Determination of Effective Stresses and the Compressibility of Soil Using Different Codes of Practice and Soil Models in Finite Element Codes
Bibliographic record
Abstract
Creep or secondary compression plays an important role in the deformation behavior of soft soils like peat and organic clay. For the determination of long term settlements several methods have been proposed and implemented in codes for the engineering practice. However, some of the methods used have been developed empirically. In this paper the effective stresses and the compressibility of soils are addressed for some practical cases using the methods mentioned and soil models in finite element codes. The conceptual differences in methods and calculation results are compared. A stress-strain-creep strain rate model is proposed (Plaxis Soft Soil Creep model) in which strain rates are coupled with stresses and strains. The proposed material creep model is based on the modified Cam-clay model, which has been extended with a viscoplastic formulation using a single creep parameter μ*. The compressibility parameters κ*, λ* and μ* used within the model can be determined by standard one-dimensional consolidation tests. Applying the different methods, the calculated settlements show differences in the ratio between the primary and secondary compression as well as the distribution in time of the excess pore pressures. For large settlements the introduction of the natural strain, defined as the strain related to the actual height instead of the initial height of the soil layer, gives a further improvement of the model proposed (a-b-c model).
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".