) | 0.000 | 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 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".