Reliability of traditional retaining wall design
Bibliographic record
Abstract
Retaining wall design has long been carried out with the aid of either the Rankine or Coulomb theories of earth pressure. To obtain a closed-form solution, these traditional earth pressure theories assume that the soil is uniform. The fact that soils are actually spatially variable leads, however, to two potential problems in design: do sampled soil properties adequately reflect the effective properties of the entire retained soil mass, and does spatial variability of soil properties lead to active earth pressure effects that are significantly different from those predicted using traditional models? This paper combines non-linear finite element analysis with random field simulation to investigate these two questions and assess just how safe current design practice is. The specific case investigated is a two-dimensional frictionless wall retaining a cohesionless drained backfill. The wall is designed against sliding using Rankine's earth pressure theory. The design friction angle and unit weight values are obtained by sampling the simulated random soil field at one location, and these sampled soil properties are then used as the effective soil properties in the Rankine model. Failure is defined as occurring when the Rankine predicted force acting on the retaining wall, modified by an appropriate factor of safety, is less than that computed by the random finite element method employing the actual soil property (random) fields. Using Monte Carlo simulation, the probability of failure of the traditional design approach is assessed as a function of the factor of safety used and the spatial variability of the soil.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".