New insights into soil arching behaviour in column-supported embankments
Bibliographic record
Abstract
The observation of failure surfaces within column-supported embankments is critical to understanding how the embankment stresses are transferred towards the column heads. In this study, finite element simulations utilising a strain-softening constitutive model, nonlocal regularisation, and the Arbitrary Lagrangian–Eulerian formulation are used to examine these failure surfaces over various embankment geometries. This methodology offers insights into the nature of the failure mechanism, the development of a plane of equal settlement, and the influence of the subsoil settlement profile. Depending on the embankment geometry, the results indicate a punching failure, inverted general bearing failure, or a localised failure develops. The transition between punching and inverted general bearing failure is found to be closely related to the establishment of a plane of equal settlement within the embankment. The height of the plane of equal settlement and the range of failure mechanisms that develop were largely insensitive to the nature of the subsoil settlement profiles simulated. These findings have implications for the practical design of efficient embankments and the effective design of future experimental studies.
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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".