Model tests on wicking geosynthetic composite reinforced bases over weak subgrade
Bibliographic record
Abstract
Road performance is significantly enhanced by incorporating geosynthetics through their reinforcement and drainage functions. This study introduces a novel geosynthetic that integrates these functions. It is made of biaxial polypropylene geogrids heat-bonded to wicking nonwoven geotextiles (WNWGs). WNWGs are chemically treated to be hydrophilic and thus possess rapid wetting and wicking properties while preserving the large lateral drainage functionality of conventional nonwoven geotextiles. To assess the combined reinforcement and drainage performance of this material, a series of model tests including rainfall simulation and plate loading tests were performed on the WNWG-geogrid composite reinforced bases over weak subgrade using a customized model test apparatus. The results confirmed that the inclusion of wicking geosynthetic composite significantly enhanced drainage, stiffness, and bearing capacity of road bases compared to the conventional nonwoven geotextile-geogrid reinforcement and the unreinforced condition. The modulus improvement factor (MIF) for this wicking composite was 2.74 as compared to 1.46 for the conventional nonwoven geotextile-geogrid reinforcement. The findings from this study demonstrate the promising performance of this new composite and provide a valuable reference for full-scale tests and applications on roads. • The wicking nonwoven geotextiles possessed significant wetting and wicking capability, rapidly reducing water in the base courses immediately after heavy precipitation. • Both rainfall simulation and plate loading tests confirmed the pronounced wicking and reinforcement functions of the proposed composite.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".