Upper-bound approach for analysis of cantilever retaining walls
Bibliographic record
Abstract
The proposed method for the analysis of cantilever retaining walls is based on ultimate limit states, but in contrast to other methods, which are recognized worldwide, also considers the condition of vertical force equilibrium, which includes the wall unit weight and the vertical component of the soil–structure interaction. The two-dimensional analytical model with polygonal soil pressure distribution is based on two new characteristics: the parameter α and the passive pressure coefficient at the embedment depth, Kb. The kinematic approach of limit analysis is used to examine the limit equilibrium state of the cantilever retaining wall according to soil properties and other loadings. The failure mechanism, composed of a classical determination of the passive pressure in the embedded part of the wall and a kinematically admissible velocity field at the retained side of the wall, estimates the limiting value of the passive earth pressure at the embedment depth. The advantage of the proposed method is that it enables the design of more slender cantilever retaining walls, at which the comparable level of safety for geotechnical and structural bearing capacity limit states is reached, which is the basic condition for safe design of retaining structures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".