Numerical Analysis of Geogrids and Recycled Concrete Aggregate for Stabilizing Road Embankments
Bibliographic record
Abstract
The amplification of urban development and the extension of road networks necessitate a comprehensive understanding of various soil improvement techniques for civil engineering applications. Soil enhancement methods, typically trusted and practical, are pivotal in addressing geotechnical engineering challenges. This study focuses on the numerical evaluation of the efficacy of geogrids and recycled concrete aggregate (RCA) in the stabilization of road embankments. Critical soil properties such as water absorption, soil erosion, and settlement susceptibility are significantly improved through these methods, promoting sustainable land use, environmental conservation, and infrastructure durability. Literature reveals that the use of geogrids or waste materials like RCA contributes effectively to soil layer enhancement. In this investigation, road embankment models, with and without the aforementioned improvements, were developed and assessed under vehicular load conditions. The findings demonstrated that the incorporation of geogrids or RCA significantly bolsters the stability of road embankments. A noteworthy reduction in vertical settlement, up to 45%, was achieved when geogrids and RCA were concurrently utilized in the embankment. This suggests that these methods, individually or in combination, could provide a viable solution for enhancing the performance and stability of road infrastructures. Further research is proposed to explore the long-term performance of these enhancement methods under various environmental and load conditions.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".