A novel approach to estimating the bearing capacity stability of geosynthetic reinforced retaining walls constructed on yielding foundations
Bibliographic record
Abstract
Yielding foundation conditions have been shown to adversely affect the stability and behaviour of overlying geosynthetic reinforced soil walls. To avoid serious problems and maintain a cost-effective design, careful consideration must be given to short-term stability. Previous research has shown that lengthening and stiffening the bottom reinforcement layer of the wall can increase the external stability, but the magnitude of this increase is not well understood. To provide insight regarding the potential benefit of lengthening and stiffening the bottom reinforcement layer, a numerical investigation is made of the plastic collapse mechanism due to bearing capacity failure of the foundation deposit for the case of a 6 m high geosynthetic reinforced retaining wall on a 10 m thick soft to firm visco plastic clay stratum. The calculated behaviour of the wall is compared with that from typical and novel design considerations for both a conventional reinforced wall and a wall where the bottom reinforcement layer has been extended and stiffened. A parametric study of the extended bottom reinforcement layer stiffness and interaction is reported, and the influence on the external stability is discussed.Key words: reinforced soil wall, soft yielding foundation, bearing capacity design, numerical analysis.
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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 | 0.000 |
| 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".