Study on the Impact of Sand-Clay Bond in Geo-grid and Geo-Textile on Bearing Capacity
Bibliographic record
Abstract
This paper aims to determine the impact of sand-clay bond in geo-grid and geo-textile on bearing capacity. In doing so, we examined clay-geo-synthetics, sand-geo-synthetics and clay-sand-geo-synthetics samples using direct shear tests. The friction between clay and reinforcement was provided by encapsulated-sand system. This method is used to transfer the tensile force mobilized in geo-synthetics from sand to clay and improve the strength parameters of clay. This study indicated that the provision of a thin layer of sand at both sides of the reinforcement significantly improved the shear strength of clay soil. Bond coefficient computations indicated that the shear strength of clay-geo-synthetics samples was higher than non-reinforced clay. The increased strength was due to the impact of open meshes of geo-synthetics which provided some degree of resistance bearing. To determine the share of resistance bearing provided by geo-synthetic transverse members in the entire direct shear strength, we conducted a series of tests on geo-synthetics-reinforced samples with and without transverse members. The resistance bearing provided by geo-synthetic transverse members was almost 10% of total shear strength. The results indicated that encapsulated geo-grid and geo-textile sand system increased the bearing capacity of clay, with geo-grid being more efficient than geo-textile.
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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) | 0.000 | 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) | 0.002 | 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".