Influence of using a creep, rate, or an elastoplastic model for predicting the behaviour of embankments on soft soils
Bibliographic record
Abstract
The predictability of the behaviour of an embankment constructed on a soft soil with three types of fully coupled finite element analysis models; namely a rate-formulated elasto-viscoplastic, a creep-formulated elasto-viscoplastic, and modified Cam clay (MCC) elastoplastic material model for the foundation soil is examined in this paper. The well documented geotextile reinforced Sackville test embankment was chosen for analyses using the three finite element models. Details of the analyses carried out using the three models and the results are discussed in comparison with field performance. All three models were found to be capable of predicting the behaviour of this embankment reasonably well. The creep model gave slightly better overall predictions of the behaviour compared to the rate and MCC models and therefore is considered to be better for predicting the time-dependent behaviour of this embankment. However, it requires the coefficient of secondary compression of the foundation soft soil as an additional input parameter and consumes more computing resources and time. In contrast, this study suggests that the MCC model is also capable of giving reasonably good overall predictions using less computing resources and time and therefore is sufficient for predicting the performance of embankments on soft soils.Key words: embankment, soft soil, geosynthetic reinforcement, analysis, viscoplasticity, creep.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".