Comparison of Geosynthetic Reinforcement Performance on Clay Soil: An Experimental Study of Variations in Placement Distance and Material Type
Bibliographic record
Abstract
This study evaluates the performance of various geosynthetic materials-woven geotextile, non-woven geotextile, and geogrid-under maximum load (Pmax), geosynthetic strain (τ), and maximum displacement on clay soil, with variations in placement depth (5 cm, 10 cm, and a combination of 5 cm & 10 cm).Clay soil samples were compacted, and geosynthetics were placed at specified depths within the soil.Load tests were performed to measure Pmax, while geosynthetic strain (τ) and maximum displacement (Max Displacement) were recorded to assess the geosynthetic performance.The results showed that geosynthetics significantly enhance soil performance.The woven geotextile exhibited the highest Pmax (240.695kg at a combined depth of 5 cm & 10 cm), followed by the non-woven geotextile (230.230kg at the same depth), and the geogrid (197.340kg at the same depth).For geosynthetic strain (τ), the highest values were recorded in the woven geotextile (1,605.553N/cm² at a combined depth of 5 cm & 10 cm), followed by the non-woven geotextile (1,513.158N/cm² ) and the geogrid (1,471.710N/cm² ).Regarding maximum displacement, the geogrid showed the most significant displacement (3.0 cm at a depth of 5 cm).The woven geotextile had the least minor displacement (1.4 cm at a combined depth of 5 cm & 10 cm), followed by the nonwoven geotextile (2.7 cm at 5 cm).The study concludes that the woven geotextile performs best regarding Pmax, geosynthetic strain, and minimal displacement.At the same time, the geogrid shows the most significant displacement and the least favorable performance.These findings suggest that the two materials are effective for different applications.Further research should focus on the long-term performance and environmental impacts of geosynthetics.
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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) | 0.001 | 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 | 0.000 | 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".