Effective use of geosynthetics to increase bearing capacity of shallow foundations
Bibliographic record
Abstract
In this research, a reinforcement mechanism for shallow foundations is determined through laboratory model tests and numerical analyses. The numerical analyses are performed with the finite element program FEMtij-2D using the elastoplastic subloading tij model. The frictional behavior between the reinforcement and the ground is simulated using an elastoplastic joint element. Several tests were performed whereby the installation depth, length, roughness, and fixity conditions at the edges of the reinforcement were varied. Results show that the effectiveness of the reinforcement and the bearing capacity of the reinforced ground depend on the position, length, roughness, and fixity condition of the reinforcement. A significant increase in the bearing capacity can be achieved if the geosynthetics are properly placed at an optimum length with the boundary fixed to the ground. The effect of the loading position is also investigated because in reality the load on a foundation does not always act at the center of the foundation. The numerical results accurately describe the experimental results; the simulations accurately account for the mechanical behaviors of both the soil and reinforcement and the frictional behavior between them. Therefore, the simulation technique can be used to predict the bearing capacity of reinforced ground.
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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) | 0.001 | 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".