Bibliographic record
Abstract
The behaviour of reinforced embankments over conventional soft cohesive soil, rate-sensitive soil and peat deposits is reviewed, and recent design and analysis methods are summarized. The findings from both field observations and finite element analyses are presented. Both undrained and partially drained behaviour of reinforced embankments are considered. The use of reinforcement in combination with prefabricated vertical drains is addressed. The effects of both the viscous and inviscous characteristics of reinforcement and foundation soils on embankment behaviour are discussed. It is concluded that the partial consolidation provided by PVDs and the tension mobilized in reinforcement can substantially increase embankment stability. However, creep of geosynthetics can decrease the embankment failure height. The mobilization of reinforcement during and after embankment construction can vary significantly depending on the soil and reinforcement characteristics. Care must be taken in design when a creep-susceptible reinforcement is being used and/or the foundation soil is rate sensitive. Note: This paper is a slightly modified version of the Giroud Lecture presented by R. K. Rowe at the Seventh International Conference on Geosynthetics in Nice, France, in 2002.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".