Numerical Evaluation for Roads Considering the Addition of Geogrids in Karst Geohazards Zones
Bibliographic record
Abstract
Design of road infrastructure in karst terrain is a challenge for any geotechnical condition caused by the weathering of the subsoil. Previous investigations pointed out the efficiency of the roads with geogrids, however there are few studies analyzing road reinforced under karst geohazards. This paper presents a numerical study of the geogrid additions in a typical Mexican road and considering 19 cavities in the subsoil due to failures of the roads in these terrains. The rocks and the soil were simulated by Hoek–Brown and Mohr–Coulomb constitutive models, considering specific characteristics of karstic materials. Hence, it was carried out in different two-dimension finite element models to analyze the geogrid behavior and its benefits. First, the geogrid position was varied inside of the road structure and applying a heavy truck load in its surface and finally, underground cavities were sequentially opened in the numerical model. It was established the best combination of the road-geogrid structure construction and the influence when cavities are developed underground analyzing the stress paths in the medium. From this study, it is found, that when the geogrid layer is embedded between bedrock and subgrade, the failure is mitigated, observing an increase in the factor of safety even with 19 voids presence in the model. Concluding that the geogrid is an adequate solution of reinforcement of roads.
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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 | 0.001 |
| Research integrity | 0.001 | 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".